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4th International Conference on Inventive Research in computing Applications, ICIRCA 2022

作者： Pradeepa, G. Devi, R. Vels Institute of Science Technology & Advanced Studies Department of Computer Applications Chennai India

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

Malicious web pages are well-known threats in cyberspace. Malicious webpages and their URLs appear to be legitimate webpages in cyberspace and are used to steal personal information from internet users (victims) or to install malicious software on the victim's machine in order to gain further access and make the victims more vulnerable in cyberspace. Identifying such web pages in cyberspace is a difficult task for internet users. Researchers use different techniques with different sets of features to detect malicious web pages in cyberspace. Most of them extract vital features from URL text for malicious webpage classification due to their simplicity and risk-free nature. Some of them extract the limited number of features from the webpage content for better classification along with other features. Our proposed system considers features that are extracted only from the webpage content and use a machine learning model for classification. The accuracy of the existing and proposed systems is demonstrated experimentally. The result shows that the proposed system is more efficient. © 2022 IEEE.

关键词： Websites

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Malicious Domain Detection using NLP Methods - A Review 11

Malicious Domain Detection using NLP Methods - A Review

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11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Pradeepa, G. Devi, R. Vels Institute of Science Technology and Advanced Studies Department of Computer Applications Chennai India

ISBN: (纸本)9781665487320

Cyber security has emerged as a serious problem in today's business world. Attackers use different techniques to deceive the internet user to perform illegal activities such as stealing sensitive information, injecting malware, using the victim's system for further attack, etc. The majority of attacks are disseminated via social media posts, emails, and SMS messages. The message or webpage has embedded malicious links that will lead the user to the attacker's nest. Consequently, the issue of identifying malicious domain names has sparked significant concern. Researchers employ a variety of methods to find such malicious pages, including black or white lists, rules, machine or deep learning, visual similarity, and language processing (NLP). the attacker's employment of various tricks to confuse internet users, detecting or blocking an attack through a malicious domain is a difficult task. NLP with Machine Learning and NLP with Deep Learning methodologies receive the most interest from researchers due to their processing power. This paper provides an overview of several NLP text processing techniques as well as recent domain-related research. © 2022 IEEE.

关键词： Malware

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Early Retinal Degeneration Detection with Deep Faster CNNs 2

Early Retinal Degeneration Detection with Deep Faster CNNs

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Priyadharshini, K. Balraj, Sindhusaranya Kumar, J.V. Rama Vidhate, Deepak A. Thilakavathy, P. Vanathi, P. Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai India Department of Computer Science and Engineering Sona College of Technonolgy Salem India SRKR Engineering College Andhra Pradesh Bhimavaram India Department of Information Technology Dr. Vithalrao Vikhe Patil College of Engineering Maharashtra Ahmednagar India Vels Institute of Science Technology and Advanced Studies Chennai India Department of Information Technology Adhiyamaan College of Engineering Hosur India

ISBN: (纸本)9798350383867

The issue of retinogenesis probably constitutes a major public health threat which gives the reason to carry out its early detection for intervention purposes to be efficient. Throughout this investigation, we suggest a new technique which uses the faster CNNs for early detection of retina degradation. We set up a diverse data collection where OCT(optical coherence tomography), fundus photography and angiography modality were included . This collection covered the various stages of degeneration. The dataset was prepared for instance with resizing, normalizing, and data augmentation were used to improve share quality and diversity. Deeper and faster networks that were developed and trained using the backpropagation technique performed well and further optimized using Adam, with high accuracy of 94 in the test dataset. 7%. The strength of our results can be seen through evaluation metrics in detail like confusion matrices and classification reports. The model can distinguish various cases of retinal degeneration accurately which proves to have high precision and recall for mild and normal degeneration. Interpolation analysis of the model using Grad-CAM and occlusion sensitivity, giving the clue about the model's decision making process, setting a precedent for clinical interpretation. The enhanced cross-validated model on an independent dataset also arrays the model in a representational where it will be able to generalize in a diverse population and in different imaging conditions. The systematic approach which proved to be more accurate and resilient than the other techniques was the one which was compared to the mentioned ones. In conclusion, our study shows the possibility for application of the Deep Faster CNNs in identifying the early signs of retinal degeneration;the tool therefore becomes an important tool for enhancing patient outcomes and prevention of vision loss in the world at large. ©2024 IEEE.

关键词： Ophthalmology

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Artificial Intelligence Controlled Manufacturing Strategies for New Energy Vehicles

Artificial Intelligence Controlled Manufacturing Strategies ...

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Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Paul Sundar Singh Priya R Department of Computer Science Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai India Department of Computer Applications Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Manufacturing strategies which focus on New Energy Vehicles (NEVs) need to innovate because their rapid growth requires more efficient and sustainable production methods. This research studies flexible manufacturing since it represents the fundamental solution for NEV production difficulties. The article evaluates three manufacturing approaches including contract manufacturing together with skateboard platform sharing and modular production lines which show advantages in cost reduction and faster production times and better market responsiveness. Real examples from Tesla, Canoo and Toyota demonstrate how flexible manufacturing works in the NEV sector. Some interconnected obstacles including elevated start-up expenses and supplier connection requirements and employee skill development remain despite productive benefits. The research presents forecasts regarding AI-controlled intelligent manufacturing sites and environmentally-friendly production practices which will facilitate the development of an optimized NEV production network. The NEV industry will push global sustainability in transportation by using flexible manufacturing to solve production challenges.

关键词： Industries Transportation Production Manufacturing Batteries Artificial intelligence Sustainable development Contracts Smart manufacturing Automotive engineering

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An Adaptive Privacy-Preserving Framework for Geospatial Data: Enhancing Accuracy and Utility in Urban Planning and Catastrophe Management

An Adaptive Privacy-Preserving Framework for Geospatial Data...

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Innovative computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： D.S. Eunice Little Dani K. Rohini Department of Computer Science Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai Tamilnadu India School of Computing Sciences Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai Tamilnadu India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

The growing need for geospatial data analysis highlight's location privacy issues. Although existing technologies such as differential privacy and location obfuscation can be relatively large, inaccurate, and difficult to handle in many applications like urban planning and disaster management, its privacy protection benefits make up for the defect. but in the research, a new privacy-preserving framework based on adaptive differential privacy model, k-anonymity and spatial cloaking is introduced. Based on geographic sensitivity, the system dynamically distributes privacy resources, thus ensuring accuracy in critical regions by choosing quality points from the data. It works to strike a balance between user privacy and system functionality by using genetic algorithm optimization and geographic analysis approaches like DBSCAN. As can be seen from the above comparisons with other systems, the experimental results show that it achieves higher levels of privacy protection than traditional methods with a lower differential privacy parameter (ε = 0.5) and larger k-anonymity k-values (k = 10). Also, the cluster quality improved phenomenally (silhouette coefficient = 0.75) and mean absolute error dropped significantly, both of which help with data utility improvement. The architecture offers a scalable, effective solution that ensures both high privacy protection and accurate representation of essential data.

关键词： Differential privacy Privacy Accuracy Adaptive systems Sensitivity Scalability Urban planning Geospatial analysis Protection Optimization

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Novel Optimized Feature Selection Using Metaheuristics Applied to Physical Benchmark Datasets

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computers, Materials & Continua 2023年第2期74卷 4027-4041页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Fadwa Alrowais Sunil Kumar Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt School of Computer Science University of Petroleum and Energy StudiesDehradun248001India Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

In data mining and machine learning,feature selection is a critical part of the process of selecting the optimal subset of features based on the target *** are 2n potential feature subsets for every n features in a dataset,making it difficult to pick the best set of features using standard ***,in this research,a new metaheuristics-based feature selection technique based on an adaptive squirrel search optimization algorithm(ASSOA)has been *** using metaheuristics to pick features,it is common for the selection of features to vary across runs,which can lead to *** of this,we used the adaptive squirrel search to balance exploration and exploitation duties more evenly in the optimization *** the selection of the best subset of features,we recommend using the binary ASSOA search strategy we developed *** to the suggested approach,the number of features picked is reduced while maximizing classification accuracy.A ten-feature dataset from the University of California,Irvine(UCI)repository was used to test the proposed method’s performance *** other state-of-the-art approaches,including binary grey wolf optimization(bGWO),binary hybrid grey wolf and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hybrid GWO and genetic algorithm 4028 CMC,2023,vol.74,no.2(bGWO-GA),binary firefly algorithm(bFA),and *** results confirm the superiority and effectiveness of the proposed algorithm for solving the problem of feature selection.

关键词： Metaheuristics adaptive squirrel search algorithm optimization methods binary optimizer

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Optimized Dense Neural Network Architecture for Bone Marrow Cancer Classification Using Custom Callbacks

Optimized Dense Neural Network Architecture for Bone Marrow ...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： G. Deepa Y. Kalpana Department of Computer Science VELS Institute of Science Technology and Advanced Studies (VISTAS) Chennai India Department of Computer Application and IT VELS Institute of Science Technology and Advanced Studies (VISTAS) Chennai India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Classification of bone marrow cancer is a crucial step in the analysis of several hematological cancers, such as multiple myeloma and leukemia. This study shows better dense neural network (DNN) architecture for significant the variation among the five main types of bone marrow cells includes lymphocytes, myelocytes, band neutrophils, and plasma cells. To find and diagnose bone marrow cancers like multiple myeloma, acute myeloid leukemia (AML), and acute lymphoblastic leukemia (ALL) early on, it is important to be able to accurately identify these cell types. The suggested DNN model uses unique callbacks to optimize training, avoid overfitting, and guarantee faster convergence. These callbacks include learning rate scheduling, early pausing, and dynamic weight modifications. The system preprocesses the high-resolution bone marrow image database to enhance contrast, adapt to fluctuations in illumination, and utilize dense layers for cell classification and convolutional layers for feature extraction. After extensive hyperparameter tuning, a model that classified these five categories of bone marrow cells obtained an overall accuracy rate of 95%. In terms of accuracy, recall, and F1 scores, the model performs better than conventional machine learning techniques while retaining computing economy. The model is able to increase training times without sacrificing classification performance by incorporating custom callbacks. With its enormous clinical application potential, our improved DNN architecture offers pathologists a dependable and effective tool for accurately classifying bone marrow cells. By improving bone marrow cancer diagnosis accuracy, this approach can improve patient care and treatment results.

关键词： Training Adaptation models Accuracy Computational modeling Neural networks Leukemia Cells (biology) computer architecture Bones Overfitting

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A Novel Preprocessing Technique to Aid the Detection of Infected Areas of CT Images in COVID-19 Patients Artificial Intelligence (AI) for Communication Systems

A Novel Preprocessing Technique to Aid the Detection of Infe...

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Trends in Quantum computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： J. Kalaivani A. S. Arunachalam R. Gobinath Department of Computer Science School of Computing Sciences Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai India Department of Computer Science Christ University Bengaluru India

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

An innovative preprocessing method for discerning infected areas in CT images of COVID-19 is described in this abstract. The methodology being suggested exploits the capabilities of artificial intelligence (AI) to improve disease detection communication systems. By employing sophisticated AI algorithms to preprocess CT images, the method seeks to increase the precision and effectiveness of COVID-19-associated area detection. The incorporation of artificial intelligence (AI) into communication systems facilitates enhanced image analysis, resulting in improved diagnostic capabilities and treatment strategizing. The study's findings demonstrate the potential of preprocessing techniques powered by artificial intelligence in augmenting communication systems with the aim of enhancing healthcare outcomes.

关键词： COVID-19 Quantum computing Communication systems Computed tomography Perturbation methods Noise Refining

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Product Reviews Based Sentimental Analysis by Feature Extraction and Classification Using Machine Learning Algorithm

Product Reviews Based Sentimental Analysis by Feature Extrac...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： A.S. Sridhar S. Nagasundaram Department of Computer Science School of Computing Sciences Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai Tamil Nadu India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Sentiment analysis and opinion mining are two of the most popular domains for evaluating and extracting insights from text data from various sources, like Facebook, Twitter, Amazon, and so on. It is essential for giving companies the ability to actively work on refining their company plans and thoroughly understand what customers have to say about their offerings. This research proposes novel technique in product review based sentimental analysis by online reviews data feature extraction with classification using machine learning model. here the input has been collected as online review data and processed for noise removal and normalization. Then, this data features are extracted and classified using graph recurrent component analysis with support Bayesian fuzzy neural network (GRCA-SBFNN) for detection of the user sentiment for the reviewed product. the experimental analysis has been carried out for various online product review dataset in terms of training accuracy, average precision, area under the curve (AUC), F-1 score and recall. The proposed technique Training accuracy 98%, Average precision 97%, AUC 91%,Recall 96%, F-1 SCORE 92%.

关键词： Training Sentiment analysis Analytical models Reviews Social networking (online) Machine learning Companies Feature extraction Fuzzy neural networks Data mining

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Analysing the Quality and Management of Data in Big Data

Analysing the Quality and Management of Data in Big Data

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Distributed computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： Pratheesh R V. Divya School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Department of Information Technology School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Chennai India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

Big data analytics delves into these immense datasets to reveal hidden patterns and correlations, yet its pervasive use raises substantial security and privacy concerns. This article centers on the specific issues concerning privacy and security within the realm of big data, distinguishing between these concerns and outlining their distinct requirements. The focus is on various privacy strategies, including L-diversity, k-anonymity, HyBrEx, and T-closeness, examining their practical applications in diverse business contexts. Privacy protection methods have surfaced throughout various stages of the big data life cycle, aiming to ensure privacy during data creation, retention, and handling. This paper provides an extensive review of these privacy mechanisms while addressing the challenges encountered by current approaches. As the extended comprehensive study is experimented with features extraction techniques such as Principle component analysis (PCA), Independent component analysis (ICA), Linear discriminant analysis(LDA) the evaluated performance in terms of accuracy achieved with 98%, 99% and 95% respectively.

关键词： Privacy Differential privacy Technological innovation Shape Reviews Big Data Security

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