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1st International Conference on Intelligent computing, Smart Communication, and Network Technologies, ICICSCNT 2023

作者： Saravanakumar, C. Prakash, M. Deepak Kumar, A. Ashokkumar, C. Department of Computer Engineering Government Polytechnic College Purasawalkam Chennai600012 India School of Computer Science and Engineering Vellore Institute of Technology Vellore632014 India Department of Computer Science and Engineering St. Joseph’s Institute of Technology Chennai India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai603203 India

ISBN: (纸本)9783031759567

Liver disease begins in the ovaries and is especially hazardous for women. As a result, abnormal cells can potentially spread to other organs. Liver disease is a type of dangerous development that affects the ovaries in females, and it is difficult to detect at an early stage, which is why it remains one of the leading causes of death from disease. Unquestionable confirmation of intrinsic and natural components is crucial for the development of novel systems to detect and eliminate hazard. We propose the detection and categorization of liver illness in ultrasound pictures using hybrid machine learning techniques. Using Machine Learning Algorithms and a vote-based classifier to classify liver tissues as fatty or normal based on ultrasound picture attributes and voting. Our created method provides four major contributions: first, the classification of liver pictures as normal or fatty is performed without a segmentation phase. Secondly, compared to our suggested effort, the datasets in earlier works were inadequate. The third contribution is a collection of 26 characteristics. On the basis of the presented methods, sigmoid radial basis function neural network (SRBFNN) and Gray-Level Co-Occurrence Matrix are the recovered features (GLCM). The fourth contribution is the voting classifier that is utilised to determine the type of liver tissue. Fruit Fly Optimization concludes the categorization process (FFO). We have achieved a classification accuracy of 98.24% using our proposed approach. Convolutional Neural Network (CNN), Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Random Forest (RF), and Naive Bayes are compared with our proposed system (NB). The experimental results demonstrate that the classification accuracy of our suggested system is superior to that of the leading classification strategies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Radial basis function networks

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Deep Learning Based Autonomous Transport System for Secure Vehicle and Cargo Matching

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Intelligent Automation & Soft computing 2023年第1期35卷 957-969页

作者： T.Shanthi M.Ramprasath A.Kavitha T.Muruganantham Department of Electronics and Communication Engineering Sona College of TechnologySalem636005Tamil NaduIndia Department of Data Science and Business Systems School of ComputingSRM Institute of Science and TechnologyKattankulathur603203TamilnaduIndia Department of Electronics and Communication Engineering K.Ramakrishnan College of TechnologyTiruchirapalli621112TamilnaduIndia Department of Electronics and Communication Engineering K.Ramakrishnan College of EngineeringTiruchirapalli621112TamilnaduIndia

The latest 6G improvements secured autonomous driving's realism in Intelligent Autonomous Transport Systems(IATS).Despite the IATS's benefits,security remains a significant *** technology has grown in popularity as a means of implementing safe,dependable,and decentralised independent IATS systems,allowing for more utilisation of legacy IATS infrastructures and resources,which is especially advantageous for crowdsourcing *** technology can be used to address security concerns in the IATS and to aid in logistics *** light of the inadequacy of reliance and inattention to rights created by centralised and conventional logistics systems,this paper discusses the creation of a blockchain-based IATS powered by deep learning for secure cargo and vehicle matching(BDL-IATS).The BDL-IATS approach utilises Ethereum as the primary blockchain for storing private data such as order and shipment ***,the deep belief network(DBN)model is used to select suitable vehicles and goods for ***,the chaotic krill herd technique is used to tune the DBN model’s *** performance of the BDL-IATS technique is validated,and the findings are inspected under a variety of *** simulationfindings indicated that the BDL-IATS strategy outperformed recent state-of-the-art approaches.

关键词： Blockchain ethereum intelligent autonomous transport system security deep belief network

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Dual-Functional Waveform Design for STAR-RIS Aided ISAC via Deep Reinforcement Learning 35

Dual-Functional Waveform Design for STAR-RIS Aided ISAC via ...

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35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Zhang, Jifa Gong, Shiqi Lu, Weidang Xing, Chengwen Zhao, Nan Ng, Derrick Wing Kwan Niyato, Dusit Dalian University of Technology School of Information and Communication Engineering Dalian China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing China Zhejiang University of Technology College of Information Engineering Hangzhou China Beijing Institute of Technology School of Information and Electronics Beijing China University of New South Wales School of Electrical Engineering and Telecommunications Sydney Australia Nanyang Technological University College of Computing and Data Science Singapore

ISBN: (纸本)9798350362244

Integrated sensing and communication (ISAC) technology effectively enables spectrum and hardware sharing between radar and communication. This paper investigates the dual-functional (DF) constant modulus waveform design for simultaneously transmitting and reconfigurable intelligent surface (STAR-RIS)-aided ISAC, in which the channel information can be used as semantic information. To investigate the performance trade-off, the weighted sum of multi-user interference (MUI) energy and waveform discrepancies is minimized via jointly optimizing the transmit waveform and the reflection and transmission coefficient matrices at STAR-RIS. Furthermore, a practical case of coupled phase shifts at STARRIS is investigated. We first formulate the optimization problem as a Markov decision process, employing a twin delayed deep deterministic policy gradient (TD3)-based deep reinforcement learning approach to address it. Simulation results verify the effectiveness of the proposed scheme. © 2024 IEEE.

关键词： Deep reinforcement learning

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A Novel CNN-Based IoT System Architecture for Real-Time Detection and Prevention of Animal Intrusion in Farmland 4

A Novel CNN-Based IoT System Architecture for Real-Time Dete...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Ashokkumar, C. Kumar, M. Arun Krishnan, R. Santhana Priya, S. Mohanap Narayanan, K. Lakshmi Julie, E. Golden School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computing Technologies Chennai India PSNA College of Engineering and Technology Department of Information Technology Dindigul India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Scad Polytechnic College Department of Computer Science and Engineering Cheranmahadevi India Francis Xavier Engineering College Department of Electronics and Communication Engineering Tirunelveli India Anna University Regional Campus Department of Computer Science and Engineering Tirunelveli India

ISBN: (纸本)9798350300888

Animal intrusion is a significant challenge for farmers, causing extensive crop damage, human injuries, and substantial financial losses. Traditional animal movement monitoring and surveillance methods alone are insufficient to provide a permanent solution. To solve this problem, a novel system architecture that combines the latest convolutional neural network (CNN) algorithm and Internet of Things (IoT) technology is proposed. Our proposed system architecture integrates various components, including a Raspberry Pi as a central processing unit, cloud storage for efficient data management, and a GSM module for instant alert generation. To train the CNN algorithm, a comprehensive and diverse Animal Dataset consisting of various animal species commonly found in farmland areas is curated. The dataset encompasses a wide range of annotated images, enabling the CNN algorithm to accurately identify and classify animals. The Raspberry Pi serves as the core of the system, responsible for real-time image processing and analysis. Utilizing the power of the CNN algorithm, the Raspberry Pi processes the captured images from strategically placed surveillance cameras. When an animal intrusion is detected, the system promptly generates an alert via the integrated GSM module, providing immediate notifications to farmers and relevant authorities. Furthermore, the system leverages cloud storage to store and manage the collected data, facilitating easy access and retrieval for analysis and system improvement. This cloud-based approach enables scalability, allowing the system to handle large amounts of data efficiently. By integrating the CNN algorithm, IoT, Raspberry Pi, cloud storage, and GSM module, a comprehensive and robust framework for real-time detection and prevention of animal intrusion is provided. The system's ability to swiftly identify and alert farmers and authorities about potential threats minimizes crop damage, ensures human safety, and significantly reduces financial l

关键词： Signal detection

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Advances in Facial Emotion Recognition: Deep Learning Approaches and Future Prospects

Advances in Facial Emotion Recognition: Deep Learning Approa...

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2024 MIT Art, Design and technology school of computing International Conference, MITADTSoCiCon 2024

作者： Deshmukh, Shrikala Chaudhary, Snehal Gayakwad, Milind Kadam, Kalyani More, Nitin S. Bhosale, Amol Amity University Mumbai India College of Engineering Department of Information Technology Pune India College of Engineering Pune India Symbiosis Institute of Technology Pune India Pune India MIT Art Design and Technology University School of Computing Department of CSE Pune India

ISBN: (纸本)9798350362879

Facial Emotion Recognition (FER) has evolved significantly with the advent of deep learning techniques, offering novel approaches to interpret and process facial expressions. This paper reviews recent advancements in FER, emphasizing the role of deep neural network architectures and their application across diverse fields including security, health, and human-machine interfaces. We analyze various methodologies, technological advancements, challenges, and future trends in FER, providing insights into the current state-of-The-Art. We conclude by discussing the limitations of current FER systems and propose future directions for research, particularly in extending emotion recognition beyond basic emotions and incorporating multimodal approaches. © 2024 IEEE.

关键词： Emotion Recognition

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Cursive Hand writing Recognition Using Neural Networks with VGG-16 2

Cursive Hand writing Recognition Using Neural Networks with ...

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2nd IEEE International Conference on Advances in computing, Communication and Applied Informatics, ACCAI 2023

作者： Rangari, Anushka Anil Das, Swagat Rajeswari, D. College of Engineering and Technology Srm Institute of Science and Technology School of Computing Department of Data Science and Business Systems Tamilnadu Kattankulathur603203 India

ISBN: (纸本)9798350315905

Recognition of cursive handwritten text from photographs is a method for finding handwritten text. Identification is difficult because every author has a distinctive writing style. The proposed work uses cutting-edge technologies including RNN, KNN, MLP, and CRNN twith VGG16 to push the boundaries of character and handwriting recognition. Additionally, it compares neural networks to CNNwith VGG16 and finds that neural networks are more accurate. In the first stage, image acquisition, the scanned image is acquired along with its normalization, feature extraction, and segmentation. Additionally, it combines with the most effective feature extraction technique to improve the accuracy of our model. It also compares the accuracy with six uniquepretreat. approach, which are image segmentation image data generator data augmentation closed loop pre-processing, sigmoid Stretching and geometric *** provides the highest accuracy (98.36%) when using data augmentation. CNN has the highest accuracy (95.25%) when using picture segmentation. The third experiment's results for the image data generator show that CNN produced the best results (92.47%), while the highest model, RNN, gave closed loop processing accuracy of 92.20%. a change in geometry KNN has the highest accuracy at 95.39%. © 2023 IEEE.

关键词： Image segmentation

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Multi-Criteria Decision-Making for Energy Management in Smart Homes Using Hybridized Neuro-Fuzzy Approach

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Distributed Generation and Alternative Energy Journal 2024年第1期39卷 83-109页

作者： Anbazhagu, U.V. Koti, Manjula Sanjay Muthukumaran, V. Geetha, V. Munrathnam, Meram Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology SRM Nagar Kattankulathur TN Chennai India Department of MCA Dayananda Sagar Academy of Technology and Management Bangalore560082 India Department of Mathematics College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur Campus 603 203 India Department of Computer Science School of Applied Sciences REVA University Bangalore India Department of Mathematics Rajiv Gandhi University of Knowledge Technologies R.k.Valley Idupulapaya Andhra Pradesh Kadapa516330 India

The necessity for smart energy oversight solutions has arisen in response to the rising popularity of energy-efficient home automation and other energy-saving technologies. Optimizing smart home energy use using multi-criteria decision-making (MCDM) is a proven methodology. However, the procedure for making decisions and MCDM’s capacity to handle various criteria are typically limiting factors. Hybrid methods, which integrate multiple decision-making approaches like Fuzzy Logic (FL) and Modular Neural Networks (MNN), could potentially be able to circumvent these restrictions and boost energy management systems’ efficacy and precision. This investigation presents a hybrid Neuro-Fuzzy (H-NF) method for MCDM in regulating energy for smart homes by combining FL with an MNN. The suggested approach would optimize energy use in smart homes by considering several parameters, notably cost, ease of use, and environmental effects. In addition, this study aims to examine how the H-NF model fares in comparison to other methods of making important decisions in terms of several performance metrics. The suggested hybridized approach has the potential to deliver more precise and effective decision-making processes for energy management in smart homes, allowing users to optimize their energy consumption while preserving comfort and lowering environmental impact. © 2024 Taylor and Francis Inc.. All rights reserved.

关键词： Decision making

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A low-carbon economic dispatch model for electricity market with wind power based on improved ant-lion optimisation algorithm

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CAAI Transactions on Intelligence technology 2023年第1期8卷 29-39页

作者： Renwu Yan Yihan Lin Ning Yu Yi Wu School of Electronic Electrical Engineering and PhysicsFujian University of TechnologyFuzhouChina Department of Computing Sciences College at BrockportState University of New YorkBrockportNew YorkUSA Department of Human Resource Fuzhou Technology and Business UniversityFuzhouChina

Introducing carbon trading into electricity market can convert carbon dioxide into schedulable resources with economic ***,the randomness of wind power generation puts forward higher requirements for electricity market ***,the carbon trading market is introduced into the wind power market,and a new form of low-carbon economic dispatch model is ***,the economic dispatch goal of wind power is be *** is projected to save money and reduce the cost of power generation for the *** model includes risk operating costs to account for the impact of wind power output variability on the system,as well as wind farm negative efficiency operating costs to account for the loss caused by wind *** model also employs carbon trading market metrics to achieve the goal of lowering system carbon emissions,and analyze the impact of different carbon trading prices on the system.A low-carbon economic dispatch model for the wind power market is implemented based on the following two ***,the solution is optimised using the Ant-lion optimisation method,which combines Levi's flight mechanism and golden *** proposed model and algorithm's rationality is proven through the use of cases.

关键词： ant-lion optimisation algorithm carbon trading Levi flight low-carbon economic dispatch wind power market

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Facial Recognition and Manual Verification Automation Device for polling booths (DigiVoter) 12th

Facial Recognition and Manual Verification Automation Device...

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12th International Conference on Recent Trends in computing, ICRTC 2024

作者： Sudhakaran, Pradeep Rekha, V. Sindhu, V. Preetha, M. Deluxni, N. Department of Computing Technologies School of Computing SRM Institute of Science and Technology Tamil Nadu Kattankulathur India Agurchand Manmull Jain College Tamil Nadu Chennai India Department of Information Technology Sri Krishna College of Engineering and Technology Tamilnadu Coimbatore India Career Development Centre SRM Institute of Science and Technology Tamil Nadu Kattankulathur India

ISBN: (纸本)9789819789450

In democratic societies, the efficiency and integrity of the voting process are paramount. The DigiVoter system presents an innovative approach to addressing the longstanding challenges of voter verification at polling booths, which have been identified as significant barriers to voter turnout and operational efficiency. By integrating facial recognition technology powered by OpenCV with a blockchain-based data storage solution, DigiVoter aims to automate and secure the voter verification process, thereby reducing queue times from the current average of 7 min to under 2 min. This paper explores the development and implementation of DigiVoter, emphasizing its potential to streamline the electoral process, enhance data security, and increase voter participation. The use of OpenCV allows for accurate and swift voter identification, while the blockchain component ensures the integrity and immutability of voting records, addressing concerns about data tampering and privacy. Preliminary results indicate that DigiVoter can significantly improve the voting experience, suggesting a promising avenue for leveraging technology to support democratic engagement. Through this exploration, the paper highlights the critical role of innovative technologies in overcoming obstacles to voting, with the ultimate goal of fostering a more inclusive and efficient electoral system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Blockchain

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Performance Evaluation of Biometric Authentication Using Fragment Jaya Optimizer-Based Deep CNN with Multi-kernel SVM

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SN Computer Science 2024年第4期5卷 337页

作者： Umasankari, N. Muthukumar, B. Shanmuganathan, C. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India School of Computing Department of CSE Sathyabama Institute of Science and Technology Chennai India Rajalakshmi Engineering College Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India

The earlier research clearly indicated that the bimodal authentication system has more efficiency than unimodal and multimodal. This is due to the reason for the best intact biometric traits of fingerprint and retina. There is a chance to additionally improve further performance of the proposed biometric trait combination by additional or alternate algorithms or methodologies. Therefore, in this research work, the multi-kernel support vector machine (MK-SVM), a machine learning classification approach, is proposed and is used for the implementation. In addition, a hybrid algorithm of fragment Jaya optimizer-based deep convolutional neural network (FJO-DCNN) approach is also used to improve the performance value for bimodal biometric authentication and classification. The recognition systems analyze both biometrics independently, and their conclusions are combined to determine whether to give or refuse access to the user in the end. According to the findings of the implementation, this work demonstrates more dependability than the cascaded biometric system. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Bimodal biometric authentication Deep convolutional neural network Fragment Jaya optimizer convolutional neural network Multi-kernel support vector machine

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