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

作者： Saravanan, S. Raj, J. Relin Francis Rajkumar, G. Vinoth Kumar, C. Antony Vasantha Soundiraraj, N. Bharathi, K. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Electronics and Communication Engineering J.P. College of Engineering Tenkasi India Department of Mechanical Engineering Rohini College of Engineering and Technology Kanyakumari India Department of Electrical and Electronics Engineering PSNA College of Engineering and Technology Dindigul India Department of Computer Science and Engineering SCAD College of Engineering and Technology Cheranmahadevi India

ISBN: (纸本)9798331532420

This study introduces a real-time tiger monitoring system built on the Faster R-CNN deep learning framework, designed to support wildlife conservation through accurate tiger detection and tracking. The primary aim is to effectively identify and monitor tigers across various environments, using an extensive dataset drawn from Google’s Open Images Dataset and further enriched with synthetic images created by Generative Adversarial Networks (GANs). These synthetic images simulate challenging conditions, such as low lighting and uncommon camera angles, to improve the model’s robustness and adaptability in field settings. The model is initialized with a pre-trained ResNet50 backbone, utilizing transfer learning to accelerate training while ensuring precise detection. Advanced techniques such as dynamic data augmentation and hard negative mining have been integrated to minimize false positives and increase model resilience. Hyperparameter tuning, including cyclical learning rate scheduling and anchor box optimization, is applied to maximize both accuracy and recall, refining the model's ability to capture tiger-specific features under diverse conditions. To facilitate real-time monitoring, the model is deployed on NVIDIA Jetson devices, supporting edge processing without relying on cloud-based infrastructure. This deployment strategy ensures efficient data processing in remote areas, where stable network connectivity may be limited. Non-Maximum Suppression is used to reduce duplicate detections, while a Kalman Filter maintains tracking stability across video frames, ensuring seamless observation of tigers in motion. This optimized Faster R-CNN-based approach enables high-accuracy, low-latency tiger detection, equipping conservationists with reliable data on tiger presence and movement patterns. By enhancing detection and tracking capabilities, this system aims to support informed decision-making in wildlife conservation and habitat management. © 2024 IEEE.

关键词： Decision making

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Machine Learning Vigilance: Safeguarding Networks Through Cyber Attack Detection 12th

Machine Learning Vigilance: Safeguarding Networks Through Cy...

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12th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： Sangeam, Ruchitha Ala, Rajitha Punyamurthy, Bheemeshwar Kanagala, Sai Sriharsha Kesari Swain, Nirmal Swetha, Rame Department of Information Technology Vardhaman College of Engineering Hyderabad India Department of Computer Science and Engineering Vardhaman College of Engineering Hyderbad India

ISBN: (纸本)9789819601424

Embracing the latest technological strides holds immense promise for individuals, organizations, and governments. However, divergent perspectives exist on these advancements, particularly regarding safeguarding critical information, securing stored data platforms, and ensuring data accessibility. A pivotal challenge in the contemporary landscape is the escalating threat of digital terror ism, causing disruptions for individuals and institutions. This form of cybercrime, orchestrated by diverse entities like criminal organizations, skilled individuals, and digital activists, now poses a significant risk to public and national security. Intrusion Detection Systems (IDS) have been developed to address this pressing issue. The study leverages the recently introduced UNSWNB dataset to evaluate the efficacy of the chosen approach. The SVM algorithm demonstrated a commendable accuracy of 83.29. The company demonstrated its ability to effectively counter cyber threats by showcasing its potential. Moreover, they utilized a convolutional neural network (CNN) to achieve this feature outperformed, and achieved an accuracy level of 99.52 with your cybersecurity project. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network intrusion

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An Efficient Deep Learning Approach for Food Quality Detection 3

An Efficient Deep Learning Approach for Food Quality Detecti...

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3rd IEEE International Conference on Industrial Electronics: Developments and Applications, ICIDeA 2025

作者： Sekar, K. Yuvarajan, T. Tejashwini, J.S. Dheepa, T. Rajalakshmi Engineering College Department of AIML Chennai India Kongunadu College of Engineering and Technology Department of Computer Science and Engineering Tiruchirappalli India

ISBN: (纸本)9798331533205

This research aims to develop a deep learning system for assessing food edibility based on user-uploaded photos, addressing the global challenge of food waste. The system leverages the YOLOv8 model, renowned for its efficiency in object detection and classification, to evaluate visual attributes such as color changes, mold, and texture abnormalities, determining whether food is fresh or spoiled. With its advanced detection capabilities, YOLOv8 is well-suited for real-time classification, ensuring quick and accurate identification of spoilage across diverse food types and varying lighting conditions. Unlike traditional systems that often struggle with accuracy, especially when dealing with a wide variety of foods, this approach significantly enhances reliability. By enabling individuals to identify unsafe or spoiled food before consumption, this project contributes to reducing waste and promoting sustainable food practices while improving food safety. © 2025 IEEE.

关键词： Deep learning

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An Intelligent Analysis of Mobile Evidence Using Sentimental Analysis

An Intelligent Analysis of Mobile Evidence Using Sentimental...

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International Conference on Information Security, Privacy, and Digital Forensics, 2022

作者： Jones, G. Maria Santhiya, P. Winster, S. Godfrey Sundar, R. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology and Panimalar Engineering College Chennai India

ISBN: (纸本)9789819950904

Smartphones are compatible and easily accessible compared to computers irrespective of place and time. Smartphones merge with our routine which acts as a medium of communication in several ways such as messaging, voice and video calling, sharing of audio and video contact, and many multimedia contents. We express our emotions through words and these communications can be analyzed by a technique called Natural Language Processing (NLP). Many researchers in the field of text mining and NLP have analyzed the polarity of online product reviews, blogs, social media comments, tweets, and many more. Mobile forensics investigation will be more effective by providing the evidences based on sentimental analysis of the text where the suspected messages contain a negative content of having threats, harassments, text against the community guidelines, and some keywords related to the cases which may be the turning point during the forensics investigation. In this paper, a novel framework called Integrated Mobile Device Forensic Investigation Process (IMDFIP) with inclusion of sentimental analysis techniques in the text which is used to emotionally analyze the texts during the communication process. This framework provides additional information to forensic analysts which will trap the suspect during investigation. The proposed framework of IMDFIP identifies the important terms, emotions, and polarity of the text during the crime. IMDFIP capitulates results compared with the previous models. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sentiment analysis

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Liver Tumors Segmentation Using 3D SegNet Deep Learning Approach

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computer Systems science & engineering 2023年第5期45卷 1655-1677页

作者： G.Nallasivan V.Ramachandran Roobaea Alroobaea Jasem Almotiri Department of Computer Science and Engineering PSN College of Engineering and TechnologyTirunelveli627152Tamil NaduIndia Department of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21974Saudi Arabia

An ultrasonic filter detects signs of malignant tumors by analysing the image’s pixel quality fluctuations caused by a liver *** of malignant growth proximity are identified in an ultrasound filter through image pixel quality variations from a liver’s *** changes are more common in alcoholic liver conditions than in other etiologies of cirrhosis,suggesting that the cause may be alcohol instead of liver *** Two-Dimensional(2D)ultrasound data sets contain an accuracy rate of 85.9%and a 2D Computed Tomography(CT)data set of 91.02%.The most recent work on designing a Three-Dimensional(3D)ultrasound imaging system in or close to real-time is *** this article,a Deep Learning(DL)model is implemented and modified to fit liver CT segmentation,and a semantic pixel classification of road scenes is *** architecture is called semantic pixel-wise segmentation and comprises a hierarchical link of encoder-decoder layers.A standard data set was used to test the proposed model for liver CT scans and the tumor accuracy in the training *** the normal class,we obtained 100%precision for chronic cirrhosis hepatitis(73%),offset cirrhosis(59.26%),and offensive cirrhosis(91.67%)for chronic hepatitis or cirrhosis(73,0%).The aim is to develop a computer-Aided Detection(CAD)screening tool to detect *** results proved 98.33%exactness,94.59%sensitivity,and 92.11%case with Convolutional Neural Networks(CNN)*** the classifier’s performance did not differentiate so clearly at this level,it was recommended that CNN generally perform better due to the good relationship between Area under the Receiver Operating Characteristics Curve(AUC)and accuracy.

关键词： Deep learning liver tumor CNN CT accuracy

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Skin lesion classification using transfer learning

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Soft Computing 2024年第20期28卷 12337-12343页

作者： Nivedhitha, G. Kalpana, P. Sidthik, A. Sheik Rani, V. Anusha Singh, Ajith B. Rajagopal, R. Department of Computer Science and Engineering Sri Krishna College of Technology Tamil Nadu Coimbatore India Department of Electrical and Electronics Engineering VSB College of Engineering and Technical Campus Tamilnadu Coimbatore642109 India Department of Electronics and Instrumentation Engineering Saveetha Engineering College Tamilnadu Chennai India Department of Electrical and Electronics Engineering Sri Krishna College of Technology Tamil Nadu Coimbatore India Department of Electrical and Electronics Engineering Francis Xavier Engineering College Tirunelveli India

This work presents an essential module for the Transfer Learning approach's classification of melanoma skin lesions. Melanoma, a highly lethal form of skin cancer, poses a significant health threat globally. Image analysis plays a crucial role in enhancing the accuracy of malignant skin lesion classification. Although neural networks trained on extensive datasets have emerged as the latest solution, their scalability remains a challenge. This study proposes an efficient method for classifying skin lesions utilizing labelled data from open sources, leveraging EfficientNet as the foundational model to robustly capture discriminative features from diverse visual perspectives. Validation of the proposed algorithms relies on the classifier's capacity to distinguish between classes is measured by the Area Under the Receiver Operating Characteristic (AUC-ROC) curve. AUC-ROC score greater than zero denotes better classification performance. Our proposed model achieves an impressive score of 98.65%. In contrast to existing approaches, our method demonstrates swift and accurate identification and segmentation of melanoma skin lesions, showcasing its efficacy in advancing the field of skin lesion classification. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Dermatology

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Environmental water quality prediction based on COOT-CSO-LSTM deep learning

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Environmental science and Pollution Research 2024年第42期31卷 54525-54533页

作者： Rajagopal, Sankarasubbu Ganesh, Sundaram Sankar Karthick, Alagar Sampradeepraj, Thangavel Department of Information Technology National Engineering College Tamilnadu Kovilpatti628503 India Department of Computer Science and Engineering R.M.K. College of Engineering and Technology Puduvoyal Tamilnadu Thiruvallur601206 India Renewable Energy Lab Department of Electrical and Electronics Engineering KPR Institute of Engineering and Technology Tamilnadu Coimbatore641407 India Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu Virudhunagar626126 India

Water resource management relies heavily on reliable water quality predictions. Predicting water quality metrics in the watershed system, including dissolved oxygen (DO), is the main emphasis of this work. The enhanced long short-term memory (LSTM) model was suggested to improve the model’s performance. Additionally, a hybrid model was employed to calculate the ideal parameter values for the LSTM model, which helped overcome the nonstationarity, unpredictability, and nonlinearity of the data about the water quality parameters. This model recruited the COOT method. The original weekly water quality values at the Vaigai River, Madurai, Tamil Nadu, India, were tested using the suggested hybrid model. An independent LSTM, the hybrid optimisation method takes its cues from the cuckoo bird’s reproductive strategy and a novel meta-heuristic optimisation technique dubbed COOT, which is based on the behaviour of a flock of coot birds. If implemented, the suggested hybrid model might serve as an alternate framework for water quality prediction, laying the groundwork for basin-wide efforts to manage water quality and control pollutants. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Macroinvertebrates

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A Novel Energy and Communication Aware Scheduling on Green Cloud Computing

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computers, Materials & Continua 2023年第12期77卷 2791-2811页

作者： Laila Almutairi Shabnam Mohamed Aslam Department of Computer Engineering Computer Science and Information Technology CollegeMajmaah UniversityAl Majmaah11952Saudi Arabia Department of Information Technology Computer Science and Information Technology CollegeMajmaah UniversityAl Majmaah11952Saudi Arabia

The rapid growth of service-oriented and cloud computing has created large-scale data centres *** data centres’operating costs mostly come from back-end cloud infrastructure and energy *** cloud computing,extensive communication resources are ***,cloud applications require more bandwidth to transfer large amounts of data to satisfy end-user *** is also essential that no communication source can cause congestion or bag loss owing to unnecessary switching *** paper proposes a novel Energy and Communication(EC)aware scheduling(EC-scheduler)algorithm for green cloud computing,which optimizes data centre energy consumption and traffic *** primary goal of the proposed EC-scheduler is to assign user applications to cloud data centre resources with minimal utilization of data *** first introduce a Multi-Objective Leader Salp Swarm(MLSS)algorithm for task sorting,which ensures traffic load balancing,and then an Emotional Artificial Neural Network(EANN)for efficient resource ***-scheduler schedules cloud user requirements to the cloud server by optimizing both energy and communication delay,which supports the lower emission of carbon dioxide by the cloud server system,enabling a green,unalloyed *** tested the proposed plan and existing cloud scheduling methods using the GreenCloud simulator to analyze the efficiency of optimizing data centre energy and other scheduler *** EC-scheduler parameters Power Usage Effectiveness(PUE),Data Centre Energy Productivity(DCEP),Throughput,Average Execution Time(AET),Energy Consumption,and Makespan showed up to 26.738%,37.59%,50%,4.34%,34.2%,and 33.54%higher efficiency,respectively,than existing state of the art schedulers concerning number of user applications and number of user requests.

关键词： EC-scheduler green cloud energy efficiency task scheduling task sorting resource allocation

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Marine Life Ecosystem Analysis Based on Climate Change Detection Using Deep Learning Algorithms

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Remote Sensing in Earth Systems sciences 2025年第2期8卷 545-554页

作者： Rebecca, B. Sandhya, A. Pokkuluri, Kiran Sree Kanishka, Gunipati Shaik, Khasimbee Krishna, B. Murali Department of Computer Science and Engineering Marri Laxman Reddy Institute of Technology and Management Telangana Dundigal 500043 India Department of CSE SRM Institute of Science and Technology Chennai Ramapuram India Department of Computer Science and Engineering Shri Vishnu Engineering College for Women Andhra Pradesh Bhimavaram India Computer Science and Engineering (Cyber Security) Madanapalle Institute of Technology & Science Madanapalle India Department of Computer Science and Engineering Aditya University Andhra Pradesh Surampalem India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

The surrounding environmental and climatic conditions have a significant impact on the utilisation of ecosystem services for recreational purposes. Climate change poses a threat to future natural leisure opportunities because of this reliance. The way society functions is being challenged by climate change and significant adaptation will probably be needed to deal with future changes in weather patterns. Machine learning (ML) methods have advanced to the point that they are now being offered as a tool to support climate studies, in addition to being the source of advances in other fields of study. This research proposes a novel technique in marine life ecosystem–based climate change detection using deep learning model. Input is collected as climate change detection–based marine life ecosystem and processed for noise removal with normalisation. Then this data has been segmented and classified using watershed semantic fuzzy component analysis with quantile shrinkage convolutional regression model. The experimental analysis has been carried out in terms of training accuracy, specificity, recall, AUC and NSE. Suggested technique achieved training accuracy 98%, specificity 96%, NSE 45%, recall 95% and AUC 97%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Climate change detection Convolutional regression Deep learning Fuzzy component analysis Marine life ecosystem

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Hybrid metaheuristic optimization algorithm for prediction of fatigue life performance of fiber-reinforced concrete

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Asian Journal of Civil engineering 2025年 1-12页

作者： Padwad, Hirkani Puri, Nischal Sahare, Sneha A. Gote, Ashwini C. Patil, Tejas R. Kakade, Niteen T. Department of Computer Science and Engineering and Emerging Technologies Shri Ramdeobaba College of Engineering and Management Ramdeobaba University Maharashtra Nagpur 440013 India Department of Computer Science and Engineering Jhulelal Institute of Technology Maharashtra Nagpur 441111 India Department of Computer Technology Yeshwantrao Chavan College of Engineering Maharashtra Nagpur 441110 India Department of Civil Engineering Priyadarshini Bhagwati College of Engineering Maharashtra Nagpur 440024 India Department of Mechanical Engineering Tulsiramji Gaikwad Patil College of Engineering and Technology Maharashtra Nagpur 441108 India

This paper deals with an integrated and multi-scale hybrid intelligence framework for optimizing fiber orientation, improving crack control, and predicting the fatigue life of FRC. At the core of such model construction is a hybrid evolutionary-physics informed neural network (HE-PINN), which employs evolutionary optimization and physics Informed learning to refine fiber orientation on the basis of outcome material properties and cyclic loading parameters in process, thus ensuring physical prediction of fiber-matrix interaction under stress redistribution. Along with this construction, the adaptive fractal dimension based metaheuristic is introduced to understand and monitor real-time image-based crack evolution in real-time;this will use fractal geometry to control the microcrack growth toward localization while dissipating energy within concrete-satisfied limits. the stress wave propagation-based adaptive swarm optimization (SWP-ASO) constructs a fusion of wavelet-transformed stress features that come from finite element simulations for load redistribution optimization function of fiber placements. It thus advances a data-driven generative designing scheme eliminating the human bias and training itself to learn high-performance reinforcement layouts through the generative adversarial network for fiber network optimization (GAN-FNO). Finally, an adaptive Bayesian-Gaussian process regression (AB-GPR) module provides real-time fatigue life prediction with uncertainty quantification and an adaptive-learning process. This combined architecture therefore provides an improvement of between 30 and 40% in fatigue life, an increase of up to 50% in energy absorption, and a reduction of up to 35% in the rates of crack propagation, presenting a considerable advancement in predictive and prescriptive modeling of smart FRC designs. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Crack propagation control Fatigue life prediction Fiber-reinforced concrete Multi-scale hybrid framework Physics informed neural networks

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