List papers
| Seq | # | Title | Abstract | Keywords | DOIX | Authors with affiliation and country |
|---|---|---|---|---|---|---|
| 1 | 1570964473 | Real-Time Car Parking Detection with Deep Learning in Different Lighting Scenarios | This paper presents an intelligent parking system utilizing image processing and deep learning to address parking challenges amidst varying lighting conditions. The escalating number of vehicles on the road increased the difficulty and time spent in finding available parking spaces, resulting in more cars congestion in the modern cities. To alleviate this issue, we propose an efficient real-time camera-based system that is capable of detection of open parking slots using deep learning methodologies. Initially, we introduce a simple parking detection technique utilizing image processing. Nevertheless, it proves ineffective in dim lighting. Subsequently, we introduce our AI-powered system, trained on the "COCO" dataset using the object detection deep learning YOLO algorithm. The used dataset has been applied with a large-scale collection of images annotated with object categories, bounding boxes, segmentation masks, and captions. It is shown that this solution accurately identifies available and occupied parking slots by detecting vehicles within the parking area. We proposed strategically positioned webcam that provides comprehensive coverage of the parking area, to be set as an initial image serving as a reference for identifying all parking slots. During operation, the webcam records real-time video footage of the parking area, enabling continuous updates for an accurate count of free and occupied parking slots. The paper details the step-by-step implementation of the system and showcases achieved results under diverse lighting conditions. In conclusion, this research demonstrates the system's effectiveness in mitigating parking challenges through the amalgamation of image processing, deep learning, and real-time video analysis. Additionally, we highlight the future potential for research to further enhance and advance this innovative system. | Smart Parking; Deep Learning; Image signal Processing; Real Time Video; lighting Environment | Mohab A. Mangoud (UoB, Bahrain); Fatema Hasan Yusuf (University of Bahrain, Bahrain) | |
| 2 | 1570990891 | NomadicBTS-2: A Network-in-a-Box with Software-Defined Radio and Web-Based App for Multiband Cellular Communication | The proliferation of mobile communications technologies has significantly contributed to the plausibility of emerging economies. However, there still exists a digital divide in several remote and hard-to-reach places, owing to the high CAPital EXpenditure (CAPEX) and OPerating EXpenditure (OPEX) of mobile network operators. In this study, a cost-effective software-defined base station named NomadicBTS-2 is developed and prototyped based on open-source technologies and the Software-Defined Radio (SDR) paradigm. NomadicBTS-2 comprises Universal Software Radio Peripheral (USRP) B200 as the Radio Frequency (RF) hardware front-end. The software backend comprises of open-source software such as USRP Hardware Driver (UHD) and services (i.e., OpenBTS, Asterisk, SIPAuthserve and SMQueue). In addition, we developed a new software (named NomadicBTS WebApp) to configure and monitor the UHD and software services through a web-based Graphical User Interface (GUI). NomadicBTS-2 was tested using two mobile stations (MSs) for simplex and duplex communication while the network link quality parameters were evaluated to determine users' Quality of Experience (QoE). Experimentation results showed that within a pico-cell, the link quality is sufficient for call routing and Short Messaging Services (SMSs) between user-to-user and network-to-user. The prototype provides a basis for a Network-in-a-Box that can be deployed for short-range communication in rural areas, hard-to-reach places, emergency situations, IoT sensor networks and to augment existing base stations to mitigate network congestion. It can also be a viable testbed in teaching and research laboratories to explore new frontiers in SDR, cognitive radio, and other wireless communication domains. | NomadicBTS-2; Software-Defined Radio; Multiband Cellular Communication; Network-in-a-Box | Emmanuel Adetiba (Covenant University, Ota, Nigeria); Petrina C. Uzoatuegwu and Ayodele Ifijeh (Covenant University, Nigeria); Abayomi Abdultaofeek (Mangosuthu University of Technology, South Africa); Obiseye O Obiyemi (Durban University of Technology, South Africa); Emmanuel O. Owolabi (Smartyou Integral PTY LTD, South Africa); Katleho Moloi and Surendra Thakur (Durban University of Technology, South Africa); Sibusiso Moyo (Stellenbosch University, South Africa) | |
| 3 | 1570995462 | Leaf Disease Identification through Transfer Learning: Unveiling the Potential of a Deep Neural Network Model | Grape is one of the world's most crucial and widely consumed crops. The yield of grapes varies depending on the method of fertilisation. Hence, some other factors also impact crop production and quality. One of the major elements affecting crop quality output is leaf disease. Therefore, it is necessary to diagnose and classify diseases earlier. Grape production is affected by a variety of diseases. It could reduce the disease's impact on grapevines if the disease were identified earlier, which would result in higher crop output. There has been a lot of experimentation with new approaches to diagnosing and classifying diseases. This endeavour aims to assist farmers in accurately analysing and informing themselves about illnesses in their early stages. The Convolutional Neural Network (CNN) is a powerful tool for defining and classifying the diseases of grapes. A dataset of 3297 photos of grape leaves affected by four distinct diseases and a healthy leaf was used to conduct the entire experiment using python and orange software. Here's a rundown of the whole procedure: Before the actual segmentation of the images begins, input photos are first pre-processed. The images are then subjected to the second processing round using several CNN hyper-parameters. Finally, CNN analyses images for details such as colour, texture, and edges, among other things. According to the results, the proposed model's predictions are 99.3% correct. | Deep Neural Network; Transfer Learning Leaf Diseases; Classification; VGG; SquezeNet | Naresh Kumar Trivedi (Chitkara University, India); Deden Witarsyah (Nusa Putra University, Indonesia); Raj Gaurang Tiwari and Vinay Gautam (Chitkara University, India); Alok Misra (Lovely Professional University, India) | |
| 4 | 1570999256 | Leveraging ALBERT for Sentiment Classification of Long-Form ChatGPT Reviews on Twitter | Sentiment analysis of content created by users on social media sites reveals important information on public attitudes toward upcoming technologies. Researchers have challenges understanding these impressions, ranging from cursory evaluations to in-depth analyses. Focusing on detailed, long-form reviews exacerbates the difficulty in achieving accurate sentiment analysis. This research addresses the challenge of accurately analyzing sentiments in lengthy and unstructured social media texts, specifically focusing on ChatGPT reviews on Twitter. The study introduces advanced natural language processing (NLP) methodologies, including Fine-Tuning, Easy Data Augmentation (EDA), and Back Translation, to enhance the accuracy of sentiment analysis in lengthy and unstructured social media texts. The primary objective is to evaluate the effectiveness of the ALBERT transformer-based language model, in sentiment classification. Results demonstrate that ALBERT, when augmented with EDA and Back Translation, achieves significant performance improvements, with 81% and 80.1% accuracy, respectively. This research contributes to sentiment analysis by showcasing the efficacy of the ALBERT model, especially when combined with data augmentation techniques like EDA and Back Translation. The findings highlight the model's capability to accurately gauge public sentiments towards ChatGPT in the complex landscape of lengthy and nuanced social media content. This advancement has implications for understanding public attitudes towards emerging technologies, with potential applications in various domains. | Sentiment Analysis; ALBERT; Natural Language Processing; ChatGPT; Long-Form Review | Wanda Safira and Benedictus Prabaswara (Bina Nusantara University, Indonesia); Andrea Stevens Karnyoto (Binus University & BDSRC, Indonesia); Bens Pardamean (Bina Nusantara University, Indonesia) | |
| 5 | 1570999297 | Interconnected Stocks Examination for Predicting the Next Day's High on the Indonesian Stock Exchange | We observed in many WhatsApp/Telegram Indonesian stock market groups, but we didn't find any stock prediction method that utilizes interconnectivity between stocks. In this paper, we examined the interconnected stock dynamics in the IDX and used it to predict the next day's high. We employed a novel method called "Connected Stocks + Rolling Window Method" which uses both the temporal dynamics of the stock market and the interconnectedness of IDX's stocks. We explored the characteristics of the interconnected stocks by implementing three machine learning algorithms - K-nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest (RF) - and found valuable insight. The experiment showed that several factors including a balanced threshold model and increased stock input size helped the performance of a model, while several factors including window size, additional features added, and using specific sectors as training data did not help the model's performance. The result also showed that several stocks like ANTM and ERAA show signs of interconnectedness and are influenceable while some like KLBF are hard to influence and show no sign of interconnectedness based on their results. This research contributes to a deeper understanding of stock market dynamics on the IDX, especially the characteristics of interconnected stocks on the IDX. | Stock prediction; machine learning; support vector machine; random forest; indonesian stock market | Andreas Werner Sihotang (Bina Nusantara University, Indonesia); Andrea Stevens Karnyoto (Binus University & BDSRC, Indonesia); Bens Pardamean (Bina Nusantara University, Indonesia) | |
| 6 | 1570999725 | Development of LoRa Multipoint Network Integrated with MQTT-SN Protocol for Microclimate Data Logging in UB Forest | The UB Forest, located on the slopes of Mount Arjuno, is a significant educational and research area with rich agricultural lands and diverse plant species. Traditional methods of microclimate data collection in this area have relied on manual sensor inspection by local farmers. This study introduces a novel approach by integrating Internet of Things (IoT) technology, particularly employing Long Range (LoRa) communication, to overcome the limitations of conventional WiFi networks in remote data access. The implementation uses ESP32 modules for data transmission and reception, focusing on establishing a LoRa network compatible with the Message Queuing Telemetry Transport for Sensor Networks (MQTT-SN) protocol. This enhances data exchange efficiency and reliability. The system is engineered to transmit 11 distinct microclimate data parameters bi-minutely from two nodes. Preliminary testing reveals a maximum transmission range of 300 meters. However, the data loss rate is significant, averaging 50%, which reduces to 15% at a distance of 100 meters. Signal strength is strongest at -94 dBm for 100 meters and -121 dBm for 300 meters. These results, while promising, fall short of the LoRa Alliance's expected performance metrics, which suggest effective operational distances of up to 2km under optimal conditions. This research demonstrates the potential and challenges of integrating IoT and LoRa technology in agricultural and environmental monitoring. The findings underscore the need for further optimization to achieve the range and reliability required for effective remote monitoring in rural and forested environments. This study sets a foundation for future enhancements in sensor network design and deployment strategies, aiming to improve data accuracy and accessibility for agricultural and environmental research. | Internet of Things (IoT); LoRa; MQTT-SN; ESP32; Microclimate data | Heru Nurwarsito and Mohammad Ali Syaugi Alkaf (University of Brawijaya, Indonesia) | |
| 7 | 1570999881 | A Protected Data Transfer through Audio Signals by Quantization combined with Blowfish Encryption: The Genetic Algorithm Approach | Information is actually very potent. It is very common for data to be transferred over the internet, and everyone is responsible for ensuring its security. Data loss, data manipulation, and theft of confidential information are all effects of security events. Information security is a set of practices and protocols that help to secure this information. Such sensitive data can be secured using a variety of methods. Information security includes two important subfields: cryptography and steganography. With the help of cryptography and steganography, information is altered into an unintelligible state and made secret respectively. The purpose of this chapter is to preserve impenetrability and improve invulnerability. The objectives of this chapter are to be recognized by enhancement of Blowfish which is believed as highly secure algorithm; the implementation of chaotic sequence-quantization method for audio samples. The proposed work's performance is contrasted with that of the existing blowfish method and standard audio LSB algorithm. The following criteria shows the demonstration of analysis of the work done - Entropy values, Avalanche effect, Attack scenario, Execution time, PSNR value, Embedding capacity, Structural similarity index etc. The suggested system is the most effective method for intensifying protection and preserving the high caliber of the original entity. | Symmetric Encryption; Quantization; Blowfish; Avalanche Effect; Entropy Value; Attack Scenario | Rashmi P Shirole (Visvesvaraya Technological University, Belgaum & NMAMIT, Nitte, India); Shivamurthy G (VTURC, India) | |
| 8 | 1571000835 | Strengthening Android Malware Detection: from Machine Learning to Deep Learning | In the recent era of the modern world, Android malware continues to escalate, and the challenges associated with its usage are growing at an unprecedented rate. This cause rapid growth in Android malware infections points to an alarming and swift rise in their prevalence, signaling a cause for concern. Traditional anti-malware systems, reliant on signature-based detection, prove inadequate in addressing the expanding scope of newly developed malware. Various strategies have been introduced to counter the escalating threat in the Android mobile field, with many leaning towards machine learning (ML) models limited by a constrained set of features. This paper introduces a novel approach employing a deep learning (DL) framework, incorporating a significant number of diverse features. The proposed framework uses Deep Neural Network (DNN) techniques on a static OmniDroid dataset, comprising 25,999 features extracted from 22,000 Android Package Kits (APKs). Of these, 16,380 features are meticulously selected for analysis, encompassing Permission, Opcodes, Application Programming Interface(API) calls, System Commands, Activities, and Services. Additionally, the data is partitioned feature-wise and subjected to feature selection on each feature set to ensure equitable consideration of all features. A comparative analysis is presented by comparing the framework accuracy with the accuracies produced by the existing ML models. The presented framework demonstrates notable enhancements in detection accuracy, achieving 89.04\% accuracy, attributed to the incorporation of a substantial number of features. | Android malware; malware detection; deep learning; artificial neural network; feature selection; machine learning | Diptimayee Sahu, Satya Narayan Tripathy and Sisira Kumar Kapat (Berhampur University, India) | |
| 9 | 1571006545 | AI-based Intelligent Window System for Hospitals in GCC Countries | Climate change stands as a formidable challenge on a global scale, manifesting through alterations in weather patterns and regional ecosystems, with the Gulf region, in particular, facing pronounced shifts due to its distinct climate and high vulnerability to such changes. These environmental shifts have far-reaching effects, not least on the operational dynamics and internal conditions of hospitals establishments pivotal to the delivery of critical healthcare services. Recognizing the urgent need to address these climatic impacts within healthcare settings, this paper introduces a cutting-edge solution: an artificial intelligence (AI)-powered intelligent window system specifically designed to enhance the hospital environment by mitigating the adverse effects of climate change. These smart windows are engineered to process and react to real-time weather data alongside a variety of relevant environmental inputs, enabling them to dynamically modify their functional properties ranging from filtering capabilities to ventilation mechanisms. This adaptive functionality aims to maintain or improve the indoor environmental quality, ensuring that it remains conducive to patient care and staff well-being. Beyond mere environmental control, the system is innovatively tailored to integrate patient-specific health information and preferences, allowing for the customization of key environmental parameters such as lighting levels, ambient temperature, and air quality. This level of personalization is intended to foster an atmosphere that not only promotes healing and comfort but also significantly enhances the patient experience by supporting the overall recovery process. Through this comprehensive approach, our proposed intelligent window system aspires to bridge the gap between technological innovation and healthcare service enhancement, offering a proactive response to the challenges posed by climate change in the healthcare sector. | Intelligent Windows; Air Quality; rtificial Intelligence; Hospital; Sensors | Ahmed Jedidi (Ahlia University, Saudi Arabia) | |
| 10 | 1571010950 | Early Autism Spectrum Disorder Screening in Toddlers: A Comprehensive Stacked Machine Learning Approach | In this paper, we have introduced a study that addresses the critical need for early detection of Autism Spectrum Disorder (ASD) in toddlers. ASD is characterized within the context of its profound impact on early childhood development, emphasizing the urgency of identifying it as early as possible. To achieve this, the study employs a diverse set of base models, including Logistic Regression, KNN, Decision Trees (DT), Support Vector Machines (SVM), and Neural Networks (NN), among others, as part of its methodology. One key aspect of the methodology is the meticulous execution of feature selection using these models. The focus is on identifying the top four features that are most indicative of ASD for subsequent training. By leveraging various machine learning algorithms, the study aims to develop accurate predictive models for early ASD detection. The results of the study are promising, with the models achieving high levels of accuracy. The models with the highest accuracy are identified, and a stacking technique is systematically applied, combining the strengths of different classifiers to further enhance performance. The most significant finding of the study is the exceptional accuracy rate of 99.148% achieved by the proposed approach. This high accuracy rate underscores the efficacy of the methodology in early ASD detection. By accurately identifying ASD in toddlers at an early stage, the study demonstrates the potential for timely intervention and support for affected children, ultimately improving their long-term outcomes and quality of life. | Machine learning; Preference algorithm; Stacking; Feature selection; Classification; Confusion Matrix | Anupam Das and Prasant Kumar Pattanaik (Kalinga Institute of Industrial Technology, India); Suchetan Mukherjee and Sapthak Mohajon Turjya (KIIT, India); Anjan Bandyopadhyay (Kalinga Institute of Industrial Technology, India) | |
| 11 | 1571012545 | Verbal Question and Answer System for Early Childhood Using Dense Neural Network Method | Questions are a well-known topic in Natural Language Processing (NLP). This feature is very suitable for use in learning activities in kindergarten to help train social interaction. The problem in this research is that the developed system must be able to understand questions from childhood. This is complex, given that their questions often need to be spoken correctly due to their limited ability to formulate questions appropriately. Therefore, this research proposes the Dense Neural Network (DNN) method, which can handle questions with non-linear word order using an Indonesian corpus of 5000 questions and answers. Experimental results show that the proposed DNN approach is superior to the Long Short Term Memory (LSTM) method in understanding and answering questions from young children, especially those that need to be more structured and formulated but have a clear context. DNN also achieved the highest accuracy in the training process, which was 0.9356. In contrast, the LSTM method showed a lower accuracy of only 0.8824. In a test of 2000 questions with different question patterns, the best accuracy was obtained by the DNN method at 93.1\%. The results of this study make an essential contribution to the development of NLP systems that can be used in the context of early childhood learning. | Dense Neural Network (DNN); Long Short Term Memory (LSTM); Natural Language Processing (NLP); Question and answer | La Ode Fefli, Yarlin (Universitas Hasanuddin, Indonesia); Zahir Zainuddin and Ingrid Nurtanio (Hasanuddin University, Indonesia) | |
| 12 | 1571013400 | A New Semantic Search Approach for the Holy Quran Based On Discourse Analysis and Advanced Word Representation Models | Semantic search is an information retrieval technique that seeks to understand the contextual meaning of words to find more accurate results. It remains an open challenge, especially for the Holy Quran, as this sacred book encodes crucial religious meanings with a high level of semantics and eloquence beyond human capacities. This paper presents a new semantic search approach for the Holy Quran. The presented approach leverages the power of contextualized word representation models and discourse analysis to retrieve semantically relevant verses to the user's query, which do not necessarily appear verbatim in Quranic text. It consists of three crucial modules. The first module concerns the discourse segmentation of Quranic text into discourse units. The second module aims to identify the most effective word representation model for mapping the Quranic discourse units into semantic vectors. To this end, the performance of five cutting-edge word representation models in assessing semantic relatedness in the Holy Quran at verse level is investigated. The third module concerns the semantic search model. Evaluation results of the proposed approach are very promising. The average precision and recall are 90.79% and 79.57%, respectively, which demonstrates the strength of the proposed approach and the ability of contextualized word representation models to capture Quran semantic information. | Information retrieval; Natural Language Processing; contextualized word representation models; discourse analysis; semantic relatedness; Holy Quran | Samira Lagrini (University of Annaba, Algeria); Amina Debbah (University of Badji Mokhtar, Algeria) | |
| 13 | 1571015706 | Big Data and Predictive Analytics for Strategic Human Resource Management: A Systematic Literature Review | In the digital transformation era, businesses generate vast amounts of data from various internal and external sources. This data explosion has not only led to the emergence of big data (BD) and predictive analytics (PA) but also revolutionized the way we approach strategic human resource management (SHRM). With the exponential growth of organizational data in volume, velocity, and diversity, there is a notable opportunity to investigate BD and PA methods that provide executives with future-oriented insights into talent dynamics. This study presents a concise overview of the main themes and patterns using a systematic literature review (SLR). Several studies have been conducted on adopting BD and PA techniques, yielding excellent results and offering valuable insights for strategic human resource management (SHRM) experts and future researchers. The search was restricted to articles written in English and published between 2016 and 2023. After conducting an initial search, approximately 50 articles were identified and screened for relevance using a set of inclusion and exclusion criteria. In the final sample, 21 articles published between 2016 and 2023 met the inclusion criteria. The SLR summary describes the essential findings and limitations. The SLR also evaluated the status of existing research on the topic and identified areas for future research. | big data; performance analytics; systematic literature review; strategic human resource management. | http://dx.doi.org/10.12785/ijcds/1601114 | Minwir Al-Shammari, Fatema Al bin ali, Mariam AlRashidi and Muneera Albuainin (University of Bahrain, Bahrain) |
| 14 | 1571016089 | Advanced Heterogeneous Ensemble Voting Mechanism with GRFOA-based Feature Selection for Emotion Recognition from EEG Signal Analysis | Important features of electroencephalogram (EEG) that underlie emotional brain processes include high temporal resolution and asymmetric spatial activations. Unlike voice signals or facial expressions, which are easily duplicated, EEG-based emotion documentation has shown to be a reliable option. Because people react emotionally differently to the same stimulus, EEG signals of emotion are not universal and can vary greatly from one individual to the next. As a consequence, EEG signals are highly reliant on the individual and have shown promising results in subject-dependent emotion identification. The research suggests using ensemble learning with an advanced voting mechanism to understand the spatial asymmetry and temporal dynamics of EEG for accurate and generalizable emotion identification. Using VMD (Variational-Mode-Decomposition) and EMD (Empirical mode decomposition), two feature extraction techniques, on the pre-processed EEG data. When selecting features, the Garra Rufa Fish optimization algorithm (GRFOA) is employed. The ensemble model includes a Temporal Convolutional Network (TCNN), an Extreme Learning Machine (ELM), and a Multi-Layer Perception Network (MLP). The proposed method involves utilizing EEG data from individual subjects for training classifiers, enabling the identification of emotions. The result is then derived via a voting classifier that is based on heterogeneous ensemble learning. Two publicly obtainable datasets, DEAP and MAHNOB-HCI, are used to validate the proposed approach using broader cross-validation settings. | Empirical Mode decomposition; Electroencephalogram; Garra Rufa Fish optimization algorithm; Emotion analysis | Rajanikanth Aluvalu (Symbiosis International University, India); Asha V (New Horizon College of Engineering, India); Anandhi Jayadharmarajan (Visvesharaya Technological University & The Oxford College of Engineering, India); MVV Prasad Kantipudi (Symbiosis International Deemed University, India); Mousumi Bhanja (Symbiosis Institute of Technology Pune, India); Jyoti Bali (MIT Vishwaprayag University, India) | |
| 15 | 1571016257 | Adaptive Exercise Meticulousness in Pose Detection and Monitoring via Machine Learning | This machine learning-based fitness monitoring system revolutionizes the industry through advanced computer vision and pose recognition technologies. Sophisticated algorithms including Move-Net and dense neural networks identify body poses during exercises with high accuracy. It analyses joint angles to provide precise form feedback beyond sole identification. An interactive voice assistant translates poses into contextual exercise instructions, repetition counting, and personalized coaching delivered audibly. Modules for exercise recognition, environmental adaptation, and customization accommodate diverse workouts, conditions, and preferences. Cloud-based training with GPU acceleration drives continual evolution. By integrating detected poses with voice-assisted commands, it creates a dynamic, engaging workout experience. This represents a pioneering fusion of machine learning and computer vision establishing new frontiers for intelligent fitness technologies. With its machine learning engine, this state-of-the-art fitness tracking system has the potential to completely transform the fitness sector. Through the utilization of sophisticated computer vision and position recognition techniques, it surpasses traditional fitness tracking approaches. Sophisticated algorithms like Move-Net and deep neural networks, which continuously and accurately evaluate body positions during exercises, are at the heart of it. This innovative combination of computer vision and machine learning is, in short, a quantum leap rather than merely a step ahead. It's changing our perspective on exercise and opening up new avenues for intelligent fitness technologies, which will lead to a healthier and more empowered future. | Dense Neural Network; Dynamic Pose Monitoring; Exercise; Fitness; Machine Learning | N. Palanivel, G. Naveen and C. Sunilprasanna (India); S. Nimalan (Manakula Vinayagar Institute of Technology, India) | |
| 16 | 1571016609 | A Hybrid Approach to Enhancing Personal Sensitive Information Protection in the Context of Cloud Storage | The growing use of cloud computing and increasing popularity of digital technologies have made it essential to store and process personal data in cloud environments. As organizations and individuals continue to adopt cloud services, the security of sensitive personal information in this dynamic environment has become a top priority. Ensuring confidentiality, integrity, and availability of personal data in the cloud is critical for mitigating the risks associated with cyber threats. This study examines security issues related to personal information in cloud systems and proposes a new approach that leverages machine learning (ML) classification and data tokenization techniques using serverless and secret vault services provided by cloud service providers (CSPs). Supervised learning algorithms, including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and Multilayer Perceptron (MLP), are used for data label prediction. Notably, we found that the CNN achieved remarkable 100% accuracy on a large dataset, ensuring perfect classification with double validation using pattern matching. Additionally, natural language processing (NLP) techniques are employed to clean and prepare data content, whereas data tokenization is used to ensure data confidentiality and integrity. Furthermore, an analysis of both model overhead and cloud performance revealed that our model is scalable, and that data handling using our approach has no significant impact on time costs. This study also provides an overview of cloud computing, its service models, and the main security threats inherent in the cloud infrastructure. The experimental design and results based on specific datasets validated the effectiveness of the proposed hybrid approach in enhancing the protection of sensitive personal information in cloud storage. | Personally Identifiable Information; Data Security; Cloud Storage; Data Masking; Data Leakage; Cloud Computing | Mohammed El Moudni and Houssaine Ziyati (C3S Lab, Higher School of Technology, UH2C, Morocco) | |
| 17 | 1571016631 | Biosignals Based Smoking Status Prediction Using Standard Autoencoder and Artificial Neural Network | Smoking is still a major global health concern since it causes a host of illnesses and early deaths around the globe. Utilizing biosignals to predict smoking status can yield insightful information for tailored interventions and smoking cessation programs. This work presents a novel method that combines an artificial neural network (ANN) and a regular autoencoder to predict smoking status based on biosignals. The proposed method involves preprocessing biosignal data to extract relevant features, which are then input into an autoencoder for dimensionality reduction. The output of an autoencoder is used as input for predicting smoking status using an ANN. The model is trained and evaluated using a dataset containing biosignal data from individuals with a known smoking status. The suggested strategy is effective, as seen by the experimental results, which show a high degree of prediction accuracy about smoking status. The model's performance is further validated through comparisons with existing methods, showing superior performance in terms of accuracy and robustness. The developed model is integrated into a user-friendly application aimed at promoting smoking cessation. In addition to specific online pages aimed at enlightening users about the negative consequences of smoking and the advantages of stopping, the program offers users individualized insights into their smoking status based on biosignals. Additionally, a menu-based chatbot is included to address user queries and provide support for smoking cessation efforts. The implemented deep learning model achieves the desired level of accuracy in predicting smoker status, and the user-friendly application offers a convenient platform for public health and personalized healthcare interventions. | Biosignals; Smoking status; Autoencoder; Artificial Neural Network | N. Palanivel (India); S. Deivanai, G. Lakshmi Priya and B. Sindhuja (Manakula Vinayagar Institute of Technology, India) | |
| 18 | 1571017038 | Efficient Neuro-Fuzzy based Relay Selection in IoT-enabled SDWSN | The Internet of Things is made up of wireless sensor devices (nodes) that work together to create a dynamic network without central management or continuous assistance. High mobility sensor nodes cause periodic topological changes in the network and link failures, which frequently force nodes to rediscover new routes for efficient data transmission in IoT, this brings attention to the issue of energy management and improvement in network lifetime. The relay selection is one method to reduce the node energy in the IoT network. However, designing communication protocols for relay selection, especially for dynamic networks, is a big challenge for researchers. To overcome these challenges, Software Defined Networking (SDN) architecture is used to minimize the overhead of sensor nodes by managing the topology control and routing decisions through artificial intelligent algorithms. The fuzzy logic and neural networks are combined to solve more complex problems such as decision-making, and optimization. This paper presents an Energy-aware Relay Selection Technique using an adaptive Neuro fuzzy-based model (ERST) to optimize the overall energy usage and improve the span of the network, a relay node is selected depending on remaining energy, signal strength, and expected transmission ratio. The proposed ERST uses a fuzzy logic inference system to make intelligent decisions based on the fuzzy rules. The neural network can be trained to fine-tune the fuzzy system using the feedback concepts to select the optimal relay node. In addition, the simulation results prove that the suggested work outperforms the previous protocols in terms of an 8% improvement in packet delivery ratio, reduces 5% of end-to-end delay, 4% minimization of energy usage, and an 8% increase in average throughput and overall network lifetime. | Internet of Things; Relay Selection; Energy Efficiency; Fuzzy-Logic; Neural networks | Poornima M R (UVCE, Bengaluru, India); VImala H S (Professor, India); Shreyas J (Manipal Institute of Technology, India) | |
| 19 | 1571017778 | Design and Development of Novel AXI Interconnect based NoC Architecture for SoC with Reduced Latency and Improved Throughput | A novel AXI interconnect-based Network-on-Chip (NoC) architecture is presented in this research. The purpose of the architecture is to make System-on-Chip (SoC) designs more efficient by reducing latency and improving throughput. Because of its high performance and bandwidth capabilities, the Advanced eXtensible Interface (AXI), which is a component of the Advanced Microcontroller Bus Architecture (AMBA) of the ARM architecture, is used. This configuration makes it possible to communicate effectively inside the chip. The proposed architecture overcomes the scalability limits that are inherent in conventional bus systems. This is accomplished by integrating AXI with NoC principles, which enables more efficient data transmission over a greater number of linked modules. By introducing an effective routing system and a network interface that has been improved, This research work enables packet transfer to occur without interruption. A 2x2 mesh topology is used to simulate the proposed architecture, and an XY routing algorithm is included into the simulation in order to guarantee that deadlock and livelock-free operations are carried out. This highlights the potential of the proposed architecture in high-performance computing applications that require rapid data exchange and minimal response times. The simulation results demonstrate significant improvements over traditional interconnect approaches, yielding a lower latency of 0.99 microseconds and a higher throughput of 4.363 flits per cycle which demonstrates the potential of the proposed architecture | AXI Interconnect; NoC; SoC; Router; Mesh Topology | M Nagarjuna (Vardhaman College of Engineering, India); Girish V Attimarad (K S School of Engineering and Management Bangalore, India) | |
| 20 | 1571018142 | Machine Learning based Material Demand Prediction of Construction Equipment for Maintenance | Construction managers faced the Construction Equipment (CE) challenges related to running repair and replacement of spare part materials as well as shortage of materials, sudden damage of spare parts and unavailability of necessary materials at job sites frequently. Regular follow up and track of materials availability and their usage at each stage of requirement phase becomes essential. This study presents Machine Learning (ML) based material demand prediction. Training of ML models utilizes historical maintenance, and procurement periodic data related to materials of the CE. This study highlights the use of Multiple Linear Regression (MLR), Support Vector Regression (SVR), Decision Tree (DT) Regressor and ensemble boosting models as Random Forest (RF) Regressor and Gradient Bosting Regressor (GBR). According to the performance measurement of each model, RF performs better and is used for prediction. Material demand prediction helps in maintenance and operational planning of CE. Subsequently, approach assists in addressing issues early by involving operators and site owners, enabling preventive actions to be taken before the scheduled procurement process. This study addresses the corrective measurement of the model using periodic data. The model performance results indicate that early prediction of maintenance costs based on the quantity of essential materials withdrawn from demand is helpful for budgeting expenditures. | Construction Equipment; Machine Learning; Material Demand; Maintenance | Poonam Prashant Katyare and Shubhalaxmi S. Joshi (Vishwanath Karad MIT World Peace University, India) | |
| 21 | 1571020029 | A Comparative Analysis and Review of Techniques for African Facial Image Processing | Facial recognition algorithms power various applications, demanding representative and diverse datasets. However, developing reliable models for African populations is hindered by the scarcity of African facial image databases. This study addresses this gap by analyzing the state and potential of African facial image collections. The methodology involves collecting and analyzing indigenous African datasets and evaluating factors like temporal relevance, geographic coverage, and demographic representation. We evaluate the quality and diversity of existing datasets, and the ethical and cultural issues of data collection. We also apply machine-learning techniques, namely Principal Component Analysis (PCA) and Support Vector Machines (SVM), to analyze and classify facial features of three African ethnic groups. The study shows that PCA can capture facial variations, and SVM can achieve 55% accuracy, with group differences. Findings highlight the potential of machine learning for inclusive facial recognition but also reveal challenges, including data imbalance and limitations in chosen features. To achieve fair and reliable facial recognition, future directions advocate for a culturally sensitive approach and highlight the importance of representative dataset systems found in Africa. Also, a concentration should be on collecting data from underrepresented regions and ethnic groups. The collection of diverse and culturally sensitive datasets can be facilitated by collaborative activities between researchers and local communities. | Facial Image Processing; Bias; Facial Image Datasets; Machine Learning; Classification and Clustering; Digital Signal Processing | Amarachi Udefi (Obafemi Awolowo University Ile Ife, Osun State & Grundtvig Polytechnic Oba, Anambra State, Nigeria); Segun Aina (Obafemi Awolowo University Ile-Ife, Osun State, Nigeria); Aderonke Lawal (Obafemi Awolowo University Ile Ife, Osun State, Nigeria); Niran Oluwaranti (Obafemi Awolowo University, Ile-Ife, Nigeria) | |
| 22 | 1571020041 | Detecting Cyber Threats in IoT Networks: A Machine Learning Approach | Internet of Things (IOT) network security challenges in cybersecurity are among the key demands that are oriented towards the safety of data distribution and storage. Prior to the present research, the loopholes that have been found in the field of tackling this danger were the greatest, especially in real-world IoT setups. Hereby, in this study, we create room for the previously unfilled gap using our innovative method to detect network cybersecurity in IoT networks. The technique is based on merging machine learning and neural network algorithms that are trained on vast IoT historical datasets. Several diverse methods, particularly gradient boosting, convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and recurrent neural networks (RNNs), are used to detect and categorize network traffic aspects that potentially suggest cyber risks. The evaluation of each algorithm's performance is based on conventional metrics, which are, for instance, accuracy, precision, recall, and F1-score. Through rigorous testing, we do illustrate the applicability of our technique, in which our solution recognized and curbed the cyber threat in IoT networks, offering the most accurate results of 93% using gradient boosting. Our discussed work can be taken as confirmation of the current advancement of machine learning and deep learning techniques in the scope of increasing cybersecurity in IoT environments. And furthermore, our examined facts may serve as the starting point of future refined investigations in this regard. | Internet of Things; Cybersecurity; Machine learning; Network security | Atheer Alaa Hammad, Sr (Ministry of Education & Directorate of Education, Iraq); May Adnan Falih (Southern Technical University, Iraq); Senan Ali Abd (University of Anbar, Iraq); Saadaldeen Rashid Ahmed (Artificial Intelligence Engineering, Iraq) | |
| 23 | 1571020530 | Modified YOLOv5-based License Plate Detection: an Efficient Approach for Automatic Vehicle Identification | Indonesia witnesses a continual annual surge in the vehicle count, with the Central Statistics Agency (BPS) projecting a total of 148.2 million vehicles in 2022, marking a 6.3 million increase from the preceding year. This growth underscores the escalating challenges associated with traffic management and violations. Hence, the development of a robust vehicle number plate image recognition system becomes paramount for effective traffic control, accurate parking records, and streamlined identification of vehicle owners. In this study, the YOLO v5 algorithm is employed in conjunction with the AOLP dataset, encompassing diverse vehicle images under challenging conditions, such as low lighting, intricate viewing angles, and blurred license plates. The YOLO v5 algorithm exhibits noteworthy performance metrics, boasting a recall value of 99.7%, precision reaching 99.1%, mAP50 of 99.4%, and mAP50-95 of 84.8%. The elevated precision signifies the model's proficiency in minimizing identification errors, while the commendable recall highlights its adeptness in locating existing number plates accurately. Concurrently, the Optical Character Recognition (OCR) model, dedicated to character recognition on number plates, attains an accuracy level of 92.85%, underscoring its efficacy in deciphering alphanumeric characters. This integrated approach leverages advanced algorithms to tackle the intricacies of realworld scenarios, affirming its viability for enhancing traffic management systems and bolstering the efficiency of vehicle-related processes. | YOLO; OCR; Object detection; Plate number; Character recognition | Rifqi Alfinnur Charisma and Suharjito Suharjito (Bina Nusantara University, Indonesia) | |
| 24 | 1571024157 | New Ensemble Model for Diagnosing Retinal Diseases from Optical Coherence Tomography Images | The vision depends greatly on the retina, unfortunately, it may be exposed to many diseases that lead to poor vision or blindness. This research aims to diagnose retinal diseases through OCT images, focusing on Drusen, diabetic macular edema (DME), and choroidal neovascularization (CNV). A new ensemble model is proposed that proposes new methods and combines them with soft and hard voting methods, it is based on three sub-models (Custom-model, Xception, and MobileNet). Because we noticed that some sub-models are better than others at classifying a particular category, each sub-model was assigned to the category it classifies best. We also used a way to correct final misclassification through a list of negative predictions created to contain categories to which the sub-model is somewhat certain that an image does not belong. The proposed ensemble model achieved a state-of-the-art accuracy of 100%, and the Custom model obtained an accuracy of 99.79% on the UCSD-v2 dataset. The Duke dataset was also employed to verify the performance efficiency of the model, with the ensemble model also achieving an accuracy of 100%, and the Custom model recording an accuracy of 99.69%. In the first dataset, the custom model specializes in Drusen and Normal, Xception in DME, and MobileNet in CNV. While the custom model in AMD, Xception in DME, and MobileNet in Normal in the second dataset. The results of this research emphasize the effectiveness of ensemble learning techniques in analyzing medical images, especially in diagnosing retinal diseases. | Ensemble Learning; Deep Learning; OCT Images; Retinal Diseases; Drusen; DME | Shibly Hameed Al-Amiry (University of Al-Qadisiyah, Iraq); Ali Mohsin Al-juboori (Al-Qadisiyah University, Iraq) | |
| 25 | 1571024236 | Convolutional Neural Network with Extreme Learning for the Classification of Plant Leaf Diseases from Images | Contemporary era has assumed significance of Artificial Intelligence along with Deep Learning due to its ability to analyze data and discover latent trends or patterns that were not known earlier. The foundation of the entire world is agriculture and particularly India is highly dependent on it. Farmers are facing many difficulties right from the selection of seed to fertilizer usage, disease control, harvesting and selling the agricultural yield. Technological innovations should be used to facilitate farmers to achieve highest yield possible with minimal expenditure. The prime motivation behind this research stems from the idea that, the ability to detect leaf issues and implement corrective measures can offer a solution to mitigate the decrease in crop productivity. The existing Deep Learning methods like Convolutional Neural Network showed high efficiency Regarding the modification and use of acquired knowledge. A novel framework has been developed by incorporating Convolutional Neural Network and tuning the hyperparameters. Training has been performed using Extreme Learning process which yielded better results. Convolutional Neural Network - Extreme Learning Algorithm is the underlying algorithm. The suggested model's performance is contrasted with many Deep Learning models. The empirical study makes use of the Plant Village dataset. The leaf disease categories considered in this research early blight, black rot, bacterial spot, apple scab, cercospora leaf spot and healthy. Convolutional Neural Network - Extreme Learning achieved 94.28% precision, 95.63% accuracy, 94.68% recall, and 96.23% F1-score using Plant Village dataset, outperforming other classifiers. The research outcomes reflect that the proposed Deep Learning model and algorithm can be used real world computer vision applications pertaining to agriculture. | Convolutional Neural Networks; Deep Learning; Extreme Learning; Hyperparameters,; Plant Leaf Disease Classification | Swapna Jamal and John Edwin Judith (Noorul Islam Centre for Higher Education, India) | |
| 26 | 1571024502 | Artificial-Intelligent-Enhanced Adaptive Vertical Beamforming Techniques for 5G Networks | The advances of 5G era systems and technology throughout the years suggests new uses for Adaptive beamforming and Digital Signal Processing (DSP) strategies in the communication systems to determine the transformational capacity of 5G wireless technology. This article evaluates the performance metrics of phase shift beamforming in a system of phased Uniform Rectangular Array (URA) aided with Artificial Intelligence (AI) to improve the link and communication quality in dense user urban environments. We use the conventional Quadrature Amplitude Modulation (QAM) for evaluating its robustness through a series of simulations for Bit Error Rate (BER) under different Signal to Noise Ratio (SNR) values. This study describes the opposition of theoretical and empirical BER to confirm the beamforming algorithm's operation in the communication system. We propose a spatial spectrum technique for a clear visualization of the Direction of Arrival (DoA) that gives the details of signal movement of users in the network and array behavior in the base station (BS). So, these results not only confirm the proposed methods effectiveness in the mobile network, but also highlight the importance of a creative AI system embedded with beamforming in achieving the expected performance metrics and reliability for future 5G communication networks and beyond. | A.I.; Beamforming; BER; Throughput; Vertical; 5G | Yousif Maher Alhatim (University of Ninevah, Iraq); Ali Othman Al Janaby (Ninevah University, Iraq & Electronics Engineering, Iraq) | |
| 27 | 1571024840 | Intelligent Approaches for Alzheimer's Disease Diagnosis from EEG Signals: Systematic Review | This systematic review explores the emerging field of Alzheimer's disease (AD) diagnosis using recent advances in machine learning (ML) and deep learning (DL) methods using EEG signals. This review focuses on 38 key articles published between January 2020 and February 2024, critically examining the integration of computational intelligence with neuroimaging to improve diagnostic accuracy and early detection of AD. AD poses significant diagnostic and treatment challenges, which are exacerbated by the aging of the global population. Traditional diagnostic methods, while comprehensive, are often limited by their time-consuming nature, reliance on expert interpretation, and limited accessibility. EEG is emerging as a promising alternative, providing a non-invasive, cost-effective way to record the brain's electrical activity and identify neurophysiological markers indicative of AD. The review highlights the shift towards automated diagnostic processes, where ML and DL techniques play a crucial role in analyzing EEG data, extracting relevant features and classifying AD stages with extremely high accuracy. It describes different methods for preprocessing EEG signals, feature extraction and application of different classifier models and demonstrates the complexity of the field and the nuanced understanding of EEG signals in the context of AD. In summary, although the review demonstrates several advantageous developments, it has highlighted critical challenges and limitations. For example, the AI needs more extensive and more diverse datasets to increase model generalizability and multi-modal data integration to achieve a more comprehensive AD diagnosis. Undoubtedly, its preprocessing techniques and classification techniques must be developed because of the complex nature of EEG data and AD pathology. To conclude, this review portrays EEG-based AD diagnosis as a promising field fueled by computational breakthroughs. Yet the insufficient literature and investigation require additional scientific inquiries and further research. Numerous outlooks highlight co-investigating EEG with complementary biomarkers and investigating innovative ML/DL approaches. | Alzheimer's Disease; EEG; Machine Learning; Deep Learning; AD Diagnosis | Nigar M. Shafiq Surameery (University of Garmian, Iraq); Abdulbasit Kadhim Alazzawi (Diyala & Collage of Science, Turkey); Aras Asaad (University of Buckingham, United Kingdom (Great Britain)) | |
| 28 | 1571026011 | Enhancing Bitcoin Forecast Accuracy by Integrating AI, Sentiment Analysis, and Financial Models | This study explores the application of advanced AI models-Long Short-Term Memory (LSTM), Prophet, and SARIMAX-in predicting Bitcoin prices. It assesses the impact of incorporating sentiment analysis from sources like Twitter and Yahoo, processed through Large Language Models. The research aims to understand how sentiment analysis, reflecting investor sentiments and market perceptions, can enhance the accuracy of these forecasting models. The paper investigates the potential synergies and challenges in improving predictive performance by integrating qualitative sentiment data with quantitative financial models. The analysis compares the models' accuracy with and without sentiment inputs, utilizing historical Bitcoin price data and sentiment indicators. This study's motivation is the growing recognition of investor sentiment's impact on market fluctuations, particularly in the highly speculative and sentiment-driven cryptocurrency markets. While robust in handling quantitative data, many studies claim that traditional financial models often fail to incorporate market sentiments. This paper also contributes to financial forecasting literature by offering insights into the benefits and complexities of combining traditional econometric models with sentiment analysis, providing a unique understanding of market dynamics influenced by investor behavior. The findings suggest that sentiment analysis can significantly refine forecasting accuracy, underscoring the importance of incorporating human sentiment and market perceptions in predictive models. | Bitcoin forecasting; LSTM; sentiment analysis; predictive accuracy | Mohamad El Abaji and Ramzi A. Haraty (Lebanese American University, Lebanon) | |
| 29 | 1571027104 | Synergistic Exploration Combining Traditional And Evolutionary Methods To Improve Supervised Satellite Images Classification | This paper aims to enhance the performance of supervised classification of satellite images by adopting a spectral classification approach, which often encounters the issue of class confusions due to its reliance solely on spectral information. The proposed approach, EAMD (Evolutionary Algorithm and Minimum Distance), integrates a Genetic Algorithm-based evolutionary method with the Minimum Distance method. During the training phase, the Genetic Algorithm generates an optimal set of subcategories to represent different object classes present in the image and identifies an optimal representative set of pixels for class assignment. Experimental tests conducted on various satellite images yield promising results, demonstrating the capability of Genetic Algorithms to enhance classification accuracy and effectively identify and exclude misleading pixels responsible for class confusions. This aspect is crucial, as the effectiveness of supervised classification heavily depends on the quality of the training samples. Validation of the approach was further reinforced by intentionally injecting erroneous data into the training data. Compared to the Minimum Distance method, the proposed approach successfully detects and avoids the erroneous pixels, a task unaccomplished by the Minimum Distance method. The obtained results demonstrate that the hybrid proposed approach offers significant potential for improving the accuracy and reliability of satellite image classification techniques. | Genetic Algorithm,; Minimum Distance; Satellite Images; Supervised Classification | Ismahane Kariche (University of Sciences and Technologie of Oran-Mohamed Boudiaf (USTOMB), Algeria); Hadria Fizazi (University of Science and Technology Mohamed Boudiaf, Algeria) | |
| 30 | 1571031764 | Classification of Road Features Using Transfer Learning and Convolutional Neural Network (CNN) | Efficient and accurate classification of road features, such as crosswalks, intersections, overpasses, and roundabouts, is crucial for enhancing road safety and optimizing traffic management. In this study, we propose a classification approach that utilizes the power of transfer learning and convolutional neural networks (CNNs) to address the road feature classification problem. By leveraging advancements in deep learning and employing state-of-the-art CNN architectures, the proposed system aims to achieve robust and real- time classification of road features. The dataset contained 7616 images of roundabouts, crosswalks, overpasses, and intersections from the MLRSNet dataset and manually extracted satellite images from Malaysia using Google Earth Pro. After that, we merged this dataset. We designed a CNN architecture that consists of 24 convolution layers and eight fully connected layers. Transfer learning models such as ResNet50, MobileNetV2, VGG19 and InceptionV3 were also explored for road feature classification. The best-performing model during the validation phase is InceptionV3, with an accuracy of 98.9777%, whereas the best-performing model during the test phase is ResNet50 and VGG-19 models, with an accuracy of 98.7132%. The proposed CNN model got 95.1208% and 94.4852% accuracy during the validation and test stage. From the evaluation, the best-performing models for road feature classification are ResNet50 and VGG-19, with an accuracy of 98.7132%. | Deep learning; Transfer Learning; Road feature; Classification | Mustafa Majeed Abd Zaid and Ahmed Abed Mohammed (Islamic University, Najaf, Iraq); Putra Sumari (Universiti Sains Malaysia (USM) & School of Computer Science, Malaysia) | |
| 31 | 1571032285 | Machine Learning-Based Real-Time Detection of Apple Leaf Diseases: An Enhanced Pre- processing Perspective | Cedar, rust, spot, frogeye, and healthy leaf are the five general types of apple leaf diseases (ALDs). An early phase diagnosis and precise detection of ALDs can manage the extent of infection and confirm the well growth of the apple production. The previous analysis utilizes difficult digital image processing (DIP) and can't be sure of a high accuracy rate for ALDs. This article introduces a precise detecting method for ALDs based on the deep learning (DL) method. It contains creating efficient PATHOLOGICAL images and proposing a new framework of a DL method to detect ALDs. Utilizing a database of 3,174 images of ALDs, the researched DL model is trained to detect the five general ALDs. This proposed work specifies that the research segmentation, transformation, and feature extraction methods give an enhanced outcome in disease handle for ALDs with maximum performance of detection rate. This article has created an effort to implement an approach that can detect the disease of apple leaves using different pre-processing methods. ALDs framework is designed for filtration, and color space transformation methods using Median, Gaussian, HIS, and HSV models. Grey Level Co-occurrence Matrix (GLCM) is used for the texture-based feature extraction (FE) method and the image creation method implemented in this article can improve the robustness of the improved feature extraction method. | Apple leaf diseases; Deep Learning; Feature Extraction (GLCM) Method; Segmentation; Transformation | Anupam Bonkra (Maharishi Markandeshwar University, India); Priya Jindal (Chitkara Business School, Chitkara University, Punjab, India); Ekkarat Boonchieng (Chiang Mai University, Thailand); Mandeep Kaur (Maharishi Markandeshwar University, India); Naveen Kumar (Chitkara University, India) | |
| 32 | 1571032872 | Blind Image Separation based on Meta-heuristic Optimization Methods and Mutual Information | There are a number of modern disciplines in digital signal processing (DSP) as so-called blind images. The core of this problem is there two images mixed in one image and require separate these images and recovering original images. There are many methods and strategies used to solve this problem. One of these solutions is unsupervised machine learning mechanisms, as in the Independent Component Analysis (ICA), which uses the statistical properties of the latent images. This method essentially is dependent upon the statistical characteristics of an observation signals and the non-Gaussian limitations between the mixed images conditions. For all applications, the ICA needs to enhancing, therefore many optimization methods used for that purpose. The swarm intelligence methods are one of many techniques utilized to enhance the ICA's efficiency. For this purpose, in this paper, three swarm optimization methods used are Quantum Particle Swarm Optimization (QPSO), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC). These methods implemented, on nine gray-scale images with seven nixing cases, separately. The results are been evaluated under three metrics for assessment are Structural Similarity Index Measurement, Peak Signal to Noise Ratio, and Normalized Cross Correlation. The applying of this system gave optimal results under the specified measurements. | Blind Image Separation,; BSS,; ICA,; Cocktail Party problem | Hussein Mohammed Salman (University of Babylon, Iraq); Ali Kadhum M. Al-Qurabat (University of Babylon & College of Science for Women, Iraq); Abd alnasir Riyadh Finjan (Supreme Commission for Hajj and Umrah, Iraq) | |
| 33 | 1571033189 | Collaborative multi-agent deep reinforcement learning approach for enhanced attitude control in quadrotors | Unmanned Aerial Vehicles (UAVs), particularly quadrotors, have become highly versatile platforms for various applications and missions. In this study, the employment of Multi-Agent Reinforcement Learning (MARL) in quadrotor control systems is investigated, expanding its conventional usage beyond multi-UAV path planning and obstacle avoidance tasks. While traditional single-agent control techniques face limitations in effectively managing the coupled dynamics associated with attitude control, especially when exposed to complex scenarios and trajectories, this paper presents a novel method to enhance the adaptability and generalization capabilities of Reinforcement Learning (RL) low-level control agents in quadrotors. We propose a framework consisting of collaborative MARL to control the Roll, Pitch, and Yaw of the quadrotor, aiming to stabilize the system and efficiently track various predefined trajectories. Along with the overall system architecture of the MARL-based attitude control system, we elucidate the training framework, collaborative interactions among agents, neural network structures, and reward functions implemented. While experimental validation is pending, theoretical analyses and simulations illustrate the envisioned benefits of employing MARL for quadrotor control in terms of stability, responsiveness, and adaptability. Central to our approach is the employment of multiple actor-critic algorithms within the proposed control architecture, and through a comparative study, we evaluate the performance of the advocated technique against a single-agent RL controller and established linear and nonlinear methodologies, including Proportional-Integral-Derivative (PID) and Backstepping control, highlighting the advantages of collaborative intelligence in enhancing quadrotor control in complex environments. | Quadrotors; Attitude Control; Multi-Agent Deep Reinforcement Learning; Collaborative Intelligence | Trad Taha Yacine and Choutri Kheireddine (Aeronautical Sciences Laboratory Aeronautical and Spatial Studies Institute, Algeria); Mohand Lagha (Aeronautical Science Laboratory Aeronautical and Spatial Studies Institute, Algeria); Khenfri Fouad (Energy and Embedded Systems for Transport, ESTACA'Lab, Laval, France) | |
| 34 | 1571033487 | An Efficient IoT-based Prediction and Diagnosis of Cardiovascular Diseases for Healthcare Using Machine Learning Models | The Internet of Things (IoT) and Machine Learning (ML) models are emerging technologies that are changing our daily lives. These are also considered as game-changing technologies in recent years, catalyzing a paradigm change in traditional healthcare practices. Cardiovascular disease (CVD) is considered a major reason for the high death rate around the world. Cardiovascular disease is caused due to several risk factors like an unhealthy diet, sugar, high Blood Pressure (BP) smoking, etc. Preventive treatment and early intervention for those at risk depend heavily on the prompt and accurate prediction of illnesses. Developing prediction models with improved accuracy is essential given the increasing use of electronic health records. Recurrent neural network variations of deep learning are capable of handling sequential time-series data. In remote places often lack access to a skilled cardiologist. Our proposal aims to develop an efficient community-based recommender system using IoT technology to detect and classify heart diseases. To address this issue, machine learning techniques are applied to a dataset to predict patients with cardiovascular disease because it's difficult for the medical team to identify CVD effectively. A public dataset is used that contains data of 70000 patients gathered at the time of medical examination and each row has 13 attributes. The risk groups were determined by their likelihood of developing cardiovascular disease. As it works successfully in forecasting diseases utilizing the support system. | Internet of things; Machine Learning; heart disease; Decision Tree; Disease Detection | Hamza Aldabbas (Albalqa Applied University, Jordan); Zaid Mustafa (Al-Balqa Applied University, Jordan) | |
| 35 | 1571038124 | Enhancing Diabetes Prediction Using Ensemble Machine Learning Model | Diabetes is a disease which is beyond cure and which has adverse effects on the health and hence has to be detected at an earlier time to avoid more damage to the body. This study aims at establishing the use of machine learning in the circumstances of diabetes prediction based on factors such as glucose levels, blood pressure, skin fold thickness, and insulin. The purpose of this study is to identify the potential of using machine learning techniques, such as Support Vector Machine (SVM), Logistic Regression and proposed Ensemble Model for the prediction of diabetes. To this aim, in the current study, a dataset including the fundamental medical features of a general population of patients was employed. Regarding this, the data pre-processing was done with the view of handling missing data, data normalization and feature extraction in a view of enhancing the performance of the proposed model. All the models have been developed, and the data was split to perform k-fold cross validation to make the predictions more accurate. From the evaluation metrics, it is evident that the proposed Ensemble Model is the most appropriate since it has a higher accuracy rate compared to the Support Vector Machine, Logistic Regression model. To compare the performance of each model the metrics used includes accuracy, precision, recall, F1-Score. Therefore, the above analysis shows that the proposed Ensemble model is effective in the prediction of diabetes, and this is why there is the need to consider data mining in order to improve the health care delivery systems. | Diabetes Prediction; Machine Learning; Ensemble Learning; Predictive Modeling; Health Informatics; Diabetes Risk Factor | Aniket Kailas Shahade and Priyanka Vinayakrao Deshmukh (Symbiosis Institute of Technology, Pune, India) | |
| 36 | 1571050022 | Instruction-Level Customization and Automatic Generation of Embedded Systems Cores for FPGA | Reducing power consumption and improving performance are crucial requirements for many applications, especially power hungry and time-consuming applications. This is particularly true when these applications are running in power or time-constrained environments like battery-operated embedded systems or on Internet of Things (IoT) devices. A general-purpose processor is not promising for this kind of applications as it cannot provide optimized performance and power consumption for specific applications. That is why domain-specific architectures (DSA) are gaining popularity, as they promise optimized performance for these types of applications in terms of throughput, power consumption, and overall cost. Unfortunately, the use of DSA presents inherent limitations as it requires custom design for each group of applications and cannot offer optimized performance for each specific application. This paper explains how to take advantage of the open standard Instruction Set Architecture (ISA) of the fifth generation of Reduced Instruction Set Computer (RISC-V) to automate the generation of a uni-processor core customized for a certain application such that the processor supports only the very specific instructions needed by this application. The proposed generator is capable of producing the Register Transfer Level (RTL) description of a processor core for any desired application given its source code. This work targets Field Programmable Gate Arrays (FPGAs) due to their re-configurability. When compared with general purpose processors, the conducted experiments show that application specific cores generated by our approach managed to achieve energy and execution time reductions reaching 8% and 5% respectively on some of the used benchmarks. The proposed methodology also offers the added flexibility stemming from the possibility to automatically re-configure the FPGA when a new or upgraded software application that would benefit from modifying the set of supported instructions is deployed. | Embedded Systems; Performance; Power Consumption; Instruction-Level Processor Customization; RISC-V; FPGA | Omar Yehia and Sandra Raafat (Ain Shams University, Cairo, Egypt); M. Watheq El-Kharashi (Ain Shams University, Egypt); Ayman M. Wahba (Ain Shams University & Faculty of Engineering, Egypt); Cherif R. Salama (The American University in Cairo & Ain Shams University, Egypt) |
36 papers.