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

作者： Gayathri, R. Raaj, R. Sathesh Balaanand, M. Kumar, J. Arun Sasikala, N. Rajesh, K. Psna College of Engineering and Technology Department of Computer Science and Engineering Dindigul India Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India Tagore Institute of Engineering & Technology Department of Computer Science and Engineering Salem India Dayananda Sagar University Department of Computer Science and Engineering Karnataka Harohalli India Ssm Institute of Science and Technology Department of Electronics and Communication Engineering Dindigul India

ISBN: (纸本)9798331504403

The Constant challenge faced by the farmers is to maintain their crops and enhance productivity. Despite various methods and solution evolving, still farmers can't upgrade to it. Farmers are not aware of the modern agricultural methods. The major cause is that the software and solution developed are of higher complexity. To upgrade them, technology is the foremost important tool to stand up in this modern world. To help them with their problem and adapt to the modern world, the development of software with inbuilt AI and user-friendly features (in agriculture) is needed. This study proposes a novel Agri-tech app, which is a IoT based app used by the farmers for weather forecasting, soil moisture and nutrient content analysis. The developed application also recommends the crop type that can be cultivated in the soil by using the database of soil moisture and nutrient content. IoT sensors fixed in the field are used to detect the moisture level in the soil and notify the farmers to irrigate the land. It recommends the farmer to add manures and fertilizers to soil by calculating the amount of nitrogen, phosphorous, and oxygen contents present in the soil. Using these technical innovation farmers can monitor their fields over a system or smart phone. Moreover, the proposed approach can promote crop diversity and rotation for healthier soil, encourage sustainable practices like organic farming. © 2024 IEEE.

关键词： Soil moisture

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Efficient Framework with Sparse Acquisition in CMOS Image Sensors for Low-Power Edge Devices 10

Efficient Framework with Sparse Acquisition in CMOS Image Se...

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10th IEEE International Symposium on Smart Electronic Systems, iSES 2024

作者： Kisku, Wilfred Kaur, Amandeep Mishra, Deepak Indian Institute of Technology Department of Electrical Engineering Jodhpur India Indian Institute of Technology Department of Computer Science and Engineering Jodhpur India

ISBN: (纸本)9798331533229

Memory bandwidth and power consumption is of utmost importance in the design of low power edge devices. This makes it essential to conserve power both at the sensor node and the computational unit. Our paper proposes a system design methodology to save power by acquiring compressed data at the CMOS Image Sensor (CIS) and making the Convolutional Neural Network (CNN) models lightweight for edge applications. A detailed study highlights the effect of sensor directed sparsity at the CIS, and its effect on CNN models such as VGG-16, MobileNet, ResNet50, EfficientNet, and ResNext models. It provides empirical evidence that the performance is mostly maintained even after pruning and quantization of considered models. The imagenette dataset is used to curate the data with the proposed acquisition pipeline that introduces data sparsity, and the experimental results provide evidence that the proposed pipeline performs at par with conventional CIS acquisition mode. For example, the analysis of VGG-16 model for a sparsity threshold level of 0.2 shows compression of × 7.6, × 16, and × 61 using only pruning (P), only quantization (Q), and with both pruning+quantization (P+Q), respectively. It is also observed that the proposed acquisition pipeline can achieve significant parameter reduction through pruning for model compression, comparable to convectional pipeline. © 2024 IEEE.

关键词： CMOS integrated circuits

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Enhanced MADM Strategy with Heptapartitioned Neutrosophic Distance Metrics

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Neutrosophic Sets and Systems 2025年 78卷 74-96页

作者： Eassa, Mohamed Myvizhi, M. Elkholy, Mohamed Abdelhafeez, Ahmed Elbehiery, Hussam Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan Department of Mathematics KPR Institute of Engineering and Technology Tamilnadu Coimbatore India Faculty of Computer Science& Engineering Alamein International University Egypt

In this work, a novel methodological approach to multi-attribute decision-making problems is developed and the notion of Heptapartitioned Neutrosophic Set Distance Measures (HNSDM) is introduced. By averaging the Penta-Partitioned Neutrosophic Distance Measures under particular conditional criteria, the suggested technique ranks the options. Additionally, we explore the fundamental characteristics of HNSDM and provide several illustrative theorems. The Heptapartitioned Neutrosophic Set Distance Measures are formulated based on the Hepta Neutrosophic Set. Lastly, a practical numerical example is provided to illustrate the efficacy of the suggested methodology. © 2025, University of New Mexico. All rights reserved.

关键词： Heptapartitioned Neutrosophic set Heptapartitioned Neutrosophic set and MADM strategy Heptapartitioned set Neutrosophic sine distance measure

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Data Protection Strategies: Employing IBBE and Attribute-Based CPRE for Secure Data Distribution

Data Protection Strategies: Employing IBBE and Attribute-Bas...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Murali, Erasappa Jerinjose, J. Sharanya, L. Lakshmi Lokeswari, K. Roshini, K. Kalpana, D. Siddharth Institute of Engineering & Technology Department of Computer Science and Engineering Andhra Pradesh Chittoor India

ISBN: (纸本)9798350378092

Worldwide, people and businesses are starting to adopt cloud computing more and more. Although there are still many issues that need to be overcome, cryptography can assist users in public clouds in protecting their data. We present a practical and safe identity-based data group sharing and distribution system for public clouds that enables data owners to simultaneously broadcast encrypted data to multiple recipients. Security issues with public cloud infrastructure are the driving force behind data confidentiality regulations. We employ conditional proxy re-encryption that is both ascribe-based and timed-release, which gives the cloud server a reencryption key. These ways, only disseminators whose characteristics comply with the encrypted data's access policy are able to distribute it to other groups beyond the release period. Through this, we are able to disseminate data groups in a secure and adaptable manner. The distribution of cipher texts can be controlled by data owners with fine-grained and timed-release access control by re-encryption conditions that are connected to characteristics and release time. © 2024 IEEE.

关键词： Ciphertext

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Forensic Eye: Convolutional Neural Network-based Crime Scene Object Detection System 2

Forensic Eye: Convolutional Neural Network-based Crime Scene...

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2nd International Conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Sudha, I. Vadivel, M. Lakshmiramana, P. Kalpana, N. Kumar, Nellore Manoj Vinoth, R. Saveethe University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science Tamilnadu Chennai India Excel Engineering College Department of Information Technology Komarapalayam Namakkal India Madanapalle Institute of Technology & Science Department of Computer Science & Technology Andhra Pradesh India Psna College of Engineering and Technology Department of Computer Science and Engineering India Saveetha School of Engineering Department of Mathematics Tamilnadu Chennai India Rmk College of Engineering and Technology Department of Computer Science and Engineering Puduvoyal India

ISBN: (纸本)9798350364699

Forensic investigations rely heavily on the accurate detection and analysis of crime related objects present at crime scenes. Traditional methods of object detection often involve manual labor and are error prone process to human error. In recent years, Convolutional Neural Networks (CNNs) have shown remarkable success in various computer vision tasks, including in object detection. In this article, we introduce ForensicEye, a novel CNN-based crime scene object detection system designed to assist forensic investigators in identifying the objects related to the crime. It utilizes a state-of-the-art CNN architecture trained on a large dataset of crime scene images, enabling robust detection even in challenging lighting and environmental conditions. We evaluated the performance of the ForensicEye on benchmark datasets with various preprocessing techniques, demonstrating its superior accuracy and efficiency compared to traditional methods. Our proposed model achieved the accuracy of 96.2%, precision of 97.9%, Recall of 96.9%, F1-score of 97.4%. © 2024 IEEE.

关键词： Convolutional neural networks

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Automatic Pill Dispenser for Elderly Medical Adherence: A Survey 5

Automatic Pill Dispenser for Elderly Medical Adherence: A Su...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Divya, K.S. Ragurja, Ashwin Devanand, Gayathri Thomas, Antony Mannickathan, Gripsy Paul Nataraj, Neetha K. Adi Shankara Institute of Engineering and Technology Department of Computer Science and Engineering Kerala Kalady India

ISBN: (纸本)9798350383522

Automatic Pill Dispensers are becoming a necessity in the current ages. The survey paper mainly examines the contemporary medicine pill dispensers focusing specifically on Internet of Things (IoT) usage and its features. This meets the needs of elderly individuals with poor memory, making it suitable for use in nuclear nursing homes and shelters. The survey goes in depth into the different applications and systems used at these dispensers and reviews historical data to show the evolution of technology in this area. The main goal is to identify and investigate the unique features associated with each method. Additionally, the paper highlights the shortcomings in these systems and provides a better understanding of the progress and limitations of medicine dispensers designed for elderly. © 2024 IEEE.

关键词： Authentication Elderly Healthcare IoT Pill Dispenser

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An Approach to Integration of Gait and Fingerprint Features for Advanced Biometric Recognition technology 14

An Approach to Integration of Gait and Fingerprint Features ...

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14th International Conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Trivedi, Amit Kumar Kumar, Kartik Aggarwal, Raghav Garg, Anmol Thapar Institute of Engineering and Technology Department of Computer Science and Engineering Punjab Patiala147001 India

ISBN: (纸本)9798350344837

In this study, the convergence of gait recognition and fingerprint identification was explored using heat map integration. Gait recognition, a biometric method that relies on distinct walking patterns, finds applications in security and healthcare. The study focused on the accuracy assessment of various body segments when gait and fingerprint technologies were combined. The results indicated that the highest accuracy, at 97.12%, was achieved by integrating the lower body's middle portion with fingerprints. © 2024 IEEE.

关键词： Integration

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Automated recognition of Myanmar sign language using deep learning module

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International Journal of Information technology (Singapore) 2024年第2期16卷 633-640页

作者： Kyaw, Nwe Ni Mitra, Pabitra Sinha, G.R. Department of Computer Science and Engineering Indian Institute of Technology Kharagpur West Bengal Kharagpur India Computer Science and Engineering GSFC University Vadodara Gujarat India

The recognition of sign language in real-time poses a significant challenge due to the dynamic nature of hand gestures, and determination of the start and end points of each gesture becomes difficult in a video stream required in the recognition system. For diverse range of applications, researchers have undertaken vision-based gesture recognition studies. In this current research, deep learning approach is employed to develop a novel model specifically designed for the classification of sign language. Our study focuses on generating datasets comprising videos of Myanmar sign language (MSL) at the word level, incorporating various lighting conditions and complex backgrounds. The implementation of a Convolutional Neural Network (CNN) model allows us to present a unique approach to hand gesture recognition, using a comprehensive process involving data collection, parameter analysis, CNN model creation, as well as training and testing phases, aimed at facilitating precise recognition tasks. Notably, the utilization of the Stochastic Gradient Descent with Momentum (SGDM) optimizer resulted in a remarkable 98% success rate, while the Adam optimizer demonstrated an even higher success rate of 99%. Therefore, our proposed system is well-suited for the development of wearable real-time systems capable of automatically recognizing MSL. © The Author(s), under exclusive licence to Bharati Vidyapeeth's institute of computer Applications and Management 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

关键词： Classification Convolutional neural network Deep learning Hand gestures Myanmar sign language (MSL) recognition

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Federated Multitask Learning for Complaint Identification Using Graph Attention Network

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1277-1286页

作者： Singh, Apoorva Chandrasekar, Siddarth Sen, Tanmay Saha, Sriparna Department of Computer Science and Engineering Indian Institute of Technology Patna Patna801106 India Department of Electronics and Communication Engineering Iiitdm Kancheepuram600127 India Ericsson Kolkata700135 India

Prior study on automatically identifying complaints on social media has relied on extensive feature engineering in centralized settings, with no consideration for the decentralized, nonidentically independently distributed (Non-IID), and privacyconscious aspect of complaints, which can hinder data collection, distribution, and learning. In this work, we propose a graph attention network (GAT)-based multitask framework that intends to learn two closely related tasks, complaint detection (primary task) and sentiment classification (auxiliary task), simultaneously in federated learning scenarios. We propose the Federated Combination (FedComb) algorithm, a two-sided adaptive optimization technique that simultaneously optimizes global and local models. The proposed methodology outperforms several baselines for the intended task of recognizing complaints in decentralized settings, according to quantitative and qualitative studies on two benchmark datasets. Impact Statement-Automatic complaint identification is crucial to organizations as they provide insights to meet the customers' requirements, including handling and addressing the complaints. Unlike standard technologies developed using data stored in the cloud, a federated learning model allows businesses to learn complaint detection models without sharing their training data spread on their local databases with third-party cloud services. In this work, we propose a GAT-based sentiment-aided complaint identification in federated learning settings. The proposed FedComb algorithm integrates local and global model optimization, making it efficient compared to existing federated algorithms. © 2022 IEEE.

关键词： Deep learning

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Dynamic strategy to use optimum memory space in real-time video surveillance

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Journal of Ambient Intelligence and Humanized Computing 2023年第3期14卷 2771-2784页

作者： Biswas, Tamal Bhattacharya, Diptendu Mandal, Gouranga Department of Computer Science and Engineering National Institute of Technology Agartala Tripura Agartala India School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India

In present days real-time video surveillance is a very essential aspect of establishing safety, observing traffic, and detecting violence and crimes. Intelligent video surveillance (IVS) is one of the most acknowledged frameworks in a security application. But, video surveillance systems consume huge memory space to store the recorded video, especially in the case of high-resolution recording. However, if we use low-resolution video recording then the video quality will be reduced and the objects/violence cannot be identified clearly. To overcome the stated limitation, this article introduced a smart video surveillance system with a dynamic strategy to use optimum memory space with the best surveillance objective. The proposed system can dynamically record video with high-resolution during suspicious movement of objects/violence as well as low-resolution video at other times. The system is based on the detection of suspicious movement of objects in consecutive frames, saving of suspicious frames with high resolution, and neglecting of less important frames. Finally, the Contrast Limited Adaptive Histogram Equalization (CLAHE) based on Color Channel is applied to all the suspicious frames as a post-processing step for better contract adjustment and to provide a clear view of suspicious objects in frames. The quality of enhanced frames are assessed by two no-reference methods, i.e. NIQMC and BIQME. Multiple visual quality parameters like sharpness, contrast, colorfulness, brightness, and naturalness of frames, etc. are used for assessment Several real-time experiments reveal that the proposed system can detect suspicious objects with 97.65% accuracy. Besides, it consumes 75% less memory space in real-time surveillance. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Video recording

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