IEEE Open Journal of the Computer Society最新文献

{"title":"Artificial Intelligence of Behavior for Human Emotion Recognition in Closed Environments","authors":"Gonzalo-Alberto Alvarez-Garcia;Claudia Zúñiga-Cañón;Antonio-Javier Garcia-Sanchez;Joan Garcia-Haro;Milton Sarria-Paja;Rafael Asorey-Cacheda","doi":"10.1109/OJCS.2024.3463173","DOIUrl":"10.1109/OJCS.2024.3463173","url":null,"abstract":"Understanding human emotions and behavior in closed environments is essential for creating more empathetic and humane spaces. Environmental factors, such as temperature, noise, and light, play a crucial role in influencing behavior, but individuals' emotional states are equally important and often go unnoticed. Artificial Intelligence of Behavior (AIoB) offers a novel approach that integrates environmental measurements with human emotions to create spatially adaptive processes that can influence behavior. In this article, we present a new human emotion sensor developed using video cameras and implemented on a System on Chip (SoC) development board. Our approach uses Convolutional Neural Networks (CNNs) to recognize the presence of emotions in enclosed spaces and generate parameters that can influence emotional states and behavior within an AIoB system. The research successfully integrates advanced CNN technology into a System on Chip (SoC) platform, allowing for real-time processing of video data. The versatility of utilizing an energy-efficient SoC extends its application to smart environments aimed at improving mental health. By employing algorithms capable of detecting emotional states across various individuals, the study enhances its effectiveness. Additionally, it identifies the best CNN operations tailored to the technical specifications of the devices involved. Thus, The development involves a three-step process: (i) collecting enough data to build a robust model, (ii) training the model and evaluating its performance using test values, and (iii) applying the model on the development board. Our study demonstrates the feasibility of using AIoB to recognize and respond to human emotions in closed areas. By integrating emotional cues with environmental measurements, our system can create more personalized and empathetic spaces that cater to the needs of individuals. Our approach could have significant implications for designing public spaces to promote well-being and emotional satisfaction.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"578-588"},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10683879","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142254389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Medicine's New Rhythm: Harnessing Acoustic Sensing via the Internet of Audio Things for Healthcare","authors":"Farrukh Pervez;Moazzam Shoukat;Varsha Suresh;Muhammad Umar Bin Farooq;Moid Sandhu;Adnan Qayyum;Muhammad Usama;Anna Girardi;Siddique Latif;Junaid Qadir","doi":"10.1109/OJCS.2024.3462812","DOIUrl":"10.1109/OJCS.2024.3462812","url":null,"abstract":"In the modern landscape of information and communication technologies, the current healthcare industry confronts significant challenges. These include a shortage of experienced medical professionals, disparities in access to healthcare services that persist across different regions around the globe, and an increased need for detailed, real-time monitoring of patients in both urban and remote regions. This article delves into the potential of the Internet of Audio Things for Healthcare (IoAuT4H), which lies at the intersection of Internet of Audio Things and the Internet of Medical Things, as a solution to these pressing issues. By seamlessly merging cutting-edge audio technology, networking, and the advanced deep learning techniques, the IoAuT4H emerges as a promising solution. It has the potential to reshape hospital and clinical infrastructure, streamline early medical interventions, and facilitate rapid emergency responses. Additionally, this study underscores the pivotal role of the IoAuT4H in strengthening overall health practices, refining care methods for the elderly, and rejuvenating paediatric health approaches. While the benefits of the IoAuT4H are numerous, this article also critically examines the challenges in its widespread adoption. These include ethical considerations, ensuring the accuracy of audio data, and integrating it effectively with existing healthcare systems. In conclusion, this article seeks to provide a comprehensive understanding of the IoAuT4H, positioning it as a bridge between current healthcare challenges and technological advancements.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"491-510"},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10683979","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142254390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Taxonomy-Based Survey of EM-SCA and Implications for Multi-Robot Systems","authors":"Yomna Mokhtar Ibrahim;Shabnam Kasra Kermanshahi;Kathryn Kasmarik;Jiankun Hu","doi":"10.1109/OJCS.2024.3461808","DOIUrl":"10.1109/OJCS.2024.3461808","url":null,"abstract":"Electromagnetic Side Channel Analysis (EM-SCA) is a major area of interest within the field of cybersecurity. EM-SCA makes use of the electromagnetic radiation that naturally leaks from any device that runs on electricity. Information about the observed device can be gained by gathering and analysing these electromagnetic traces. Numerous studies have demonstrated the applicability of this side channel in various environments for legal and illegal objectives. On the other hand, multi-robot systems, including swarm robotics, have received considerable attention in recent years due to their ability to conduct complex tasks using simple robots cooperating with each other. Although multi-robot and swarm robot systems are likely to be widely used in practical applications in the near future, security concerns in this context have not yet received enough attention. In particular, to the best of our knowledge, EM-SCA threats and benefits have never been thoroughly examined in this context before. In order to spotlight this matter, this work begins with a thorough introduction to EM-SCA and provides a taxonomic structure. Then, guided by this taxonomy, we present a range of EM-SCA scenarios that need to be considered in multi-robot applications.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"511-529"},"PeriodicalIF":0.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10681247","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142254391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Utilizing Deep improved ResNet50 for Brain Tumor Classification Based MRI","authors":"Karrar Neamah;Farhan Mohamed;Safa Riyadh Waheed;Waleed Hadi Madhloom Kurdi;Adil Yaseen Taha;Karrar Abdulameer Kadhim","doi":"10.1109/OJCS.2024.3453924","DOIUrl":"10.1109/OJCS.2024.3453924","url":null,"abstract":"A robust approach for brain tumor classification is being developed using deep convolutional neural networks (CNNs). This study leverages an open-source dataset derived from the MRI Brats2015 brain tumor dataset. Preprocessing included intensity normalization, contrast enhancement, and downsizing. Data augmentation techniques were also applied, encompassing rotations and flipping. The core of our proposed approach lies in the utilization of a modified ResNet-50 architecture for feature extraction. This model integrates transfer learning by replacing the final layer with a spatial pyramid pooling layer, enabling it to leverage pre-trained parameters from ImageNet. Transfer learning from ImageNet aids in countering overfitting. Our model's performance was evaluated with various hyperparameters, including existing methods in terms of accuracy, precision, recall, F1-score, sensitivity, and specificity. This study showcases the potential of deep learning, transfer learning, and spatial pyramid pooling in MRI-based brain tumor classification, providing an effective tool for medical image analysis. Our methodology employs a modified ResNet-50 architecture with transfer learning, integrating a spatial pyramid pooling layer for feature extraction. Systematic evaluation showcases the model's superiority over existing methods, demonstrating remarkable results in accuracy (0.9902), precision (0.9837), recall (0.9915), F1-score (0.9891), sensitivity, and specificity. The comparative analysis against prominent CNN architectures reaffirms its outstanding performance. Our model not only mitigates overfitting challenges but also offers a promising tool for medical image analysis, underlining the combined efficacy of spatial pyramid pooling and transfer learning. The study's optimization parameters, including 25 epochs, a learning rate of 1e-4, and a balanced batch size, contribute to its robustness and real-world applicability, furthering advancements in efficient brain tumor classification within MRI data.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"446-456"},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10670206","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142208847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Grasshopper Optimization Algorithm and Neural Network Classifier for Detection and Classification of Barley Leaf Diseases","authors":"O. Dorgham;G. Abu-Shareah;O. Alzubi;J. Al Shaqsi;S. Aburass;M. A. Al-Betar","doi":"10.1109/OJCS.2024.3457160","DOIUrl":"10.1109/OJCS.2024.3457160","url":null,"abstract":"The prevalence of plant diseases presents a substantial challenge to global agriculture, significantly impacting both production levels and economic stability in numerous countries. This study focuses on the early detection of two prevalent diseases affecting barley leaves: net blotch and spot blotch. We introduce a novel model designed for the accurate detection and classification of these diseases. The model employs advanced pre-processing techniques, including the transformation of images into the CIELAB color space and the segmentation of affected areas, to enhance disease identification accuracy. Key shape properties characterizing the diseased regions are extracted and analyzed to distinguish between the two diseases. A critical component of our approach is the feature selection phase, aimed at identifying the most pertinent and informative features, thereby minimizing classification errors and maximizing model accuracy with a minimal set of shape properties. To optimize this process, we have incorporated the Grasshopper Optimization Algorithm, which effectively identifies the optimal shape properties for feature selection. The final classification is executed using a Back Propagation Neural Network Classifier. The efficacy of our model was tested using images of barley afflicted with the specified diseases. The results were compelling, with the model achieving a remarkable accuracy rate, largely attributable to the integration of the grasshopper optimization algorithm in the feature selection stage.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"530-541"},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10670315","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142208848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stitch-Able Split Learning Assisted Multi-UAV Systems","authors":"Tingkai Sun;Xiaoyan Wang;Xiucai Ye;Biao Han","doi":"10.1109/OJCS.2024.3447773","DOIUrl":"https://doi.org/10.1109/OJCS.2024.3447773","url":null,"abstract":"Unmanned aerial vehicles (UAVs), commonly known as drones, have gained widespread popularity due to their ease of deployment and high agility in various applications. In scenarios such as search missions and target tracking, conducting complex and computation-intensive tasks in multi-UAV systems have become essential. Recent investigations have explored the integration of collaborative centralized learning (CL) and federated learning (FL) into multi-UAV systems. However, CL methods raise privacy concerns and may suffer from communication delays, while FL methods demand high UAV-side computation capability. To address these challenges, split learning (SL) emerges as a promising alternative, offering reduced learning iteration time and improved accuracy in resource-constrained edge clients. In this study, we leverage SL and Stitch-able Neural Network (SN-NET) to propose a novel Stitch-able Split Learning (SSL) approach for multi-UAV systems. The proposed SSL approach is capable of tackling challenges in terms of device instability and model heterogeneity that associated in multi-UAV systems. Comparative simulations are conducted, evaluating its performance against CL, FL, traditional SL and SFLV1 (SplitFed Learning V1) approaches to establish its superiority.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"418-429"},"PeriodicalIF":0.0,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10643654","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142152077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Publish Subscribe System Security Requirement: A Case Study for V2V Communication","authors":"Hemant Gupta;Amiya Nayak","doi":"10.1109/OJCS.2024.3442921","DOIUrl":"https://doi.org/10.1109/OJCS.2024.3442921","url":null,"abstract":"The Internet of Things (IoT) enables the linkage between the physical and digital domains, with wireless sensor networks (WSNs) playing a vital role in this procedure. The market is saturated with an abundance of IoT devices, a substantial proportion of which are designed for consumer use and have restricted power and memory capabilities. Our analysis found that there is very little research done on defining the security requirements of the IoT ecosystem. A crucial first step in the design process of a secure product entails meticulously scrutinizing and recording the precise security requirements. This paper focuses on defining security requirements for Vehicle-to-Vehicle (V2V) communication systems. The requirements are specified utilizing the Message Queuing Telemetry Transport for Sensor Network (MQTT-SN) communication protocol architecture, specifically tailored for use in sensor networks. The modified Security Requirement Engineering Process (SREP) and Security Quality Requirement Engineering (SQUARE) methodologies have been used in this paper for the case study. The security of the communication between the ClientApp and the road-side infrastructure is our main priority.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"389-405"},"PeriodicalIF":0.0,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10636299","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142084516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"InFER++: Real-World Indian Facial Expression Dataset","authors":"Syed Sameen Ahmad Rizvi;Aryan Seth;Jagat Sesh Challa;Pratik Narang","doi":"10.1109/OJCS.2024.3443511","DOIUrl":"https://doi.org/10.1109/OJCS.2024.3443511","url":null,"abstract":"Detecting facial expressions is a challenging task in the field of computer vision. Several datasets and algorithms have been proposed over the past two decades; however, deploying them in real-world, in-the-wild scenarios hampers the overall performance. This is because the training data does not completely represent socio-cultural and ethnic diversity; the majority of the datasets consist of American and Caucasian populations. On the contrary, in a diverse and heterogeneous population distribution like the Indian subcontinent, the need for a significantly large enough dataset representing all the ethnic groups is even more critical. To address this, we present InFER++, an India-specific, multi-ethnic, real-world, in-the-wild facial expression dataset consisting of seven basic expressions. To the best of our knowledge, this is the largest India-specific facial expression dataset. Our cross-dataset analysis of RAF-DB vs InFER++ shows that models trained on RAF-DB were not generalizable to ethnic datasets like InFER++. This is because the facial expressions change with respect to ethnic and socio-cultural factors. We also present LiteXpressionNet, a lightweight deep facial expression network that outperforms many existing lightweight models with considerably fewer FLOPs and parameters. The proposed model is inspired by MobileViTv2 architecture, which utilizes GhostNetv2 blocks to increase parametrization while reducing latency and FLOP requirements. The model is trained with a novel objective function that combines early learning regularization and symmetric cross-entropy loss to mitigate human uncertainties and annotation bias in most real-world facial expression datasets.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"406-417"},"PeriodicalIF":0.0,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10636346","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142121603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Musical Genre Classification Using Advanced Audio Analysis and Deep Learning Techniques","authors":"Mumtahina Ahmed;Uland Rozario;Md Mohshin Kabir;Zeyar Aung;Jungpil Shin;M. F. Mridha","doi":"10.1109/OJCS.2024.3431229","DOIUrl":"10.1109/OJCS.2024.3431229","url":null,"abstract":"Classifying music genres has been a significant problem in the decade of seamless music streaming platforms and countless content creations. An accurate music genre classification is a fundamental task with applications in music recommendation, content organization, and understanding musical trends. This study presents a comprehensive approach to music genre classification using deep learning and advanced audio analysis techniques. In this study, a deep learning method was used to tackle the task of music genre classification. For this study, the GTZAN dataset was chosen for music genre classification. This study examines the challenge of music genre categorization using Convolutional Neural Networks (CNN), Feedforward Neural Networks (FNN), Support Vector Machine (SVM), k-nearest Neighbors (kNN), and Long Short-term Memory (LSTM) models on the dataset. This study precisely cross-validates the model's output following feature extraction from pre-processed audio data and then evaluates its performance. The modified CNN model performs better than conventional NN models by using its capacity to capture complex spectrogram patterns. These results highlight how deep learning algorithms may improve systems for categorizing music genres, with implications for various music-related applications and user interfaces. Up to this point, 92.7% of the GTZAN dataset's correctness has been achieved on the GTZAN dataset and 91.6% on the ISMIR2004 Ballroom dataset.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"457-467"},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10605044","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141741079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DeepCon: Unleashing the Power of Divide and Conquer Deep Learning for Colorectal Cancer Classification","authors":"Suhaib Chughtai;Zakaria Senousy;Ahmed Mahany;Nouh Sabri Elmitwally;Khalid N. Ismail;Mohamed Medhat Gaber;Mohammed M. Abdelsamea","doi":"10.1109/OJCS.2024.3428970","DOIUrl":"10.1109/OJCS.2024.3428970","url":null,"abstract":"Colorectal cancer (CRC) is the second leading cause of cancer-related mortality. Precise diagnosis of CRC plays a crucial role in increasing patient survival rates and formulating effective treatment strategies. Deep learning algorithms have demonstrated remarkable proficiency in the precise categorization of histopathology images. In this article, we introduce a novel deep learning model, termed \u0000<italic>DeepCon</i>\u0000 which incorporates the divide-and-conquer principle into the classification task. \u0000<italic>DeepCon</i>\u0000 has been methodically conceived to scrutinize the influence of acquired composition on the learning process, with a specific application to the classification of histology images related to CRC. Our model harnesses pre-trained networks to extract features from both the source and target domains, employing a two-stage transfer learning approach encompassing multiple loss functions. Our transfer learning strategy exploits a learned composition of decomposed images to enhance the transferability of extracted features. The efficacy of the proposed model was assessed using a clinically valid dataset of 5000 CRC images. The experimental results reveal that \u0000<italic>DeepCon</i>\u0000 when coupled with the Xception network as the backbone model and subjected to extensive fine-tuning, achieved a remarkable accuracy rate of 98.4% and an F1 score of 98.4%.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"380-388"},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10599835","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141720314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}