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

作者： Yogabalajee, V. Sundaram, Karthic Kanagaraj, Kamaraj Kpr Institute of Engineering and Technology Department of Computer Science Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350384369

The agricultural industry is currently grappling with the challenges posed by plant diseases, particularly those that impact soybean crops. Traditional disease diagnosis methods are often time-consuming and rely heavily on manual image analysis, which can introduce inefficiencies and potential errors into the process. However, recent strides in deep learning models, such as Shufflenet, InceptionResNetV2, DenseNet121, and Enhanced DenseNet121, have emerged as a promising avenue for enhancing the precision and efficiency of soybean disease identification. Enhanced DenseNet121 has modified and additional layers is improved the perfromance, in particular, has showcased exceptional accuracy, achieving an impressive 98.34% success rate. The core objective of this research is to advance sustainable agricultural practices by leveraging advanced technology for precise classification of plant diseases, thereby offering significant advantages to the agricultural sector. These cutting-edge models not only improve disease identification but also contribute to increased crop yields, reduced chemical usage, and resource optimization, ultimately fostering a more sustainable and prosperous future for agriculture. © 2024 IEEE.

关键词： Plant diseases

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Network Anomaly Detection using Autoencoder on Various Datasets: A Comprehensive Review

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Recent Patents on engineering 2024年第9期18卷 63-77页

作者： Singh, Richa Srivastava, Nidhi Kumar, Ashwani Amity Institute of Information Technology Amity University Lucknow Campus Uttar Pradesh Lucknow India Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida India

The scientific community is currently very concerned about information and communication technology security because any assault or network anomaly can have a remarkable collision on a number of areas, including national security, the storage of private data, social welfare, economic concerns, and more. As a result, many strategies and approaches for this goal have been developed over time, making the anomaly detection domain a large research subject. The primary concern of this patent study is to review the most crucial elements relating to anomaly detection, including an overview of background analysis and a core study on the most important approaches, procedures, and systems in the field. To make the structure of this survey easier to understand, the domain of anomaly detection was examined along with five dimensions: Detection methods in network traffic, objectives of the patent paper, various datasets used, accuracy, and open issues/gaps. The gap which has been identified after the survey can be extended as a future scope might be helpful for the researcher. © 2024 Bentham science Publishers.

关键词： Anomaly detection

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Implementation of Elliptic Curve Cryptosystem with Bitcoin Curves on SECP256k1, NIST256p, NIST521p, and LLL

Journal of ICT Standardization

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Journal of ICT Standardization 2023年第4期11卷 329-354页

作者： Ulla, Mohammed Mujeer Preethi Khan, Md Sameeruddin Sakkari, Deepak S. School of Computer Science and Engineering Presidency University Karnataka Bangalore India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering Sri Krishna Institute of Technology Bangalore India

Very recent attacks like ladder leaks demonstrated the feasibility of recovering private keys with side-channel attacks using just one bit of secret nonce. ECDSA nonce bias can be exploited in many ways. Some attacks on ECDSA involve complicated Fourier analysis and lattice mathematics. This paper will enable cryptographers to identify efficient ways in which ECDSA can be cracked on curves NIST256p, SECP256k1, NIST521p, and weak nonce, kind of attacks that can crack ECDSA and how to protect yourself. Initially, we begin with an ECDSA signature to sign a message using the private key and validate the generated signature using the shared public key. Then we use a nonce or a random value to randomize the generated signature. Every time we sign, a new verifiable random nonce value is created, and a way in which the intruder can discover the private key if the signer leaks any one of the nonce values. Then we use Lenstra–Lenstra–Lovasz (LLL) method as a black box, we will try to attack signatures generated from bad nonce or bad random number generator (RAG) on NIST256p, SECP256k1 curves. The combination of nonce generation, post-message signing, and validation in ECDSA helps achieve Uniqueness, Authentication, Integrity, and Non-Repudiation. The analysis is performed by considering all three curves for the implementation of the Elliptic Curve Digital Signature Algorithm (ECDSA). The comparative analysis for each of the selected curves in terms of computational time is done with the leak of nonce and with the Lenstra–Lenstra–Lovasz method to crack ECDSA. The average computational costs to break ECDSA with curves NIST256p, NIST521p, and SECP256k1 are 0.016, 0.34,0.46 respectively which is almost zero depicting the strength of the algorithm. The average computational costs to break ECDSA with curves SECP256K1 and NIST256p using LLL are 2.9 and 3.4 respectively. © 2023 River Publishers.

关键词： Authentication

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Ultra-low power MoS2 optoelectronic synapse with wavelength sensitivity for color target recognition

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science China(Information sciences) 2025年第4期68卷 187-196页

作者： Bo WEI Yabo CHEN Xiaotong HAN Yan KANG Bujia LIANG Cheng LI Xiaokuo YANG Liang FANG Yuanxi PENG Institute for Quantum Information & State Key Laboratory of High Performance Computing College of ComputerNational University of Defense Technology Fundamentals Department Air Force Engineering University College of Advanced Interdisciplinary Studies National University of Defense Technology Institute of Quantum Information Science and Technology College of ScienceNational University of Defense Technology College of Computer National University of Defense Technology

Optoelectronic synapses that integrate visual perception and pre-processing hold significant potential for neuromorphic vision systems（NVSs）. However, due to a lack of wavelength sensitivity, existing NVS mainly focuses on gray-scale image processing, making it challenging to recognize color images. Additionally, the high power consumption of optoelectronic synapses, compared to the 10 fJ energy consumption of biological synapses, limits their broader application. To address these challenges, an energy-efficient NVS capable of color target recognition in a noisy environment was developed,utilizing a MoS2optoelectronic synapse with wavelength sensitivity. Benefiting from the distinct photon capture capabilities of 450, 535, and 650 nm light, the optoelectronic synapse exhibits wavelength-dependent synaptic plasticity, including excitatory postsynaptic current（EPSC）, paired-pulse facilitation（PPF）, and long-term plasticity（LTP）. These properties can effectively mimic the visual memory and color discrimination functions of the human vision system. Results demonstrate that the NVS, based on MoS2optoelectronic synapses, can eliminate the color noise at the sensor level, increasing color image recognition accuracy from 50% to 90%. Importantly, the optoelectronic synapse operates at a low voltage spike of0.0005 V, consuming only 0.075 fJ per spike, surpassing the energy efficiency of both existing optoelectronic and biological synapses. This ultra-low power, color-sensitive device eliminates the need for color filters and offers great promise for future deployment in filter-free NVS.

关键词： optoelectronic synapse neuromorphic vision system color recognition 2D materials image pre-processing

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Deep convolutional spiking neural network fostered automatic detection and classification of breast cancer from mammography images

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Research on Biomedical engineering 2023年第4期39卷 833-841页

作者： SenthilPrakash, T. Kannan, G. Prabhakaran, Salini Prajapati, Bhagirath Parshuram Department of Computer Science and Engineering Shree Venkateshwara Hi-Tech Engineering College Tamil Nadu Gobichettipalayam India Department of Electronics and Communication Engineering Amrita College of Engineering and Technology Nagercoil India Department of Computer Science and Engineering Puducherry Technological University Puducherry India Department of Computer Engineering A. D. Patel Institute of Technology Gujarat New Vallabh Vidyanagar India

Purpose: Breast cancer (BC) is a leading cause of death among women. Several existing methods on breast cancer classification were suggested with machine learning, but none of them accurately classify the breast cancer and requires high computational time. To overcome these problems, deep convolutional spiking neural network fostered automatic detection and classification of breast cancer from mammography images (DCSNN-BCDC-MI) is proposed in this manuscript. Methods: Initially, input breast mammography pictures are taken from Digital Database for Screening Mammography (DDSM) data set. The input breast mammography images are pre-processed and feature extracted. The DCSNN is employed to categorize pictures using retrieved characteristics, such as benign and malignant. Results: The proposed DCSNN-BCDC-MI technique is implemented at PYTHON. The performance of the proposed DCSNN-BCDC-MI technique attains 31.13%, 12.78%, 14.38%, 19.33%, and 19.11% high accuracy and 61.08%, 66.42%, 66.54%, 50.26%, and 53.03% low computation time comparing to the existing techniques, like deep learning fed capsule neural network for breast cancer detection utilizing mammogram images (OMLTS-DLCN-BCDC-MI), extended model for tumor location detection based on deep learning (VGG-CNN-BCDC-MI), deep learning algorithm for automated identification with breast cancer classification depending on transfer-learning strategy (VGG16-BCDC-MI), deep learning method for breast cancer identification on mammography by learning certain cancerous mass representations (CNN-Deep AE-BCDC-MI), and automatic identification with mammograms classification dependent on increased extreme learning machine along deep learning (ICS-ELM-BCDC-MI), respectively. Conclusion: The simulation result exemplifies that the proposed method can be capable to determine the optimum global solutions efficiently with the exact existing methods. © 2023, The Author(s), under exclusive licence to The Brazilian Society of Biomedical Enginee

关键词： Mammography

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Effective NSGA-II Algorithm for a Limited AGV Scheduling Problem in Matrix Manufacturing Workshops with Undirected Material Flow

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Complex System Modeling and Simulation 2025年第1期5卷 68-85页

作者： Xuewu Wang Jianing Zhang Yi Hua Rui Yu School of Information Science and Engineering East China University of Science and TechnologyShanghai 200237China Institute for Sustainable Manufacturing and also with the Department of Electrical and Computer EngineeringUniversity of KentuckyLexingtonKY 40506USA

Automatic guided vehicles(AGVs)are extensively employed in manufacturing workshops for their high degree of automation and *** paper investigates a limited AGV scheduling problem(LAGVSP)in matrix manufacturing workshops with undirected material flow,aiming to minimize both total task delay time and total task completion *** address this LAGVSP,a mixed-integer linear programming model is built,and a nondominated sorting genetic algorithm II based on dual population co-evolution(NSGA-IIDPC)is *** NSGA-IIDPC,a single population is divided into a common population and an elite population,and they adopt different evolutionary strategies during the evolution *** dual population co-evolution mechanism is designed to accelerate the convergence of the non-dominated solution set in the population to the Pareto front through information exchange and competition between the two *** addition,to enhance the quality of initial population,a minimum cost function strategy based on load balancing is *** local search operators based on ideal point are proposed to find a better local *** improve the global exploration ability of the algorithm,a dual population restart mechanism is *** tests and comparisons with other algorithms are conducted to demonstrate the effectiveness of NSGA-IIDPC in solving the LAGVSP.

关键词： limited automatic guided vehicle(AGV)scheduling problem nondominated sorting genetic algorithm II(NSGA-II) dual population co-evolution matrix manufacturing workshop

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Innovative Skin Melanoma Diagnosis using Deep Learning and African Gorilla Troops Optimizer 1

Innovative Skin Melanoma Diagnosis using Deep Learning and A...

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1st International Conference on Software, Systems and Information technology, SSITCON 2024

作者： Parthasarathi, P. Nivedha, S. Balamurugan, M. Yuvaraj, D. Siamala Devi, S. Sangeethaa, S.N. Bannari Amman Institute of Technology Department of Computer Science and Engineering Sathyamangalam India Bannari Amman Institute of Technology Department of Information Technology Sathyamangalam India Sona College of Technology Dpartment of Computer Science and Engineering Salem India Kongu Engineering College Department of Computer Science and Design Perundurai India Sri Eshwar College of Engineering Kuniamuthur Department of Information Technology Coimbatore India Bannari Amman Institute of Technology Sathyamangalam Department of Computer Science and Engineering Erode India

ISBN: (纸本)9798350352931

Skin melanoma, a dangerous kind of skin cancer, necessitates accurate identification and diagnosis to improve patient outcomes. The use of deep learning algorithms into medical imaging promises significant advancements in skin disease analysis, particularly melanoma diagnosis. This study looks at the usage of a novel optimisation technique, the African Gorilla Troops Optimizer (AGTO), to enhance deep learning models in the segmentation and analysis of skin images. The AGTO, which is based on African gorilla groups' social and hunting activities, is an effective approach of improving convolutional neural networks (CNNs) for accurate and reliable skin image segmentation. The proposed approach uses AGTO to distinguish melanoma lesions from benign skin disorders, which increases their identification. Extensive research on benchmark skin image datasets has confirmed the AGTO-enhanced deep learning model's durability and performance, emphasising its clinical use in dermatology and the proposed model shows 98 %accuracy. This work not only underlines the need of advanced optimisation techniques in medical image processing, but it also paves the way for more effective diagnostic tools for skin problems. © 2024 IEEE.

关键词： Image segmentation

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Enhanced Visuals through Software-Optimized Memory Pipeline 2

Enhanced Visuals through Software-Optimized Memory Pipeline

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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Ramesh, P.S. Senthilvadivu, S. Sudha, I. Kumar, T. Rajesh Naveen, V. Nagarajan, B. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Saveetha University Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Simats Department of Computer Science and Engineering Tamil Nadu Chennai India Madanapalle Institute of Technology and Science Department of Computer Science and Technology Madanapalle India Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Coimbatore India

ISBN: (纸本)9798350387933

Image processing algorithms, which supply the image quality, are used by modern mobile devices to capture images. These methods need more RAM to process an image and fix these problems. Software pipelines are utilized to reduce memory usage as much as feasible while maintaining the highest level of flexibility and affordability. Our method uses a line-based buffer software pipeline, which improves image quality and runs at a minimal cost. By transferring only the output to the next phase, the algorithm operates on an individual basis. Each step is completed in less time. Using picture enhancement and noise reduction techniques results in increased productivity, reduced memory consumption, and greater image quality. The applied framework demonstrates noise reduction with the PSNR algorithm and yields good results. © 2024 IEEE.

关键词： Image quality

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Real-Time Kannada Sign Language Recognition to Speech and Text for Rural Primary Healthcare Centers 1

Real-Time Kannada Sign Language Recognition to Speech and Te...

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1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Swetha, P. Sreenivasa, B.R. Pes University Department of Computer Science and Engineering Bangalore India Bapuji Institute of Engineering & Technology Department of Computer Science and Design Davangere India

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376517

Understanding and interpreting sign languages are crucial for promoting communication among the Deaf and Hard of Hearing (DHH) populations [1]. Kannada Sign Language (KSL), primarily utilized in Karnataka, lacks sufficient digital tools and technical support compared to several other well-studied sign languages. To reduce this gap, this study uses machine language learning (ML) techniques to convert KSL gestures into text and speech. The hand movements and facial expressions used in the KSL signs were gathered and examined for this purpose. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs), two examples of advanced machine-learning methods, have been used to create a reliable recognition system that can perform KSL in real time. The model has shown promising accuracy rates in the preliminary findings, instilling confidence in its potential to enhance KSL users' communication significantly. This study aims to improve education and daily communication among KSL speakers. The proposed model can be used at the regional Primary Healthcare Centers (PHC) for the effective interaction of a patient and doctors. In the future, we plan to expand the dataset, improve the algorithmic performance, and create user-friendly programs to make such programs useful at urban hospitals, too. © 2024 IEEE.

关键词： Convolutional neural networks

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Content Analysis of Twitter Conversations Associated with Turkey–Syria Earthquakes

Content Analysis of Twitter Conversations Associated with ...

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International Conference on Data Analytics and Management, ICDAM 2023

作者： Kaur, Harkiran Kumar, Harishankar Singla, Abhinandan Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Punjab Patiala147001 India

ISBN: (纸本)9789819965434

This research study performs content analysis of Twitter discussions associated with the earthquakes that occurred in Turkey and Syria in 2023. The authors investigated the main themes and topics of the discussions, expressed in the tweets. A dataset of tweets related to this topic has been collected using relevant hashtags and keywords. The obtained data has been analyzed using both manual and automated state-of-the-art methods. As the main findings of this study it has been observed that the most common themes in the tweets were expressions of sympathy and solidarity, calls for help and financial support, and news updates about the earthquakes. As per this proposed study, Twitter offers a worthwhile forum for people to express their state of mind and responses to natural catastrophes and may be used to segment information and assembly assistance in the times of need. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data Analytics

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