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Enhancing IoT-Driven Smart Home Security and Automation with a GCN Model 3

Enhancing IoT-Driven Smart Home Security and Automation with...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Ganti, Sreelakshmi Vankayalapati, Ravi Kumar Krishnamoorthy, P. Thakare, Pallavi S. Nayak, Ullal Akshatha Vignesh, P. Geethanjali College of Engineering and Technology Department of Electronics and Communication Engineering Medchal India Equinix TX United States Sasi Institute of Technology & Engineering Department of Computer Science and Engineering West Godavari India Dr D. Y. Patil Institute of Technology Department of Information Technology Pimpri India Nitte Meenakshi Institute of Technology Department of Information Science and Engineering Bangalore India Kcg College of Technology Department of Mathematics Chennai India

ISBN: (纸本)9798331508456

A network of computing devices and sensors, the Internet of Things (IoT) rapidly exchanges data to assess circumstances and propose new services. Intelligent home technologies rely on IoT. Contemporary smart home systems offer automatic lighting control, smoke detectors, temperature sensors, and smart lock installations. But these technologies raise serious privacy and security concerns. Security flaws let hackers access sensitive data, sendoff false alarms, and take control monitoring systems. Smart home technology is vulnerable to many threats because consumers are wary of it. Recommended steps are preprocessing, sensing, and model training. Preparing raw, unstructured data for analysis. DHT11 humidity and temperature sensors and HC-SR501 PIR motion detectors collect data. It trained the model using Multiscale DGCN data. Better than GCN and BiLSTM, Multiscale DGCN performs. It uses Internet of Things-based smart home security data well, with an average accuracy rate of 93.87%. Identifying outliers in sensor data solves major smart home security challenges. Increasing smart home system accuracy reduces security concerns and boosts user trust, which may drive adoption. © 2025 IEEE.

关键词： Smart homes

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Robust Fingerprint Minutiae Extraction and Matching using Improved AKAZE

Robust Fingerprint Minutiae Extraction and Matching using Im...

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Data science and Information System (ICDSIS), IEEE International Conference on

作者： R. Archana Reddy Hassan M. Al-Jawahry B S Shruthi K. Ma Heswaran Alok Kumar Pani School of Computer Science and Engineering SR University Warangal Telangana India Department of Computers Techniques Engineering College of technical Engineering The Islamic university Najaf Iraq Department of Civil Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science St. Joseph’s College (Autonomous) Tiruchirappalli India Department of Computer Science and Engineering School of Applied Science Birla Global University Bhubaneswar India

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381672

Personal identification is important to protect data from cyber-attacks. Minutiae feature extraction and matching are key tasks for recognizing fingerprints and are essential to Automatic Fingerprint Recognition (AFR) systems. Fingerprint recognition technologies are becoming increasingly significant in preventing identity theft and ensuring accurate identification. However, the major problem is their identification sensors are vulnerable to presentation or spoofing attacks from the fake fingerprint samples. To overcome these problems, an improved AKAZE is introduced for feature extraction and feature matching in the process of fingerprint identification. Thinning and dilation methods are used for preprocessing the image and Improved AKAZE is used for feature extraction of the fingerprint images. After extracting features, the proposed Improved AKAZE is used for identifying and matching those fingerprint images. The proposed method is evaluated on publicly available FVC2004 dataset. The results show that the improved AKAZE achieved 99.95% of accuracy in identification and matching of fingerprint images.

关键词： Performance evaluation Accuracy Image matching Identity theft Fingers Fingerprint recognition Feature extraction

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Multimodal Transformer Fusion for Emotion Recognition using Facial and Voice Data

Multimodal Transformer Fusion for Emotion Recognition using ...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： Rana Veer Samara Sihman Bharattej R Hyba AbdulJaleel K. Subhashini Ramya Srikanteswara Narendra Chennupati Doctorate of Business Administration National Louis University FL USA Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computer Applications Nandha Engineering College Erode India Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru India Hindsight Software Solutions Inc Frisco TX

ISBN: (数字)9798331533663

ISBN: (纸本)9798331533670

Nowadays, emotion recognition has become a crucial aspect of human-computer interaction for personalized and empathetic interactions. However, the existing Convolutional Neural Network (CNN) depended in a single modality which led to limited accuracy. Hence, this research proposes Multimodal Transformer Fusion (MTF) to recognize human emotions by combining the strengths of audio and image data. Initially, the input data is collected from Facial Emotion Recognition 2013 (FER2013) and Berlin Database of Emotional Speech (EmoDB) datasets which consists of facial images and speech signals. Then, the image data is preprocessed with histogram equalization and wiener filter to improve image quality and the signal data are preprocessed with Butterworth Bandpass Filter (BBF) respectively. After that, the Linear Binary Patterns (LBP) and Mel-Frequency Cepstral Coefficients (MFCC) are employed to extract the features from image and signal data respectively. Then, Convolutional Recurrent Neural Network (CRNN) and Bi-Directional Gated Recurrent Unit (Bi-Gru) are introduced to classify the emotions into seven types. Finally, the proposed MTF is employed to integrate the classified emotions and provides a final prediction of recognized emotions. From the results, the proposed MTF achieved better results when compared to CNN with Long Short-Term Memory (CNN-LSTM) in terms of accuracy (97.28%) respectively.

关键词： Emotion recognition Accuracy Image recognition Convolution Computational modeling Speech recognition Feature extraction Transformers Convolutional neural networks Integrated circuit modeling

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Efficient Video Anomaly Detection using One-Class Support Vector Machine

Efficient Video Anomaly Detection using One-Class Support Ve...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： Srinivasan J Vidyadevi G Biradar Muhamed Fallahhusein Hari Suresh Babu Gummadi R. Kavitha Department of Computer Applications Madanapalle Institute of Technology & Science Madanapalle India Department of Information Science and Engineering Nitte Meenakshi Institute of Technology Bangalore India Department of Computers Techniques Engineering College of Technical Engineering Islamic University of Najaf Najaf Iraq Frontier Communications Dallas USA Department of Electronics and Communication Engineering Vivekanandha College of Engineering for Women (Autonomous) Tiruchengode India

ISBN: (数字)9798331533663

ISBN: (纸本)9798331533670

Nowadays, video anomaly detection is crucial task in various applications and the increasing amount of video data generated from various sources has made it essential to develop efficient anomaly detection methods. However, the existing Local Outlier Factor (LOF) faced challenges in detecting anomalies due to the complexity and variability of video data. Hence, a One-Class Support Vector Machine (OCSVM) is proposed to identify unusual events in videos by separating normal and anomalous data through a hyperplane. Initially, the input video data is collected from University of California, San Diego (UCSD) pedestrian dataset which consists of grayscale video footage captured along walkways at the UCSD. Then, shot segmentation using boundary detection algorithm is done to divide the video into meaningful shots. Then, Vision Transformer (ViT) is employed to identify the relevant characteristics within the frames. After that, key frame extraction process selects the representative frames from video sequence by capturing significant changes or events. Finally, the proposed OCSVM is introduced to detect unusual patterns by learning a boundary around normal data points. From the results, the proposed OCSVM achieved better results when compared to existing Convolutional Neural Network (CNN) in terms of accuracy (95.74%), precision (92.16%), recall (92.11%), and F1-score (93.27%) respectively.

关键词： Support vector machines computer vision Pedestrians Computational modeling Video sequences Transformers Feature extraction Convolutional neural networks Integrated circuit modeling Anomaly detection

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Decision Support Method and Risk Analysis Based on Merged-Cyber Security Risk Management

Decision Support Method and Risk Analysis Based on Merged-Cy...

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Data science and Information System (ICDSIS), IEEE International Conference on

作者： Mohanarangan Veerappermal Devarajan Mohammed Al-Farouni Ramya Srikanteswara R Rana Veer Samara Sihman Bharattej Priyan Malarvizhi Kumar Ernst & Young (EY) Sacramento USA Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science Saint Leo University Florida United States Department of Information Science College of Information University of North Texas Texas USA

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381672

The Cyber Security Risk Management is the essential part in today’s business because of quickly changing landscape of threat and presence of developing the cyber-attacks. Developed numerous researches which aim to built systematic process in risk assessment and management. This challenge developed particularly in content of difficult infrastructures, from attacks has involved from technic to social technic and protect against them required that contextual data. Though, the previous researches have restricted input from the intelligence properties of threat and developing the attack trends, resulted in restricted contextual data relevance to the risks of cyber security. In this research, developed Merged Cyber Security Risk Management (m-CSRM) method which responded to the difficulty through helping the systematic identity of critical assets by utilize of decision support method developed on fuzzy set mechanism through predict the risk types by Machine Learning (ML) methos and that is processed through automated tool. The proposed m-CSRM method attained highest success rate of 82.13% which is efficient than other existing methods.

关键词： Deep learning Fuzzy sets Systematics Machine learning algorithms Data science Market research Prediction algorithms

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Quantum Inspired Marine Predator Algorithm with Long Short-Term Memory for Detection and Classification of Chronic Kidney Disease

Quantum Inspired Marine Predator Algorithm with Long Short-T...

引用

Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： T. Srikanth Hassan Mohamed D. Jayaprakash Vasanthakumar G U G. Vijayakumari School of Computer Science and Engineering Malla Reddy Engineering College for Women (UGC-Autonomous) Maisammaguda Hyderabad India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Information Technology Sona College of Technology Salem India Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Electronics and Communication Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

Chronic Kidney Disease (CKD) is a continual decline of the kidney working time being and it is a progressive condition. The main problem with CKD is, it is asymptomatic in its early stages, this makes the early detection challenging. In order to detect the symptoms of the disease and to figure out the exact stage of the disease advance technologies have been introduced. The traditional methods failed to diagnosing the disease accurately in early stage. The paper proposed a Quantum Inspired Marine Predator Algorithm (QIMPA) in order to predict and classify the chronic kidney disease. This algorithm is employed with the marine predators using Brownian and Levy flights that uses hunting strategies. This strategy works in three phases, firstly, prey's movements and strategies are noted how it escapes from the predator, then a balanced approach is created between predator and prey. Finally, in this phase, optimization process produces a Levy flight pattern to capture the predator and prey moves at low velocity. Long Short-Term Memory (LSTM) is utilized for the classification, it works by capturing the long-range values of features that enhances the diagnosing kidney disease. The proposed QIMPA model obtained 99.04% for CKD dataset compared to the existing methods.

关键词： Measurement Accuracy Prediction algorithms Feature extraction Chronic kidney disease Classification algorithms Pattern recognition Long short term memory Optimization Diseases

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Spatio-Temporal Analysis in Video for Gesture Recognition using 3-Dimensional Convolutional Neural Network with Long Short-Term Memory

Spatio-Temporal Analysis in Video for Gesture Recognition us...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： Srimaan Yarram Hyba AbdulJaleel P. Uma R Maranan S meenakshi Sundaram Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computer Science and Engineering Nandha Engineering College Erode India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai India Department of AI & DS Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (数字)9798331533663

ISBN: (纸本)9798331533670

Nowadays, the gesture recognition in smart interfaces has gained significant attention for allowing the users to interact with devices in a more natural and spontaneous way. Also, the spatio-temporal analysis in video plays a crucial role in gesture recognition where it involves understanding the spatial and temporal relationships between body parts as well as movements. However, the existing Hidden Markov Model (HMM) faced challenges in detecting gestures where the variations in lighting and backgrounds affected the performance. Hence, this research proposes a combination of 3-Dimensional Convolutional Neural Network with Long Short-Term Memory (3D-CNN-LSTM) to model the temporal dependencies by resisting lighting and background variations. Initially, the input data is collected from 20BN-Jester dataset which consists of short video clips and fed into preprocessing where, data augmentation is applied to increase the data quantity. After that, the extraction of key features extracts the salient frames where the major motion occurs to reduce redundancy. Finally, the proposed 3D-CNN-LSTM is employed to learn spatio-temporal features by capturing information from frames and the fully connected layer with SoftMax activation function classifies the hand gestures. From the results, the proposed 3D-CNN-LSTM established better results when compared to existing 2D-CNN in terms of accuracy (98.9%) respectively.

关键词： Hands Accuracy Redundancy Lighting Hidden Markov models Gesture recognition Feature extraction Data augmentation Convolutional neural networks Long short term memory

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Urban Change Detection based Convolutional Auto Encoder - Content Loss using High-Resolution Images

Urban Change Detection based Convolutional Auto Encoder - Co...

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Distributed Systems, computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： K. Geethaprathibha Haider Alabdeli Mamatha Bai B G Bura Vijay Kumar Boya Divya Department of IT Malla Reddy Engineering College for Women(RH) Hyderabad India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru India School of Computer Science and Artificial Intelligence SR University Warangal India Department of CSE Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

The Change Detection (CD) in high-resolution images is significant for understanding the surface of land. There have developed numerous Deep Learning (DL) approaches to CD, many of these algorithms failed to predict edges and protect changed area of shape from the multi-spectral images. In this manuscript, the Convolutional Auto encoder - Content Loss (CAE-CL) approach is developed to detect the changes in urban areas. The CL function is used in the CAE, which enhances the performance of CAE to detect the changes of urban areas. Initially, the deep features are extracted by using the initial layers of CAE and then the features are given to final detection layer. This last layer detects the changes in urban areas effectively with high performance. Various evaluation measures considered to evaluate the performance of CAE-CL approach. The CAE-CL technique reached out the accuracy of 99.51%, precision of 99.75%, f1-score of 98.92%, Intersection of Union (IoU) of 98.03% and Kappa Coefficient (KC) of 89.27% while comparing with existing techniques.

关键词： Deep learning Accuracy Shape Image edge detection Urban areas Land surface Feature extraction Prediction algorithms Loss measurement computer security

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Automatic Diagnosis and Grading of Diabetic Retinopathy using Bat Optimization Algorithm-Refined Deep Residual Network

Automatic Diagnosis and Grading of Diabetic Retinopathy usin...

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Evolutionary Algorithms and Soft Computing Techniques (EASCT), International Conference on

作者： B. Haritha Lakshmi Venkata Ramana Kaneti R S Soundariya Sanjib Kumar Nayak Rajesh N IT Department Malla Reddy Engineering College for Women Hyderabad India Department of Computer Science and Engineering VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad India Department of Computer Technology Bannari Amman Institute of Technology Sathyamangalam India Department of Computer Science and Engineering (Data Science) CMR Technical Campus Hyderabad India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

Diabetic Retinopathy (DR) is one of the most severe sight-threatening disorders resulting from diabetes, and can eventually lead to blindness and visual impairment. Early detection and medical therapy can assist in controlling and preventing DR progression. However, manual grading is extremely difficult and time-consuming because of the retina’s complicated structure. In this research, the Bat Optimization Algorithm- Refined Deep Residual Network (BOA-RDRN) is proposed for automatic diagnosis and grading of DR. Initially, the DIARETDB0 dataset is employed for the proposed method, and then pre-processing is performed for image denoising which eliminates noise. The Attention-based Fusion Network (AFU-Net) is used to segment the lesion region. The Grey-Level Co-occurrence Matrix (GLCM) and Local Ternary Pattern (LTP) are used to extract the features and BOA is employed to find an optimal subset of features in retinal images. Finally, the RDRN is utilized to classify the anomalies as normal or abnormal. The BOA-RDRN achieves an accuracy of 99.42% compared to the existing methods such as Tunicate Swarm Spider Monkey optimization-based Refined Deep Residual Network (TSSMO-RDRN), Weighted Kernel Fuzzy C-Means Clustering and Dilation-Based Function (WKFM-DBF), Improved Harris Hawk Optimization-based Convolution Neural Network (IHHO-CNN), and Enhanced Fuzzy C-Means Clustering-Resnet 152 respectively.

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Compressed Data Aggregation Using Regulated Duty Cycle for WSN-IOT Networks 1

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International Conference on Advances in Communication technology and computer engineering, ICACTCE 2023

作者： Gupta, Anuradha Gulati, Tarun Bindal, Amit Kumar Kumar, Karan Ramachandra, A.C. Seleck, Július ECE Department M. M. Engineering College M. M. Deemed to be University Ambala Mullana India Department of Computer Science and Engineering M. M. Engineering College M. M. Deemed to be University Ambala Mullana India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengalore India Faculty of Management Comenius University in Bratislava Odbojárov 10 Bratislava 2582005 Slovakia

ISBN: (数字)9783031371646

ISBN: (纸本)9783031371639

IoT sensors can send the data using different modes i.e. periodically/one time only/continues streaming using different application-types i.e. periodic/single shot/streaming etc. and resource consumption may also vary as per requirement. In order to utilize resources in optimal way, for each application-type, there is need to regulate the data aggregation. It may be affected by other parameters also i.e. transmission range, Signal-to-Noise-Ratio (SNR) etc. This paper introduces a compressed data aggregation approach (using Zlib library) that takes care of application behavior by enforcing a transmission duty cycle and its performance will be analyzed using different parameters (SNR/Throughput/Packet delivery ratio/Residual Energy etc.). © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Signal to noise ratio

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