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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Abarna, P.S. Priya, S. Padma Pavalarajan, S. Sivatharani, S. Manshi, M. Department of Computer Science and Business Systems PSNA College of Engineering and Technology Dindigul India

ISBN: (纸本)9798331522667

The risk of gas leaks has grown significantly as a life threatening issue in industrial activities, cooking, and heating. This system integrates automatic reaction mechanisms, real-time monitoring capabilities, and advanced detection technologies in a multifaceted manner to address various dangers. The comprehensive gas leak detection and automatic cut off system is intended to improve safety in a variety of situations, including industrial and residential ones. To precisely identify the existence of dangerous gases like methane and propane, the proposed system combines infrared, electrochemical, and semiconductor types of gas sensors integrated with a mobile application, users may remotely administer and monitor the system in real-time. The system initiates a multi-tiered response upon detecting a gas leak;it sounds an alarm, notifies user-specified The mobile devices about the leak, and turns on an exhaust fan to release concentrated gas. A solenoid valve is used by the system to quickly shut off the gas supply and electricity from any location. The need of user-friendly controls and prompt notifications in emergency situations is highlighted in this paper's discussion of software architecture, sensor integration, and user interface design. Experimental results show that the system is reliable and capable of responding quickly under different circumstances. In future the application of machine learning algorithms for predictive maintenance and the possibility of integrating IoT to further strengthen security protocols. The results emphasize importance to deploy these kinds of extensive safety systems widely in order to reduce the risks associated with gas exposure. © 2025 IEEE.

关键词： Solenoid valves

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Smart Sanitation Management: Integrating IoT, AI-Driven Analytics, and Automated Sensing for Efficient Public Toilet Maintenance 2

Smart Sanitation Management: Integrating IoT, AI-Driven Anal...

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2nd International Conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Haripriya, K. Preethi, C. Hammed, S.Shahul Pavalarajan, S. Sharon, S. Akhil Suryakumar, S. PSNA College of Engineering and Technology Department of Computer Science and Business Systems Dindigul India

ISBN: (纸本)9798331505745

An IoT-based self-sustained public toilet maintenance system aims to improve hygiene, efficiency, and resource management. It integrates smart sensors, microcontrollers, and cloud connectivity to monitor parameters such as water levels, air quality, and toilet usage in real-time. AI-driven predictive maintenance optimizes cleaning schedules, minimizes downtime, and ensures efficient resource use. Automated water control prevents wastage, while air quality sensors detect harmful gases and activate ventilation. A smart notification system alerts municipal authorities about supply shortages, enabling timely replenishment of water, soap, and tissues. By reducing manual intervention, the system lowers operational costs and improves service quality. Cloud connectivity allows seamless data processing, offering insights for better sanitation management. The integration of IoT and AI ensures proactive maintenance responses. A comparative analysis highlights the novelty of this system in automating and optimizing maintenance. © 2025 IEEE.

关键词： Replenishment (water resources)

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Music Recommendation System 10

Music Recommendation System

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10th International Conference on Advanced Computing and Communication systems, ICACCS 2024

作者： Shyam Prakash, D. Jyothir, B.A. Raja Santhosh, M. Prithivi Raj, P. Mithun Raj, M. Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

The digital music streaming services have changed the way people find and listen to music. Nowadays, with millions of songs to choose from, it can be difficult to choose the music that best suits your taste. Music recommendation systems have become an essential part of these platforms. They are designed to improve user experience by providing personalized music recommendations. In this abstract, we will provide a detailed description of our music recommendation system. The system uses advanced machine learning (ML) and data analysis techniques to provide personalized music recommendations. The system uses collaborative filtering, content-based filtering and hybrid recommendation approaches. In addition, the system takes into consideration contextual factors (e.g., user demographic, listening history, real-time behavior) to refine its suggestions. Our proposed music recommendation system is rigorously evaluated using large datasets and various metrics (e.g. accuracy, diversity, serendipity, etc.). We also consider the ethical implications of our recommendation algorithms to ensure fairness and transparency in our recommendations. © 2024 IEEE.

关键词： Collaborative filtering

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Deep Learning Based Side-Channel Attack Detection for Mobile Devices Security in 5G Networks

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1012-1026页

作者： Amjed A.Ahmed Mohammad Kamrul Hasan Ali Alqahtani Shayla Islam Bishwajeet Pandey Leila Rzayeva Huda Saleh Abbas Azana Hafizah Mohd Aman Nayef Alqahtani Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Imam Alkadhim University College Department of Computer Techniques EngineeringBaghdad 10066Iraq Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Department of Networks and Communications Engineering College of Computer Science and Information SystemsNajran UniversityNajran 61441Saudi Arabia Institute of Computer Science and Digital Innovation UCSI UniversityKuala Lumpur 56000Malaysia Department of Intelligent Systems and Cyber Security Astana IT UniversityAstana 20000Kazakstan Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinah 42353Saudi Arabia Department of Electrical Engineering College of EngineeringKing Faisal UniversityAl-Hofuf 31982Saudi Arabia

Mobile devices within Fifth Generation(5G)networks,typically equipped with Android systems,serve as a bridge to connect digital gadgets such as global positioning system,mobile devices,and wireless routers,which are vital in facilitating end-user communication ***,the security of Android systems has been challenged by the sensitive data involved,leading to vulnerabilities in mobile devices used in 5G *** vulnerabilities expose mobile devices to cyber-attacks,primarily resulting from security ***-permission apps in Android can exploit these channels to access sensitive information,including user identities,login credentials,and geolocation *** such attack leverages"zero-permission"sensors like accelerometers and gyroscopes,enabling attackers to gather information about the smartphone's *** underscores the importance of fortifying mobile devices against potential future *** research focuses on a new recurrent neural network prediction model,which has proved highly effective for detecting side-channel attacks in mobile devices in 5G *** conducted state-of-the-art comparative studies to validate our experimental *** results demonstrate that even a small amount of training data can accurately recognize 37.5％of previously unseen user-typed ***,our tap detection mechanism achieves a 92％accuracy rate,a crucial factor for text *** findings have significant practical implications,as they reinforce mobile device security in 5G networks,enhancing user privacy,and data protection.

关键词： Fifth Generation(5G)networks smartphone information leakage Side-Channel Attack(SCA) deep learning

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Wind Speed Forecasting using Deep Learning

Wind Speed Forecasting using Deep Learning

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2024 International Conference on Smart systems for Electrical, Electronics, Communication and computer Engineering, ICSSEEC 2024

作者： Janakiraman, V. Chitra, P. Thiagarajar College of Engineering Department of Computer Science and Business Systems Madurai India

ISBN: (纸本)9798350378177

Wind energy is a resource that is environmentally sustainable, crucial component needed for wind energy production. Accurate estimation of wind speed is essential for many applications. This paper proposes the Hybrid deep learning model for wind speed forecasting. The hybrid wind speed model was developed based on the Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM). CNN model includes a series of Convolutional layers followed by pooling layers, designed to extract relevant features from the input sequences, final dense layers of the network facilitate the prediction of future wind speeds. LSTM model is used for improving the accuracy of short - term prediction. Using the wind dataset from the National Renewable Energy Laboratory (NREL) in Muppandal, Kanyakumari, Tamil Nadu, wind speed was determined. The performance of the proposed model (CNN-LSTM) is quantified using Mean Absolute Error (MAE), Mean Root Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The experimental results proved that the proposed hybrid model outperformed the benchmarks models being the RNN, CNN, GRU and LSTM. © 2024 IEEE.

关键词： Wind forecasting

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Data Augmentation Impact on Deep learning Performance for Stress Detection 11

Data Augmentation Impact on Deep learning Performance for St...

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11th IEEE International Conference on Intelligent Computing and Information systems, ICICIS 2023

作者： Hamdi, Eman Mabrouk, Mohamed Rady, Sherine Aref, Mostafa Faculty of Computer and Information Science Computer Science Department Cairo Egypt Faculty of Computer and Information Science Information Systems Department Cairo Egypt

ISBN: (纸本)9798350322101

The mental health of individuals has a major influence on society. Mental disorders such as depression and anxiety are related to issues and distress to function in work, social, or family gatherings. Motivated by helping people to early detect stress, we propose a deep learning model for stress detection that is trained and tested on Stress Annotated Dataset (SAD). Also, different techniques of textual data augmentation are applied to generate more data samples to study the impact on the stress detection task. The model achieves competitive results in the task of stress detection and proves that data augmentation has a great effect on the performance. As the accuracy is raised by 6.1% to 10.4 % by applying various techniques of data augmentation. © 2023 IEEE.

关键词： Stresses

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Integrating Transformer and Bidirectional Long Short-Term Memory for Intelligent Breast Cancer Detection from Histopathology Biopsy Images

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computer Modeling in Engineering & sciences 2024年第10期141卷 443-458页

作者： Prasanalakshmi Balaji Omar Alqahtani Sangita Babu Mousmi Ajay Chaurasia Shanmugapriya Prakasam Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha61451Saudi Arabia College of Science and Arts in Rijal Alma King Khalid UniversityAbha61451Saudi Arabia Department of Information Technology Muffakham Jah College of Engineering and TechnologyHyderabad500034India Department of Computer Science and Business Systems Rajalakshmi Engineering CollegeThandalam602105India

Breast cancer is a significant threat to the global population,affecting not only women but also a threat to the entire *** recent advancements in digital pathology,Eosin and hematoxylin images provide enhanced clarity in examiningmicroscopic features of breast tissues based on their staining *** cancer detection facilitates the quickening of the therapeutic process,thereby increasing survival *** analysis made by medical professionals,especially pathologists,is time-consuming and challenging,and there arises a need for automated breast cancer detection *** upcoming artificial intelligence platforms,especially deep learning models,play an important role in image diagnosis and ***,the histopathology biopsy images are taken from standard data ***,the gathered images are given as input to the Multi-Scale Dilated Vision Transformer,where the essential features are ***,the features are subjected to the Bidirectional Long Short-Term Memory(Bi-LSTM)for classifying the breast cancer *** efficacy of the model is evaluated using divergent *** compared with other methods,the proposed work reveals that it offers impressive results for detection.

关键词： Bidirectional long short-term memory breast cancer detection feature extraction histopathology biopsy images multi-scale dilated vision transformer

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Efficient and Secure IoT Based Smart Home Automation Using Multi-Model Learning and Blockchain Technology

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computer Modeling in Engineering & sciences 2024年第6期139卷 3387-3415页

作者： Nazik Alturki Raed Alharthi Muhammad Umer Oumaima Saidani Amal Alshardan Reemah M.Alhebshi Shtwai Alsubai Ali Kashif Bashir Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia Department of Computer Science and Engineering University of Hafr Al-BatinHafar Al-BatinSaudi Arabia Department of Computer Science&Information Technology The Islamia University of BahawalpurBahawalpurPakistan Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Computing and Mathematics Manchester Metropolitan UniversityManchesterUK Woxsen School of Business Woxsen UniversityHyderabadIndia Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon

The concept of smart houses has grown in prominence in recent *** challenges linked to smart homes are identification theft,data safety,automated decision-making for IoT-based devices,and the security of the device *** home automation systems try to address these issues but there is still an urgent need for a dependable and secure smart home solution that includes automatic decision-making systems and methodical *** paper proposes a smart home system based on ensemble learning of random forest(RF)and convolutional neural networks(CNN)for programmed decision-making tasks,such as categorizing gadgets as“OFF”or“ON”based on their normal routine in *** have integrated emerging blockchain technology to provide secure,decentralized,and trustworthy authentication and recognition of IoT *** system consists of a 5V relay circuit,various sensors,and a Raspberry Pi server and database for managing *** have also developed an Android app that communicates with the server interface through an HTTP web interface and an Apache *** feasibility and efficacy of the proposed smart home automation system have been evaluated in both laboratory and real-time *** is essential to use inexpensive,scalable,and readily available components and technologies in smart home automation ***,we must incorporate a comprehensive security and privacy-centric design that emphasizes risk assessments,such as cyberattacks,hardware security,and other cyber *** trial results support the proposed system and demonstrate its potential for use in everyday life.

关键词： Blockchain Internet of Things(IoT) smart home automation cybersecurity

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MR3D-Net: Dynamic Multi-Resolution 3D Sparse Voxel Grid Fusion for LiDAR-Based Collective Perception 27

MR3D-Net: Dynamic Multi-Resolution 3D Sparse Voxel Grid Fusi...

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27th IEEE International Conference on Intelligent Transportation systems, ITSC 2024

作者： Teufel, Sven Gamerdinger, Jorg Volk, Georg Bringmann, Oliver University of Tübingen Faculty of Science Department of Computer Science Embedded Systems Germany

ISBN: (纸本)9798331505929

The safe operation of automated vehicles depends on their ability to perceive the environment comprehensively. However, occlusion, sensor range, and environmental factors limit their perception capabilities. To overcome these limitations, collective perception enables vehicles to exchange information. However, fusing this exchanged information is a challenging task. Early fusion approaches require large amounts of bandwidth, while intermediate fusion approaches face interchangeability issues. Late fusion of shared detections is currently the only feasible approach. However, it often results in inferior performance due to information loss. To address this issue, we propose MR3D-Net, a dynamic multi-resolution 3D sparse voxel grid fusion backbone architecture for LiDAR-based collective perception. We show that sparse voxel grids at varying resolutions provide a meaningful and compact environment representation that can adapt to the communication bandwidth. MR3D-Net achieves state-of-the-art performance on the OPV2V 3D object detection benchmark while reducing the required bandwidth by up to 94 % compared to early fusion. Code is available at https://***/ekut-esIMR3D-Net © 2024 IEEE.

关键词： Vehicle-to-grid

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Analyzing Swarm Robotics Approaches in Natural Disaster Scenarios: A Comparative Study 1

Analyzing Swarm Robotics Approaches in Natural Disaster Scen...

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1st International Conference on Innovative and Intelligent Information Technologies, IC3IT 2024

作者： Hafid, Aicha Hocine, Riadh Guezouli, Lahcene Computer Science Departementn Algeria University of Chahid Mostafa Benboulaid Batna2 Faculty of Sciences and Technology Computer Systems Computer Science Departementn Algeria

ISBN: (纸本)9798331541064

Natural disasters such as earthquakes, volcanic eruptions, tsunamis, and wildfires pose significant challenges for human intervention due to their unpredictable nature and the hazardous environments they create. In recent years, swarm robotics has emerged as a promising approach to address these challenges. Swarm robots, inspired by the collective behavior observed in nature, operate autonomously and collaboratively, offering unique advantages in disaster scenarios. These robots can rapidly deploy, adapt to dynamic environments, and pro- vide extensive coverage, making them particularly suited for search and rescue missions, environmental monitoring, and infrastructure assessment. This paper presents a comprehensive review and comparative analysis of various approaches utilizing swarm robotics to tackle natural disasters. We explore key methodologies, including decentralized coordination algorithms, communication protocols, and adaptive navigation strategies, and evaluate their effectiveness in real-world disaster scenarios. Through a detailed examination of existing literature and case studies, we highlight the strengths and limitations of each approach, providing insights into their practical applications and potential for future developments. The findings underscore the critical role of swarm robotics in enhancing disaster response capabilities, offering scalable, resilient, and efficient solutions for mitigating the impact of natural disasters. We conclude with recommendations for future research directions aimed at overcoming current limitations and advancing the field toward fully autonomous and reliable swarm-based disaster management systems. © 2024 IEEE.

关键词： Volcanoes

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