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3rd International Conference on Automation, computing and Renewable Systems, ICACRS 2024

作者： Koradaa, Sampath Kumar Potnuru, Syam Virat Gupta AshokKumar, C. Kumar, C. Jothi Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9798331532420

This research introduces a cutting-edge framework leveraging advanced AI and IoT technologies to revolutionize lion conservation. The primary focus is on real-time lion detection, individual identification using unique facial features, gender classification, and health condition assessment, including malnutrition, injuries, and illnesses. By integrating GPS modules and IoT sensors, the system facilitates noninvasive and ethical monitoring of lions and their habitats. The study employs YOLO (You Only Look Once) for real-time detection of lions in video feeds and EfficientNet B7 for multitask classification, enabling precise identification of individual lions and their health status. DeepSORT is utilized for tracking lion movements over time, ensuring continuous observation and territorial mapping. These models are deployed on edge devices, such as drones and camera traps, to deliver instantaneous results while minimizing latency. The proposed system offers numerous advantages, including improved conservation efforts through real-time alerts and actionable insights. Its ability to monitor health conditions and movement patterns enables proactive interventions to safeguard lion populations. Moreover, the integration of IoT sensors ensures comprehensive environmental monitoring, supporting a holistic understanding of the ecosystem. By employing cloud platforms for data analytics and storage, the system provides accessible dashboards and detailed reports, empowering conservationists and wildlife authorities with valuable insights. This framework not only enhances the efficiency of lion conservation but also ensures ethical practices, setting a benchmark for integrating AI and IoT in wildlife preservation. The research underscores the potential of technology to address critical ecological challenges and promote sustainable conservation efforts. © 2024 IEEE.

关键词： Mammals

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Image Caption Generation with Python: A Deep Learning-Based Approach 5

Image Caption Generation with Python: A Deep Learning-Based ...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Krishna, Siram Chaitanya Reddy, Painti Nagi Kirubanantham, P. School of Computing SRM Institute of Science and Technology Kattankulathur Department of Computing Technologies Chennai India School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Faculty of Engineering and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331518097

Today, the day-to-day generation of such a large amount of content on social media and other websites makes it impossible to maintain each image manually, so the need for automatically identifying sensitive images and filtering them out becomes inevitable. This project proposes a sensitive image classification application using automatic captions in identifying and filtering inappropriate content. Thus, the information extracted from images by ResNet50 in the caption generation process is allowed to integrate such features with a standard model through Senti-Long Short-Term Memory networks (SLSTM) for better captioning. Deep learning models bring about drastically improved quality of captions along with contextual understanding. The captions are tested rigorously with training loss metrics, BLEU scores, and sentiment analysis through the VADER Sentiment Analyzer. This system efficiently categorizes images involving violence, negativity, or dangerous situations into the class of negative. A Flask-based web application was developed for implementing a scalable and user-friendly interface, ensuring efficient content moderation. This project has illustrated the possibility of integrating deep learning techniques with natural language processing to yield beneficial content moderation, further ensuring safe and controlled online environments. © 2024 IEEE.

关键词： Long short-term memory

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Detection of Alzheimer’s Disease from Brain MRI Images Using Convolutional Neural Network 6th

Detection of Alzheimer’s Disease from Brain MRI Images Usin...

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6th International conference on communication and computational technologies, ICCCT 2024

作者： Santosh, Nomula Khan, Patan Imran Saranya, P. Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9789819774227

Alzheimer's disease (AD) ranks as the predominant factor leading to the onset of dementia. AD leads to a steady decline in memory, reasoning, behavior, and social abilities. These changes affect a person's personality. Alzheimer's disease is most common in older adults. Conventional diagnostics methods for Alzheimer's disease face challenges like invasiveness, cost, imprecision, and patient discomfort. The aim is to develop an innovative AI model offering a non-invasive, cost-effective solution. In this suggested system, we use a convolutional neural network to classify AD into four classes: Normal Phase, First Phase, Second Phase, and Third Phase. The dataset size is 6000 MRI (Magnetic Resonance Imaging) images. AD causes the brain regions to shrink, and connections between the network of neurons may break down. Hence, accurate and timely Alzheimer's disease classification is crucial for effective treatment and better patient outcomes. The primary goal of the suggested model is to classify AD with greater accuracy using a deep learning model. The work follows several preprocessing steps for the input images, such as image resizing, image pixel resizing, and batching images to enhance the input features, and for disease classification, the Convolutional Neural Network model is used. The suggested model was evaluated using Kaggle dataset images, and a maximum accuracy of 97.25% was obtained using the Kaggle dataset. This research addresses the critical need for precise AD classification, potentially revolutionizing patient outcomes through early detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Enhanced Fruit Fly Optimization Algorithm for Wrapper Based Feature Selection in Cancer Prediction 1

Enhanced Fruit Fly Optimization Algorithm for Wrapper Based ...

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1st International Conference on Cognitive, Green and Ubiquitous computing, IC-CGU 2024

作者： Saranya, P. Viji, D. Jangiti, Aditya School of Computing College of Engineering and Technology SRM Institute of Science and Technology Department of Computational Intelligence Tamil Nadu Chennai India School of Computing College of Engineering and Technology SRM Institute of Science and Technology Department of Computing Technologies Tamil Nadu Chennai India

ISBN: (纸本)9798350361780

Nowadays, smart healthcare appliances generate vast amount of medical data. Such huge amount of data requires classification methodologies, through which disease diagnostics can be carried out. Chronic diseases like cancer can be predicted in advance to help the patients to take treatment in initial stages of the disease. This article proposes an improvised feature selection and classification -method for cancer prediction. Here we propose an enhanced fruit fly optimization algorithm in KNN classifier for searching strategy with wrapper methods for feature selection. Lung cancer data set, breast cancer data set and leukemia data set has been taken from the UCI repository for testing the performance of the proposed model. For Lung cancer data set, accuracy, specificity, sensitivity, and fscore value observed are 96.97 %, 96.28 %, 96.35 %, and 96.34% respectively. For breast cancer dataset, accuracy, specificity, sensitivity, and fscore value observed are 99.97 %, 98.2 %, 96.85 %, and 90.35% respectively. For Breast cancer dataset, accuracy, specificity, sensitivity, and fscore value observed are 96.67%, 98.28 %, 95.85 %, and 93.85% respectively. The proposed method works well with various dataset and shows prominent accuracy improvement over the existing systems. © 2024 IEEE.

关键词： Optimization

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Deep Learning Based Attack Detection for Cyber-Physical System Cybersecurity:A Survey

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IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 377-391页

作者： Jun Zhang Lei Pan Qing-Long Han Chao Chen Sheng Wen Yang Xiang IEEE the School of Science Computing and Engineering TechnologiesSwinburne University of Technology the School of Information Technology Deakin University the College of Science and Engineering James CookUniversity

With the booming of cyber attacks and cyber criminals against cyber-physical systems(CPSs),detecting these attacks remains *** might be the worst of times,but it might be the best of times because of opportunities brought by machine learning(ML),in particular deep learning(DL).In general,DL delivers superior performance to ML because of its layered setting and its effective algorithm for extract useful information from training *** models are adopted quickly to cyber attacks against CPS *** this survey,a holistic view of recently proposed DL solutions is provided to cyber attack detection in the CPS context.A six-step DL driven methodology is provided to summarize and analyze the surveyed literature for applying DL methods to detect cyber attacks against CPS *** methodology includes CPS scenario analysis,cyber attack identification,ML problem formulation,DL model customization,data acquisition for training,and performance *** reviewed works indicate great potential to detect cyber attacks against CPS through DL ***,excellent performance is achieved partly because of several highquality datasets that are readily available for public ***,challenges,opportunities,and research trends are pointed out for future research.

关键词： Cyber-physical system cybersecurity deep learning intrusion detection pattern classification

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An Interval Multi-Objective Evolutionary Generation Algorithm for Product Design Change Plans in Uncertain Environments

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IEEE Transactions on Evolutionary Computation 2024年第3期29卷 1-1页

作者： Zheng, Rui-zhao Zhang, Yong Sun, Xiao-yan Gong, Dun-wei Gao, Xiao-zhi School of Information and Control Engineering China University of Mining and Technology Xuzhou China College of Automation and Electronic Engineering Qingdao University of Science and Technology Qingdao China School of Computing University of Eastern Finland Kuopio Finland

Design change is an important issue in complex product development projects. In a complex product with numerous parts (also known as components), the change of one key part may spread to other parts associated with it, generating a chain reaction throughout the entire project. Therefore, it is necessary to select a suitable change plan involving only fewer crucial parts in order to enhance the product’s performance, minimize change cost, and reduce change duration/time. Focusing on the case where the correlation strength between parts cannot be accurately obtained, in this paper we study an interval multi-objective evolutionary algorithm for finding excellent design change plans. Firstly, on the basis of the established multi-layer product network with interval correlation weights, an interval multi-objective optimization model of the product design change planning problem is established, where three new objective functions regarding product performance, carbon trading cost and supply risk are defined. Then, a constraint multi-objective evolutionary algorithm based on interval Pareto dominance is proposed to search for optimal change plans. Several novel operators, including the problem characteristic-guided population update strategy, the probability-based interval Pareto dominance, and the interval constraint handling strategy, are developed to enhance the algorithm’s performance. Finally, the proposed algorithm is compared with eight existing algorithms on the two design change cases, experimental results revealed its effectiveness. IEEE

关键词： Evolutionary algorithms

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A Game Theoretic Link Scheduler for Wireless Networks with Heterogeneous Energy Sources

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China Communications 2024年第12期21卷 108-125页

作者： He Tengjiao Kwanwu Chin Wang Yishun Sieteng Soh College of Information Science and Technology Jinan UniversityGuangzhouChina School of Electrical Computer and Telecommunications EngineeringUniversity of WollongongNSWAustralia School of Electrical Engineering Computing and Mathematical SciencesCurtin UniversityWAAustralia

This paper considers link scheduling in a wireless network comprising of two types of nodes:(i)hybrid access points(HAPs)that harvest solar en-ergy,and(ii)devices that harvest radio frequency(RF)energy whenever HAPs *** aim is to de-rive the shortest possible link schedule that determines the transmission time of inter-HAPs links,and uplinks from devices to *** first outline a mixed in-teger linear program(MILP),which can be run by a central node to determine the optimal schedule and transmit power of HAPs and *** then out-line a game theory based protocol called Distributed Schedule Minimization Protocol(DSMP)that is run by HAPs and ***,it does not require causal energy arrivals and channel gains *** results show that DSMP produces schedule lengths that are at most 1.99x longer than the schedule computed by MILP.

关键词： channel access finite battery mathemati-cal program renewable energy wireless power trans-fer

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Bridging the Semantic Gap: A Deep Learning Framework for Semantic Video Search Integrating Vision and Language for Enhanced Video Retrieval 10

Bridging the Semantic Gap: A Deep Learning Framework for Sem...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Annamalai, J. Arjun Sneha, S. Sharon Ashokkumar, C. Srm Institute of Science and Technology School of Computing College of Engineering and Technology Department of Computing Technologies Chennai Kattankulathur603203 India

ISBN: (纸本)9798350353068

Traditional video search engines often rely on tags or manual annotations for content retrieval, limiting the accuracy and efficiency of search results. Moreover, keyword-centric searches may not adeptly capture the nuanced and intricate queries users pose when seeking specific video content. The envisioned video search system integrates machine learning and natural language processing components to enable efficient and effective video retrieval based on user queries. The pipeline includes video processing employing Vision Transformer with GPT-2 (ViT-GPT2) architecture to analyze intricate details within video frames, Speech-to-Text (STT) models for transcribing spoken content, and the combination of transcriptions with video captions to form textual descriptions. These descriptions are then embedded using BERT, a transformer-based model, to grasp contextual relationships. Semantic matching is achieved through deep learning models, and a ranking mechanism based on similarity scores facilitates efficient retrieval. Leveraging datasets such as the MSR-VTT dataset, spanning visual recognition domains, contributes to the evaluation and training of the system's components. The system's efficacy is evaluated using metrics such as precision, recall, F1-score, and ranking metrics, with datasets spanning visual recognition, speech transcription, and semantic similarity domains. The comprehensive evaluation approach ensures the robustness and relevance of the video search system, aligning with user expectations and real-world use cases. © 2024 IEEE.

关键词： Semantics

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AI-Driven Insights: A Survey on Innovative Approach for Lung Cancer Prediction Utilizing Machine Learning and Deep Learning Methods 3

AI-Driven Insights: A Survey on Innovative Approach for Lung...

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3rd International Conference on Automation, computing and Renewable Systems, ICACRS 2024

作者： Bhargav, A. Lakshmi Ashokkumar, C. Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science & Technology Kattankulathur Chennai603203 India

ISBN: (纸本)9798331532420

The lungs are vital for exchanging oxygen and carbon dioxide, providing oxygen to cells for energy production, and removing waste carbon dioxide from the *** also help filter and protect the body from harmful particles and pathogens in the *** lung function is essential for overall health and *** cancer ranks as the second most common cause of cancer-related fatalities *** emphasizes the challenge of early detection due to the lack of symptoms in the initial *** contributes to a high mortality rate compared to other lung *** identification and precise prediction of lung cancer serves as vital for improving patient outcomes. Machine learning (ML) techniques have shown great promise in revolutionizing lung cancer prediction by leveraging various data sources such as medical images, clinical data, and genomic information. This paper presents a review of lung cancer prediction using ML, DL, and AI *** classification of lung cancer through medical imaging is essential for physicians to determine appropriate treatment options, ultimately aiming to reduce cancer *** goal is to compare the performance and accuracy of convolutional neural networks (CNNs), Random forest algorithm, ensemble extreme boosting (XGBoost) algorithm,Support Vector Machine (SVM), AdaBoost classification model (ADB-C), Long Short-Term Memory Networks (LSTM), incremental multiple resolution residual network (iMRRN) models while also providing explanations for their predictions. © 2024 IEEE.

关键词： Lung cancer

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Comprehensive Methods for Secure Medical Image Transfer: Encryption and Standardization Techniques

Comprehensive Methods for Secure Medical Image Transfer: Enc...

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2024 International Conference on Science, technology, engineering and Management, ICSTEM 2024

作者： Priyadharshini, K. Nandi, Hitesh Parmar, Harsh Dept. of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9798350376913

The primary goal of the process is to securely transfer medical images. This involves utilizing MRI datasets for processing and applying resizing and filtering techniques such as Conv2D and Gaussian blur to standardize the images. Additionally, an attack model is developed to enhance information security, starting with a PGD attack to access model weights and followed by a DAG attack to increase loss functions. Encryption is then performed using a Chaos-based encryption model to create a chaotic map and the Henon map for image conversion. A randomly generated key encryption method is utilized to generate a key for secure transfer. The encrypted image is displayed, and in the receiver section, the process is reversed for decryption by inputting the key generated in the encryption model. Finally, the original medical image is retrieved along with parameters such as MSE, MAE, RMSE, and F2 score to assess the accuracy of the recovered image and ensure its correctness. © 2024 IEEE.

关键词： Cryptography

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