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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Gowda, Dankan V. Kumar, Pullela S.V.V.S.R. Prasad, K.D.V. Ashreetha, B. Kumar, Mekala Bharath Karthikeya, Karanam Department of Electronics and Communication Engineering BMS Institute of Technology and Management Karnataka Bangalore India Department of Computer Science & Engineering Aditya College of Engineering Andhra Pradesh Surampalem India Symbiosis Institute of Business Management Hyderabad India Pune India Department of Electronics and Communication Engineering School of Engineering Mohan Babu University Andhra Pradesh Tirupati India Department of Electronics and Communication Engineering Sree Vidyanikethan Engineering College Andhra Pradesh Tirupati India

ISBN: (纸本)9798350361155

The difficulty in differentiating between the normal and cancerous cells in the brain through the frequent magnetic resonance imaging approaches is one of the major obstacles to realization of diagnostic precision. The presented work reveals a new MRI image processing technology, which includes an original software that contains complex algorithms and trained machine learning models, as the programs that make the images much better than before. Carefully calibrated computer vision dataset which features brain scans is subjected to the well-defined novel approach, whose performance is compared with the traditional MRI methods by calculating multiple metrics such as classification accuracy, sensitivity, specificity, ROC are and so on. This paper is the climax of a deep cognition of various cell types revealed by the newest MRI method which presents a better contrast than the old techniques. Next, the quality in the detection and recognition of the tumour after, comparison of these modalities displays that the modality of higher resolution, the ability to detect the tumour earlier and better. Such technological improvements in MRI machines will enable the surgeons to identify the growth of tumors at the early stages that will lay the right groundwork for the design of personalized treatment plans and also will have positive impact on the lives of the patients. Through this effect, the level of quality followed by MR imaging has been improved as well as arising new alliances between major imaging companies and machine learning technologies. It can be thought as the border - eraser diagnostics and imaging which will obey medical laws. Therefore, it states that the present hypothetical world should be improved while the advanced and proven diagnostics systems should be developed. The daily clinical applications of these advanced MRIs may well be the beginning of a new era in diagnostic oncology, which will be a very important way forward in improving treatment combined wi

关键词： Magnetic resonance imaging

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AI-Powered IoT Framework for Enhancing Building Safety through Stability Detection 8

AI-Powered IoT Framework for Enhancing Building Safety throu...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Raja, S.R. Devi, T. Rathi Raj, J. Relin Francis Sankar, V. Krishna Krishnan, R. Santhana Venkatalakshmi, R. Hindustan Institute of Technology and Science Computer Applications Chennai India Christ University Department of Computer Science and Engineering Bangalore India Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Saveetha School of Engineering Chennai India Scad College of Engineering and Technology Department of Civil Engineering Cheranmahadevi India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Psna College of Engineering and Technology Department of Information Technology Dindigul India

ISBN: (纸本)9798350376425

The rapid urbanization and increasing structural complexities of modern buildings have heightened the need for advanced monitoring systems to ensure building safety. The research presents an AI-powered IoT framework that enhances building safety through advanced stability detection mechanisms. The proposed framework employs a novel algorithm, Ensemble Learning with IoT Sensor Data Aggregation (EnIoT-SDA), which integrates ensemble learning techniques with aggregated sensor data to provide accurate and real-time stability assessments of building structures. The effectiveness of EnIoT-SDA was evaluated through a comprehensive simulation analysis, comparing its performance against existing algorithms, including Support Vector Machine (SVM), Gradient Boosting Machines (GBM), and Fuzzy Logic Systems (FLS). Simulation metrics, such as accuracy, false positive rate, computational time, and detection latency, were used to assess and compare the algorithms' performance. The results demonstrated that EnIoT-SDA outperformed the existing methods in several key areas, offering improved accuracy and reduced detection latency, thus establishing its potential as a robust solution for building safety monitoring. The study underscores the significant advancements brought by integrating ensemble learning with IoT sensor data and highlights areas for future research and development in this domain. © 2024 IEEE.

关键词： Support vector machines

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Advancing Neurological Health- A Collaborative Ensemble Learning Approach for Enhancing Early Diagnosis of Amyotrophic Lateral Sclerosis (ALS) 10

Advancing Neurological Health- A Collaborative Ensemble Lear...

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

作者： Srinivasan, Karthik Ravi, R. Durga Pavani, A. Mandadi, Sowmya Srivel, R. Saudi Electronic University Department of Information Technology Riyadh Saudi Arabia J. J College of Engineering and Technology Department of Computer Science and Engineering India Aurora's Technological and Research Institute Department of Computer Science and Engineering India Adhiparasakthi Engineering College Department of Computer Science and Engineering Melmaruvathur India

ISBN: (纸本)9798350353068

In this study, we propose the integration of Support Vector Machines (SVM) into an ensemble learning framework alongside K-Nearest Neighbors (K-NN) and Naive Bayes models to enhance the early diagnosis of Amyotrophic Lateral Sclerosis (ALS). ALS diagnosis is challenging due to its heterogeneous clinical manifestations. Leveraging diverse datasets including genetic data, medical imaging, and clinical records, we train SVM, K-NN, and Naive Bayes models independently. The ensemble approach amalgamates their predictions, harnessing the complementary strengths of each model. Through rigorous performance evaluation encompassing accuracy, precision, recall, and F1-score metrics, we demonstrate the superior diagnostic capabilities of the ensemble. Employing cross-validation ensures the robustness of our model, while fine-tuning hyperparameters optimizes its performance. Integration of SVM into the ensemble framework contributes additional discriminative power, enhancing the accuracy and reliability of ALS diagnosis. Our research underscores the pivotal role of ensemble learning in addressing the complexities of medical diagnosis, offering a notable advancement in ALS diagnostic methodologies. The proposed approach holds promise for improving patient care and setting new standards in clinical practice © 2024 IEEE.

关键词： Support vector machines

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Intelligent Deep Learning Based Cybersecurity Phishing Email Detection and Classification

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computers, Materials & Continua 2023年第3期74卷 5901-5914页

作者： R.Brindha S.Nandagopal H.Azath V.Sathana Gyanendra Prasad Joshi Sung Won Kim Department of Computing Technologies SRM Institute of Science and TechnologyKattankulathur603203India Department of Computing Science and Engineering Nandha College of TechnologyErode638052India School of Computing Science and Engineering VIT Bhopal UniversityBhopal466114India Department of Computer Science and Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-si38541Gyeongbuk-doKorea

Phishing is a type of cybercrime in which cyber-attackers pose themselves as authorized persons or entities and hack the victims’sensitive data.E-mails,instant messages and phone calls are some of the common modes used in *** the security models are continuously upgraded to prevent cyberattacks,hackers find innovative ways to target the *** this background,there is a drastic increase observed in the number of phishing emails sent to potential *** scenario necessitates the importance of designing an effective classification *** numerous conventional models are available in the literature for proficient classification of phishing emails,the Machine Learning(ML)techniques and the Deep Learning(DL)models have been employed in the *** current study presents an Intelligent Cuckoo Search(CS)Optimization Algorithm with a Deep Learning-based Phishing Email Detection and Classification(ICSOA-DLPEC)*** aim of the proposed ICSOA-DLPEC model is to effectually distinguish the emails as either legitimate or phishing *** the initial stage,the pre-processing is performed through three stages such as email cleaning,tokenization and stop-word ***,the N-gram approach is;moreover,the CS algorithm is applied to extract the useful feature ***,the CS algorithm is employed with the Gated Recurrent Unit(GRU)model to detect and classify phishing ***,the CS algorithm is used to fine-tune the parameters involved in the GRU *** performance of the proposed ICSOA-DLPEC model was experimentally validated using a benchmark dataset,and the results were assessed under several *** comparative studies were conducted,and the results confirmed the superior performance of the proposed ICSOA-DLPEC model over other existing *** proposed model achieved a maximum accuracy of 99.72%.

关键词： Phishing email data classification natural language processing deep learning cybersecurity

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SITSL: Strategic Image Tag Recommendation Framework Encompassing Semantics Oriented Learning and Reasoning 2

SITSL: Strategic Image Tag Recommendation Framework Encompas...

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2nd IEEE International Conference for Women in Innovation, Technology and Entrepreneurship, ICWITE 2024

作者： Kumari, Rupa Deepak, Gerard National Institute of Technology Department of Electronics and Communication Engineering Meghalaya India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Computer Science and Engineering Manipal India

ISBN: (纸本)9798350383287

This paper introduces the concept of a strategic structure for Web Image Tagging that is oriented toward semantics and knowledge centric. It encompasses strategies like LDA (Latent Dirichlet Allocation) based topic modelling and ontology generation through the dataset terms and further help in the metadata generation. The generated metadata is classified using autoencoders classifier to yield the classified instances. In the proposed model, the classifier for logistics is applied in the dataset of web images that integrates features through Lin Similarity from the ontologies generated in the perfect global dataset. The LDA also enhances the topics and contextualizes that for classified instances of the dataset. The normalized compression distance with lance William index helps in semantics similarity computation for semantics measure-based reasoning and learning. Shuffle frog algorithm helps in Optimization helps in refining the initial solutions into a much more optimal solution state to produce in the top-tier images. So, to produce in the top-tier tags for the images and overall precision of dash with a measure of dash and an FDR of dash has been achieved by the proposed framework which is the best-in-class model for web image tagging. © 2024 IEEE.

关键词： Semantics

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Evolution and Role of Optimizers in Training Deep Learning Models

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IEEE/CAA Journal of Automatica Sinica 2024年第10期11卷 2039-2042页

作者： XiaoHao Wen MengChu Zhou Guangxi Normal University Guilin 541004China School of Information and Electronic Engineering Zhejiang Gongshang UniversityHangzhou 310018China the Helen and John C. Hartmann Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA IEEE

TO perform well,deep learning(DL)models have to be trained *** optimizer should be adopted?We answer this question by discussing how optimizers have evolved from traditional methods like gradient descent to more advanced techniques to address challenges posed by highdimensional and non-convex problem *** challenges include their hyperparameter sensitivity,balancing between convergence and generalization performance,and improving interpretability of optimization *** continue to seek robust,efficient,and universally applicable optimizers to advance the field of DL across various domains.

关键词： Which Deep continue

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Visual Computing Unified Application Using Deep Learning and computer Vision Techniques

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第1期18卷 59-74页

作者： Sowmya, B.J. Meeradevi Seema, S. Dayananda, P. Supreeth, S. Shruthi, G. Rohith, S. Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Karnataka Bengaluru India Department of Artificial Intelligence and Machine Learning Ramaiah Institute of Technology Karnataka Bengaluru India Department of Computer Science and Engineering M S Ramaiah Institute of Technology Karnataka Bengaluru India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Karnataka Manipal India School of Computer Science and Engineering REVA University Karnataka Bengaluru India Department of Electronics & Communication Engineering Nagarjuna College of Engineering & Technology Karnataka Bengaluru India

Vision Studio aims to utilize a diverse range of modern deep learning and computer vision principles and techniques to provide a broad array of functionalities in image and video processing. Deep learning is a distinct class of machine learning algorithms that utilize multiple layers to gradually extract more advanced features from raw input. This is beneficial when using a matrix as input for pixels in a photo or frames in a video. computer vision is a field of artificial intelligence that teaches computers to interpret and comprehend the visual domain. The main functions implemented include deepfake creation, digital ageing (de-ageing), image animation, and deepfake detection. Deepfake creation allows users to utilize deep learning methods, particularly autoencoders, to overlay source images onto a target video. This creates a video of the source person imitating or saying things that the target person does. Digital aging utilizes generative adversarial networks (GANs) to digitally simulate the aging process of an individual. Image animation utilizes first-order motion models to create highly realistic animations from a source image and driving video. Deepfake detection is achieved by using advanced and highly efficient convolutional neural networks (CNNs), primarily employing the EfficientNet family of models. © 2024 by the authors of this article. Published under CC-BY.

关键词： Animation

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Weapon Detection System For Security And Surveillance Using YOLOv8 2

Weapon Detection System For Security And Surveillance Using ...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Arivalagan, Divya Rubankumar, D. Thushiyanth, K. Suresh Kumar, S. Priyadharsini, V. Vignesh, O. Madras Institute of Technology Anna University Department of Electronics Engineering Chennai India QIS College of Engineering and Technology Department of Electronics and Communication Engineering Ongole India Akshaya College of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350371314

The study focuses on developing and refining an advanced object detection model for integration into security systems. It begins with an exploration of challenges during early training epochs, aiming to enhance precision, recall, and overall performance. The training process involves meticulous data preprocessing, augmentation, and iterative optimization of hyperparameters to foster model convergence. Significantly, this project holds implications for security applications, offering an innovative approach to threat detection through advanced computer vision technologies. Notably, the study incorporates the usage of YOLOv3 and YOLOv8 models. The YOLOv8 model showed a mAP50 of 0.936 compared to YOLOv3, which had an mAP50 of 0.719, demonstrating superior performance with YOLOv8. Considerations for real-time processing, integration of temporal information, and collaboration with security professionals are vital for further advancements. © 2024 IEEE.

关键词： Object detection

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Integrating Decision Trees with Cloud Computing for Predicting Cognitive Decline in Alzheimer's Patients 2

Integrating Decision Trees with Cloud Computing for Predicti...

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2nd IEEE International Conference on Integrated Circuits and communication Systems, ICICACS 2024

作者： Meenakshi, K. Ravinder, Bathini Pasha, Mohammed Rehaman Thinakaran, K. Jayanayudu, Dabbara Maniraj, S.P. SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Vadapalani India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Guntur India Malla Reddy Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad India Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Saveetha School of Engineering Tamil Nadu Chennai India Siddartha Institute of Science and Technology Department of Electronics and Communication Engineering Andhra Pradesh Puttur India SRM Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350317558

The proposed research work suggests a combined technique based on decision trees and cloud computing for predicting cognitive decline in Alzheimer's patients. Medical records and data from wearable devices are among the many sources that perform preprocessing and feature engineering. Decision tree models can make the most efficient use of computing resources. Decision tree topologies are tested for performance criteria, including accuracy and F1 score. The system's predictive powers are rather encouraging since it frequently achieves accuracy rates over 90%. However, limitations in data quality and the intricacy of decision tree interpretation provide challenges. The prediction of cognitive deterioration in Alzheimer's patients may be made more scalable and interpretable by integrating decision trees with cloud computing. This method has promise for early identification and intervention despite its limitations. To further improve prediction accuracy and clinical value, future research should increase data quality and make it easier to understand. © 2024 IEEE.

关键词： Alzheimer's disease Cloud computing Decision trees Early detection Healthcare

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A sparse attack method on skeleton-based human action recognition for intelligent metaverse application

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Future Generation computer Systems 2023年 143卷 51-60页

作者： Dai, Cheng Huang, Yinqin Chien, Wei-Che College of Computer Science Sichuan University Sichuan Chengdu China School of Information and Communication Engineering University of Electronic Science and Technology of China Sichuan Chengdu China Department of Computer Science and Information Engineering National Dong Hwa University Hualien City Taiwan

Graph convolutional networks (GCNs) based skeleton human action recognition has been one of the hottest techniques in intelligent Metaverse system applications in recent years. However, the robustness of GCN model based skeleton action recognition, has not been widely discussed due to the complex spatio-temporal nature. Base on solving this problem, we propose a sparse attack method with dynamic attention which can generate adversarial samples with lower deviation under the condition of keeping attack success rate in this paper. Firstly, we develop spatial temporal consistency loss which can not only preserve spatial integrity between reference sample and adversarial sample but also keep temporal coherence between consecutive frames across adversarial sample. Then, we develop an interaction-based perturbation contribution analyze method to discard unnecessary perturbations. Besides that, we design a dynamic attention approach to tilt the perturbations towards some joints that have higher dynamics which can decrease overall deviations. Finally, we conduct extensive experiments to evaluate our proposal and the experimental results show that our method contributes to discover potential causes of model fragility and provides material in adversarial training which will increase the robustness of GCN based skeleton human action recognition models. © 2022

关键词： Musculoskeletal system

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