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SSARS: Secure smart-home activity recognition system

computers and Electrical Engineering 2025年 123卷

作者： Anna Palagan, C. Selvin Retna Raj, T. Muthuvairavan Pillai, N. Anish Pon Yamini, K. Department of Electronics and Communication Engineering Saveetha Engineering College Tamil Nadu Chennai India Department of Electronics and Communication Engineering DMI College of Engineering Tamil Nadu Chennai India Department of Computer Science and Business Systems R.M.D Engineering College Tamil Nadu Tiruvallur India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India

Smart homes provide assistance services that enhance the well-being, independence, and health of the residents, particularly the elderly. As techniques for human activity recognition in smart homes continue to advance, current methods face challenges such as insecure transmission of raw data and individual movement classification. To overcome these challenges, this study proposes Secure Smart-Home Activity Recognition System (SSARS). The proposed methodology utilizes an advanced preprocessing technique, AI-PSD, to reduce impulse noise in the data by combining adaptive interpolation (AI) and power spectral density (PSD). The Fractional Fast Fourier Transform (F-FFT) effectively captures statistical and dynamic aspects of human activities, offering a more detailed understanding of movement patterns. The extracted features are securely transmitted through encryption based on Factor private Key-based Elliptic Curve Cryptography (FK-ECC). Additionally, this study introduces the Pade activation function with a modified Physical Neural Network (P-PNN) to improve the system's classification ability. The proposed SSARS showed outstanding performance across various metrics, including an accuracy of 98.68 % and a precision of 98.93 % when compared with existing state-of-the-art approaches. © 2025 Elsevier Ltd

关键词： Adaptive interpolation-power spectral density Factor private key-based elliptic curve cryptography Fractional fast Fourier transform Human activity recognition Physical neural network Smart home Viola-Jones algorithm

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Automatic Classification of Heart Sounds Utilizing Hybrid Model of Convolutional Neural Networks 5th

Automatic Classification of Heart Sounds Utilizing Hybrid Mo...

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5th International Conference on New Trends in Information and communications Technology Applications, NTICT 2021

作者： Shyaa, Methaq A. Hasan, Ayat S. Ibrahim, Hassan M. Hamza, Weam Saadi Department of Information and Communication Systems Iraqi Ministry of Interior Baghdad Iraq Department of Computer Science College of Education for Pure Science University of Diyala Diyala Iraq University of Information Technology and Communication Baghdad Iraq

ISBN: (纸本)9783030934163

This paper introduces the employment of a deep learning model to develop an automated classification of heart sound into normal and abnormal. The Cardiology Challenge database 2016 (PhysioNet) has been employed in this work. This dataset is established with the assumption that the phonocardiograms (PCG) are extracted concurrently with the electrocardiograms (ECG). For each heartbeat, the PCG record on a time-domain basis is segmented and is converted to images. Then, these images feed the proposed convolutional neural network (CNN) model to perform the classification. Most previous methods used traditional machine learning techniques to classify the heartbeats. These methods depend on feature extraction and selection methods that are sensitive to different colors, shapes, and sizes. CNN has the ability to achieve feature extraction and classification automatically, unlike traditional machine learning methods which require manual feature extraction, and the performance relies on the chosen features. The proposed is designed by integrating multiple ideas including parallel convolutional layers and residual links. To prove that the proposed model is beneficial in terms of feature extraction and classification, the extracted features have been used to train the SVM classifier. The results show that the model can offer acceptable and comparable accuracy of classification. It has outperformed the previous methods by achieving an accuracy of 93.49% and 94.66% with the SVM classifier. High performance could help id saving many people’s lives. © 2021, Springer Nature Switzerland AG.

关键词： Cardiology

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Comparative Study between KNN and CNN's Techniques for Kidney Stone Detection

Comparative Study between KNN and CNN's Techniques for Kidne...

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Smart Computing and Application (ICSCA), International Conference on

作者： Fahdah M. Al-Tbenawey Shoayee Alotaibi Marwa Salem Shamaila Mir Zahida Mir Department of Computer Science and Information Computer Science and Engineering College University of Ha'il Hail Saudi Arabia Department of Electrical Communication and Electronics Systems Engineering Faculty of Engineering Modern Science and Arts University (MSA) Cairo Egypt

This paper aims to determine the better technique for kidney stone detection between K-Nearest Neighbor (KNN) and Convolutional Neural Networks (CNNs). As well known, the presence of kidney stones is an important topic that deserves discussion regarding expected medical outcomes. Most of the detection measures comprise CT scans, ultrasounds, and abdominal X-rays. These measures have varying effectiveness levels, hence used in the detection to prepare patients for specific treatment options. Patients' treatment options comprise percutaneous nephrolithotomy (PCNL), extracorporeal shockwave lithotripsy (ESWL), and ureteroscopy. The different detection techniques carried out for the patients are essential to test whether the patient has small or large kidney stones. One essential technique is using conventional neural networks (CNNs) technology. In this paper kidney, stone detection will be carried out using both of K-Nearest Neighbor (KNN) and CNN's techniques. A comparison between both techniques will be carried out to figure out the better. All of the simulations are carried out using MATLAB.

关键词： Support vector machines Image segmentation Ultrasonic imaging Ultrasonic variables measurement Particle separators Neural networks X-rays

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An Improved Fuzzy Logic Based Alcohol Detection System to Preserve Road Safety using Smart Sensors Association 3

An Improved Fuzzy Logic Based Alcohol Detection System to Pr...

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3rd IEEE International Conference on Mobile Networks and Wireless communications, ICMNWC 2023

作者： Kavitha, V.R. Muthu, Annamalai Geetha, B. Pandi, V. Samuthira Dinesh, M. Princy, B. Anni R.M.K. Engineering College Department of Computer Science and Business Systems Tamil Nadu Chennai India University of Technology & Applied Science Department of Engineering Nizwa Oman Prince Shri Venkateshwara PadmavathyEngineering College Department of Science & Humanities Tamil Nadu Chennai India Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Tamil Nadu Chennai India Saveetha School of Engineering SIMATS Department of VLSI Microelectronics Tamil Nadu Chennai India Panimalar Engineering College Department of Computer and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350317022

The increased risk of being involved in a traffic collision is directly proportional to the rate at which the number of vehicles on the road is growing. The majority of people in the world believe that driving under the influence of alcohol is a significant factor in the number of people killed in car accidents. The fundamental purpose of this investigation is to develop a device that is able to determine the degree of drunkenness that is present in the driver of a motor vehicle. The purpose of the proposed system is to discourage users from getting behind the wheel while under the influence of alcohol or drugs in order to reduce the number of accidents that are caused by intoxicated drivers. As part of this investigation, we propose the Fuzzy Assisted Alcohol Detection Mechanism (FAADM), which is a logical fuzzy-based alcohol detection mechanism. It uses a Direct Current (DC) motor as the vehicle's engine and a push button as its ignition system. This mechanism detects the presence of alcohol using a fuzzy-logic approach. The efficiency of this system is always being evaluated to ensure that it performs as designed. If this system were put into place, there would be a reduction in the number of accidents on the roads of smart cities that were caused by drivers who were under the influence of alcohol. It is strongly suggested that this system be fitted and installed inside the vehicle. In the event of an unexpected emergency, the engine should be turned off and the alarm should be activated. If there has been no drinking or accidents, the security system can be disarmed and the vehicle's engine should be started. If alcohol is found, the car will be turned off and an alarm will ring. As the number of incidents caused by intoxicated drivers continues to rise, the implementation of a system that combines alcohol detection and vehicle immobilization has been proposed as a potential solution. Engineers have developed a gadget that can detect the amount of alcohol in a d

关键词： Alcohol Detection Mechanism Alcohol Identification Drunk and Drive FAADM Fuzzy Logic Microcontroller Road Safety Sensors

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User Acceptance of New HCI Technologies 6

User Acceptance of New HCI Technologies

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6th IEEE International Conference and Workshop Obuda on Electrical and Power Engineering, CANDO-EPE 2023

作者： Katona, Jozsef Kovari, Attila Institute of Electronics and Communication Systems Obuda University Kandó Kálmán Faculty of Electrical Engineering Budapest Hungary Institute of Computer Engineering University of Dunaujvaros Department of Software Development and Application Dunaujvaros Hungary John von Neumann University Gamf Faculty of Engineering and Computer Science Kecskemét Hungary Institute of Digital Technology Eszterházy Károly Catholic University Faculty of Computer Science Eger Hungary Institute of Engineering University of Dunaujvaros Department of Natural Sciences Dunaujvaros Budapest Hungary

ISBN: (纸本)9798350328752

Human-computer interaction (HCI) is an evolving field of research that focuses on understanding and improving the communication and interaction between humans and computers. Over the past decades, we have seen many significant advances in this field, which have contributed to the widespread adoption and integration of technology into our daily lives. HCI research and development aims to design information technology systems to meet the needs and preferences of users. Usability, efficiency, accessibility and user satisfaction are important considerations in HCI design. This paper presents the results of a pilot study on user acceptance of HCIs. The results show that users are positive about and willing to use HCIs. © 2023 IEEE.

关键词： Human computer interaction

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Performance Analysis and Early Detection of Lung Cancer with Spiking Neural Networks

Performance Analysis and Early Detection of Lung Cancer with...

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Emerging systems and Intelligent Computing (ESIC), International Conference on

作者： Annasab Krishna Ghatage V S Priya Sumitha P Srinivasan Muhammed Anshad P. Y Badepally Mallaiah A. Durai Murugan Department of General Engineering DKTE Society’s Textile and Engineering Institute (Rajwada) Ichalkaranji M.S. India Department of Electronics and Communication Systems KG College of Arts and Science Coimbatore Tamilnadu India Department of Electronics and Communication Engineering Sona College of Technology Salem Tamilnadu India Department of Computer Science MES Institute of Technology & Management Kerala India Department of Information Technology CVR College of Engineering Hyderabad Telangana India Department of Computer Science And Business Systems M.Kumarasamy College of Engineering India

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522117

Cancer of the lung occurs when non-native lung cells grow uncontrollably. The increased incidence of this disease has raised both sexes mortality rates. Lung cancer is a disease that cannot be eradicated, although its risk can be diminished. The survival rate of lung cancer is greatly affected by its early identification. Training the model, extracting features, binarizing, decreasing noise, and converting to grayscale are suggested steps. We convert RGB photographs to grayscale first. Noise reduction removes background noise. A threshold value binarizes grayscale pictures into binary. Feature extraction uses directional gradient histograms, the Gabor filter, and standard deviation. Training the model with the Spiking Neural Network (SNN) ensures accuracy and durability. Compared to Alex Net and CNN, the suggested model is more accurate. It greatly increases lung cancer detection with 91.14% accuracy. This emphasizes early detection's role in lung cancer survival. The proposed method combines state-of-the-art feature extraction with model training to diagnose lung cancer accurately. Due to its precision, this approach may improve diagnostics and health outcomes.

关键词： Training Accuracy Computational modeling Lung cancer Spiking neural networks Gray-scale Predictive models Feature extraction Convolutional neural networks Standards

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Using Deep Learning for Simulation of Real time Video Streaming Applications

Using Deep Learning for Simulation of Real time Video Stream...

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Simulation Winter Conference

作者： Abdolreza Abhari Dipak Pudasaini Department of Computer Science Distributed Systems & Multimedia Processing Laboratory (DSMP lab) Ryerson University Toronto Ontario CANADA

ISBN: (纸本)9781665476621

The traditional approaches for simulation of video analytics applications suffer from the lack of real-data generated by employed machine learning techniques. Machine learning methods need huge data that causes network congestion and high latency in cloud-based networks. This paper proposes a novel method for performance measurement and simulation of video analytics applications to evaluate the solutions addressing the cloud congestion problem. The proposed simulation is achieved by building a model prototype called Video Analytic Data Reduction Model (VADRM) that divides video analytic jobs into smaller tasks with fewer processing requirements to run on edge networking. Real data generated from VADRM prototype is characterized and tested by curve fitting to find the distribution models for generating the larger number of artificial data for resource management simulation. Distribution models based on real data of CNN-based VADRM prototype are used to build a queueing model and comprehensive simulation of real-time video analytics applications.

关键词： Analytical models Visual analytics Computational modeling Buildings Prototypes Data models Real-time systems

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Joint Data Transmission and Energy Harvesting for MISO Downlink Transmission Coordination in Wireless IoT Networks

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Sensors 2023年第8期23卷 3900页

作者： Liu, Jain-Shing Lin, Chun-Hung Hu, Yu-Chen Donta, Praveen Kumar Department of Computer Science and Information Engineering Providence University Taichung433 Taiwan Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung804 Taiwan Department of Computer Science and Information Management Providence University Taichung433 Taiwan Research Unit of Distributed Systems TU Wien Vienna1040 Austria

The advent of simultaneous wireless information and power (SWIPT) has been regarded as a promising technique to provide power supplies for an energy sustainable Internet of Things (IoT), which is of paramount importance due to the proliferation of high data communication demands of low-power network devices. In such networks, a multi-antenna base station (BS) in each cell can be utilized to concurrently transmit messages and energies to its intended IoT user equipment (IoT-UE) with a single antenna under a common broadcast frequency band, resulting in a multi-cell multi-input single-output (MISO) interference channel (IC). In this work, we aim to find the trade-off between the spectrum efficiency (SE) and energy harvesting (EH) in SWIPT-enabled networks with MISO ICs. For this, we derive a multi-objective optimization (MOO) formulation to obtain the optimal beamforming pattern (BP) and power splitting ratio (PR), and we propose a fractional programming (FP) model to find the solution. To tackle the nonconvexity of FP, an evolutionary algorithm (EA)-aided quadratic transform technique is proposed, which recasts the nonconvex problem as a sequence of convex problems to be solved iteratively. To further reduce the communication overhead and computational complexity, a distributed multi-agent learning-based approach is proposed that requires only partial observations of the channel state information (CSI). In this approach, each BS is equipped with a double deep Q network (DDQN) to determine the BP and PR for its UE with lower computational complexity based on the observations through a limited information exchange process. Finally, with the simulation experiments, we verify the trade-off between SE and EH, and we demonstrate that, apart from the FP algorithm introduced to provide superior solutions, the proposed DDQN algorithm also shows its performance gain in terms of utility to be up to 1.23-, 1.87-, and 3.45-times larger than the Advantage Actor Critic (A2C), greed

关键词： Energy harvesting

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Challenges in Testing Large Language Model Based Software: A Faceted Taxonomy

arXiv

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arXiv 2025年

作者： Dobslaw, Felix Feldt, Robert Yoon, Juyeon Yoo, Shin Mid Sweden University Department of Communication Quality Management and Information Systems Östersund Sweden Chalmers University of Technology Department of Computer Science and Engineering Gothenburg Sweden KAIST School of Computing Daejeon Korea Republic of

Large Language Models (LLMs) and Multi-Agent LLMs (MALLMs) introduce non-determinism unlike traditional or machine learning software, requiring new approaches to verifying correctness beyond simple output comparisons or statistical accuracy over test datasets. This paper presents a taxonomy for LLM test case design, informed by both the research literature, our experience, and open-source tools that represent the state of practice. We identify key variation points that impact test correctness and highlight open challenges that the research, industry, and open-source communities must address as LLMs become integral to software systems. Our taxonomy defines four facets of LLM test case design, addressing ambiguity in both inputs and outputs while establishing best practices. It distinguishes variability in goals, the system under test, and inputs, and introduces two key oracle types: atomic and aggregated. Our mapping indicates that current tools insufficiently account for these variability points, highlighting the need for closer collaboration between academia and practitioners to improve the reliability and reproducibility of LLM testing. © 2025, CC BY-SA.

关键词： Taxonomies

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Precise Identification of Anomalies in Brats MR Brain Images using Metaheuristic Optimization Techniques 7

Precise Identification of Anomalies in Brats MR Brain Images...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Rosaline, S. Maheswari, K. Dhanasakkaravarthi, B. Vaishali, M. Kannan, Sathish Selvaraju, Nandakumar R.M.K. Engineering College Department of Electronics And Communication Engineering Thiruvallur Tamil Nadu Kavaraipettai601206 India Cmr Technical Campus Department of Computer Science And Engineering Hyderabad Telangana Kandlakoya501401 India Agni College of Technology Department of Mechanical Engineering Chennai Tamil Nadu Thazhambur603103 India Sri Eshwar College of Engineering Department of Computer Science And Business Systems Tamil Nadu Coimbatore641 202 India Saveetha School of Engineering Saveetha Institute of Medical And Techinical Sciences Department of Vlsi Microelectronics Tamil Nadu Chennai602105 India Hindustan Institute of Technology And Science Department of Automobile Engineering Chennai Tamil Nadu Padur603103 India

ISBN: (纸本)9798350359299

A condition known as an anomaly occurs when some brain features behave improperly and cause either minor or significant harm to brain cells. Brain tumors, vascular malformations, and inflammatory lesions are among the most well-known abnormalities. A variety of Machine Learning (ML) and Deep Learning (DL) methods are employed in the analysis and detection of brain irregularities. The use of metaheuristic optimization is one such method. Three optimization processes have been chosen for this investigation. The study examines and documents anomaly detection efficiency. A public dataset comprising brain scan images from disparate institutions is employed in the research to analyze the effectiveness of the detection procedure. The three algorithms used are Sequential Minimal Optimization (SMO), Krill Herd Optimization (KHO), and Particle Swarm Optimization (PSO). The three algorithms are fed the preprocessed images for training and testing. From the simulation result, it is established that the KHO algorithm is the best method for identifying brain abnormalities. The KHO achieved an accuracy of 0.96, whereas the competitive model like PSO scored an accuracy value of 0.93 and SMO attained 0.95. The KHO method is quite accurate in detecting the presence of any kind of abnormality. © 2024 IEEE.

关键词： Anomaly detection

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