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An Integrated Framework for Enhanced Learning Environments: IoT-Driven Smart Classrooms with Multi-Layered Security Protocols and Adaptive Infrastructure

An Integrated Framework for Enhanced Learning Environments: ...

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2024 International Conference on Advances in Computing, Communication, electrical, and Smart Systems, iCACCESS 2024

作者： Shayer, Khandokar Shadman Medul, Mahamudul Hasan Badoruzzaman, Md. Islam Shuvo, Jahidul Rabbu, Mohammad Mahmudul Haque, F.M. American International University-Bangladesh Department of Electrical & Electronic Engineering Dhaka Bangladesh International University of Business Agriculture and Technology Department of Computer Science & Engineering Dhaka Bangladesh

ISBN: (纸本)9798350350289

The learning environment has experienced an evolution in this era of advanced instructional technologies. Smart classrooms are currently equipped with interactive devices and resources to enhance the effectiveness of learning, and this trend has evolved into an innovative classroom management technique. Conventional classes require a substantial workforce and an administrative team. In a conventional classroom setting, the level of security is generally either limited or completely absent. Considering the circumstances mentioned above, research has been conducted as a provisional resolution. Utilizing the Internet of Things, this research aims to develop a smart classroom that effectively handles real-time information and implements techniques for intelligent security and energy saving. The research utilized ESP32-CAM, a fingerprint sensor, a flame sensor, SIM8001, and an Arduino N ano. The prototype transmits live data to the website server. The hardware implementation results have been thoroughly assessed to verify the project's effective completion. © 2024 IEEE.

关键词： Websites

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SecureChainAI: Integrating Blockchain and Artificial Intelligence for Enhanced Security in IoT Environments 5

SecureChainAI: Integrating Blockchain and Artificial Intelli...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Thamaraiselvi, K. Pushpalatha, A. Chidambarathanu, K. Wankhede, Jaishri P. Alagumuthukrishnan, S. Sarveshwaran, Velliangiri New Horizon College of Engineering Department of Information Science and Engineering Bangalore India Karpagam College of Engineering Department of Computer Science and Design Coimbatore India R.M.K. Engineering College Department of Computer Science and Business Systems Kavaraipettai India Cmr Institute of Technology Department of Computer Science and Engineering Hyderabad India Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur India

ISBN: (纸本)9798331504403

Internet of Things (IoT) systems develop with paramount importance for security in view of the increased deployment of IoT devices over critical sectors like healthcare, industry automation, and smart cities. The risk exposure of such systems is extremely high to potent cyber threats, including those that may result in data integrity and privacy issues and operational unreliability. Security in IoT systems has thus emerged through heterogeneous integration with blockchain and artificial intelligence models. This blockchain technology provides a decentralized, immutable ledger for secure data transactions. AI models, especially those designed by anomaly detection and prediction algorithms, can identify and mitigate cyber threats. In this paper, the authors propose the SecureChainAI model that synergistically integrates artificial intelligence algorithms with blockchain to safeguard IoT environments. The two core building blocks that will make this proposed model real are CaSNet and the EchoChain algorithm. The CaSNet model integrates LSTM, RNN, and GRU techniques with novelty detection for proficient attack prediction and detection using their peculiar capabilities in sequential data handling and the identification of novel attack patterns. The EchoChain algorithm puts blockchain technology into the IoT infrastructure to provide security in communication and integrity of the values. This makes the architecture lightweight, shifting computational loads to the collector node, and hence suitable for IoT devices with low computational resources. Evaluations of the performance necessary to detect different classes of cyberattacks using SecureChainAI have improved on the detection accuracy, precision, recall, f1-score, and reduced error rates. © 2024 IEEE.

关键词： Network intrusion

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Optimized Approaches to Malware Detection: A Study of Machine Learning and Deep Learning Techniques 14

Optimized Approaches to Malware Detection: A Study of Machin...

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14th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2025

作者： Fahim, Abrar Dey, Shamik Absur, Md Nurul Siam, Md Kamrul Huque, Md Tahmidul Godhuli, Jafreen Jafor Department of Electrical and Electronic Engineering Islamic University of Technology Dhaka Bangladesh Department of Electrical and Electronic Engineering Ahsanullah University of Science and Technology Dhaka Bangladesh Department of Computer Science City University of New York New York United States Department of Computer Science New York Institute of Technology New YorkNY United States Department of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka Bangladesh

ISBN: (纸本)9798331531935

Digital systems find it challenging to keep up with cybersecurity threats. The daily emergence of more than 560,000 new malware strains poses significant hazards to the digital ecosystem. The traditional malware detection methods fail to operate properly and yield high false positive rates with low accuracy of the protection system. This study explores the ways in which malware can be detected using these machine learning (ML) and deep learning (DL) approaches to address those shortcomings. This study also includes a systematic comparison of the performance of some of the widely used ML models, such as random forest, multi-layer perceptron (MLP), and deep neural network (DNN), for determining the effectiveness of the domain of modern malware threat systems. We use a considerable-sized database from Kaggle, which has undergone optimized feature selection and preprocessing to improve model performance. Our finding suggests that the DNN model outperformed the other traditional models with the highest training accuracy of 99.92% and an almost perfect AUC score. Furthermore, the feature selection and preprocessing can help improve the capabilities of detection. This research makes an important contribution by analyzing the performance of the model on the performance metrics and providing insight into the effectiveness of the advanced detection techniques to build more robust and more reliable cybersecurity solutions against the growing malware threats. © 2025 IEEE.

关键词： Network intrusion

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Pilot Study on Aerial Wireless Superdirectional Sound Sources Utilizing Parametric Array Loudspeakers via Local 5G Networks

Pilot Study on Aerial Wireless Superdirectional Sound Source...

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2023 IEEE International Symposium on Antennas and Propagation, ISAP 2023

作者： Iihoshi, Yohei Okubo, Kan Department of Electrical Engineering and Computer Science Faculty of System Design Tokyo Metropolitan University Hino Tokyo Japan

ISBN: (纸本)9798350341140

This study presents a pilot study on the potential future use of aerial sound sources utilizing parametric array loudspeakers (PALs) in drones via fifth-generation (5G) networks. Integrating superdirectional loudspeakers with high-speed wireless communication is essential for projecting sound from the air to specific locations using drones. Herein, as an experimental approach, we have evaluated the performance of wireless communication, specifically local 5G, for realizing aerial sound sources. The results demonstrated the high performance of the local 5G connection intended for PALs in the air. © 2023 IEEE.

关键词： 5G networks aerial sound source millimeter wave band parametric array loudspeaker wireless communication

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Distance-based mutual congestion feature selection with genetic algorithm for high-dimensional medical datasets

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Neural Computing and Applications 2025年第8期37卷 6217-6232页

作者： Nematzadeh, Hossein Mani, Joseph Nematzadeh, Zahra Akbari, Ebrahim Mohamad, Radziah Modern College of Business and Science Muscat Oman Department of Electrical Engineering and Computer Science Florida Institute of Technology MelbourneFL32901 United States Otolaryngology Department School of Medicine Emory University AtlantaGA30322 United States Department of Computer Engineering Sari Branch Islamic Azad University Sari Iran Faculty of Computing Universiti Teknologi Malaysia Skudai81310 Malaysia

Abstract: Feature selection poses a challenge in high-dimensional datasets, where the number of features exceeds the number of observations, as seen in microarray, gene expression, and medical datasets. There is not a universally optimal feature selection method applicable to any data distribution, and as a result, the literature consistently endeavors to address this issue. One recent approach in feature selection is termed frequency-based feature selection. However, existing methods in this domain tend to overlook feature values, focusing solely on the distribution in the response variable. In response,this paper introduces the Distance-based Mutual Congestion (DMC) as a filter method that considers both the feature values and the distribution of observations in the response variable. DMC sorts the features of datasets, and the top 5% are retained and clustered by KMeans to mitigate multicollinearity. This is achieved by randomly selecting one feature from each cluster. The selected features form the feature space, and the search space for the Genetic Algorithm with Adaptive Rates (GAwAR) will be approximated using this feature space. GAwAR approximates the combination of the top 10 features that maximizes prediction accuracy within a wrapper scheme. To prevent premature convergence, GAwAR adaptively updates the crossover and mutation rates. The hybrid DMC-GAwAR is applicable to binary classification datasets, and experimental results demonstrate its superiority over some recent works. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Feature Selection

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Improved Convolutional Neural Network and Transfer Learning with VGG16 Approach for Image Classification

Improved Convolutional Neural Network and Transfer Learning ...

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2023 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2023

作者： Habibullah Ullah, Md. Oli Kamal, Minhaz Sohag, Md. Sabbir Hasan Rahman, Md. Saifur Bangladesh University of Business and Technology Department of Computer Science and Engineering Dhaka Bangladesh Bangladesh University of Business and Technology Department of Electrical and Electronic Engineering Dhaka Bangladesh

ISBN: (纸本)9798350307771

Transfer learning may boost modeling speed. This research unifies the improved VGG16 model. Skipping VGG16's entirely connected layer and tying it to the layer following it improves CNN's architecture and reduces its processing burden. CNNs are classifying cat and dog photos. With a small dataset, CNN training may take time and resources. Transfer learning uses ImageNet models to handle similar challenges in new contexts. Transfer learning enhances CNN cat-dog classification. The proposed technique selects a suitable pre-trained model, freezes its layers to retain acquired properties, adds layers to learn task-specific features, and tunes it using the cat and dog dataset. Data augmentation improves model performance and prevents overfitting. Hyperparameter optimization and validation enhance model accuracy and speed. This method enhances cat and dog photo classification even without adequate data to train CNNs with an accuracy of 97%. © 2023 IEEE.

关键词： Convolutional neural networks

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Light Shift Detection Using a Multiple Photodetection Method Suitable for a Chip-Scale Atomic Clock

Light Shift Detection Using a Multiple Photodetection Method...

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2023 Joint Conference of the European Frequency and Time Forum and IEEE International Frequency Control Symposium, EFTF/IFCS 2023

作者： Fukuoka, Masahiro Goka, Shigeyoshi Tokyo Metropolitan University Graduate School of System Design Department of Electrical Engineering and Computer Science Tokyo Japan

ISBN: (纸本)9798350311426

In this study, we demonstrated light shift detection in a running atomic clock using the multi-photodetection method. This method enables the observation of atomic resonances with different intensities using the intensity distribution of the laser beam. As it requires only two photodiodes to detect light shifts, it is easily applicable to miniature vapor-cell-based atomic clocks such as chip-scale atomic clocks and miniature atomic clocks. © 2023 IEEE.

关键词： chip-scale atomic clock coherent population trapping intensity distribution laser light shift VCSEL

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STaaS: Cost Effective and Scalable Union Mounting in ICP for Aerospace Applications

STaaS: Cost Effective and Scalable Union Mounting in ICP for...

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2024 International Conference on Green Energy, Computing and Sustainable Technology, GECOST 2024

作者： Sakthivel, V. Li, Jueying Rama Abirami, K. Min, Dugki Lee, Jae Woo Choi, Eunmi School of Computer Science and Engineering Vellore Institute of Technology Chennai India Konkuk University Department of Computer Science and Engineering Seoul Korea Republic of Curtin University Department of Electrical and Computer Engineering Malaysia Konkuk Aerospace Design - Airworthiness Institute Konkuk University Seoul Korea Republic of Kookmin University Department of Computer Science and Engineering Seoul Korea Republic of

ISBN: (纸本)9798350357905

Cloud computing widely used in various specific areas leads to the emergence of 'Short-Time as a Service' (STaaS) as a cost-effective and scalable way for enterprises and organizations to access and utilize computing resources. Union mounting, a method that enables multiple file systems to be combined into one virtual file system, has been proposed as a strategy to optimize STaaS by allowing resources to be allocated dynamically based on user requirements. This research paper explores the feasibility and benefits of STaaS with the union mounting method, using a project providing a simulation environment on the Industry Cloud Platform (ICP) for the aerospace organization that requires an environment that can automatically and quickly create cluster groups of Cloud In the Loop Simulation (CILS) for Unmanned Aerial Vehicle (UAV) operations. The results demonstrate that STaaS with a union mounting method can provide significant benefits in terms of CILS cluster group creation, performance optimization, and flexibility. Specifically, our proposed research could reduce instance creation time costs by up to 90% compared to the traditional creation method. Overall, STaaS with union mounting method improves the creation time of the CILS cluster group for the aerospace organization requiring flexibility and cost-effectiveness. © 2024 IEEE.

关键词： Antennas

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Monitoring of Individual Household electrical Appliances and Prediction of Energy Consumption Using Machine Learning 5

Monitoring of Individual Household Electrical Appliances and...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Selvam, N. Seenivasan, D. Naveen, G. Sudahar, R. Janagan, P. Dhanushmathi, S. M.Kumarasamy College of Engineering Department of Electrical and Electronics Engineering Karur India M.Kumarasamy College of Engineering Department of Computer Science and Business Systems Karur India

ISBN: (纸本)9798350385649

The study suggests a scalable method for estimating residential power usage. To increase the forecasting accuracy, an SVM (support vector machine) based technique combines many machine learning models, such as random forests, decision trees, and linear regression. Real-world data from Dutch families were used in the study, and the findings demonstrate that the suggested strategy performs better than conventional single-model techniques and attains higher accuracy in predicting energy use. The method is suited for usage in real-world applications since it is scalable and applies to huge datasets. An approach for machine learning used in load forecasting is called SVM (Support Vector Machine). Applications for long-term individual household forecasting include calculating advance payments to customers. However, there is a dearth of research on this type of forecasting;most techniques either concentrate on long-term predictions at the aggregate level or on individual family-level short-term projections. In order to close this distance, we provide a technique that uses only a few months' worth of current year's consumption data to anticipate each monthly consumption throughout the next year. Even with little data, utility suppliers may use this technique to predict any customer's consumption for the upcoming year. Data power use in the future may be predicted using linear regression. The linear regression process is used to forecast data in the future. © 2024 IEEE.

关键词： Forecasting

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Automated CKD Detection via Image Processing Employing Delta-E Metric and CNN

Automated CKD Detection via Image Processing Employing Delta...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Espin Shalo, S.P. Jeeshan, R.J. Gunasekar, S. Pius Agbulu, G. Venkat Ramana, N. Rajalakshmi Engineering College Department of Computer Science and Engineering Tamilnadu Chennai602105 India Rajalakshmi Engineering College Department of Computer Science and Design Tamilnadu Chennai602105 India Chinhoyi University of Technology Department of Mechatronics Engineering Zimbabwe Indian Institute of Technology Department of Electrical Engineering Tamilnadu Chennai India

ISBN: (纸本)9798331510022

Chronic Kidney Disease (CKD) is a global health concern that often progresses silently until advanced stages, making early detection critical. This paper presents a point-of-care system that automates the color comparison between a test strip and a reference strip to detect biomarkers like albumin associated with kidney dysfunction. The system leverages mobile-captured images, applying image processing techniques such as segmentation and color extraction, and compares the test strip's top color with reference colors on the parent strip using the Delta-E metric in the LAB color space. The closest match quantifies the biomarker concentration, providing a reliable diagnostic measure. To enhance diagnostic precision, this research incorporates Convolutional Neural Networks (CNNs) to refine image preprocessing and anomaly detection. The CNN model complements existing methods by mitigating environmental influences like lighting variability and improving feature extraction accuracy. This hybrid approach ensures consistency while adapting to diverse datasets and image conditions. The result is a cost-effective, fast, and accessible diagnostic tool that reduces manual errors and supports early CKD detection in resource-limited settings. Future work will focus on expanding the application of this system to detect other diseases and further optimize the integration of CNNs for enhanced scalability and robustness. © 2024 IEEE.

关键词： Convolutional neural networks

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