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An efficient optimal load balancing algorithm for distributed file systems in cloud environment

International Journal of Networking and Virtual Organisations 2024年第2期30卷 134-151页

作者： Nebagiri, Manjula H. Latha, P.H. Department of Computer Science Atria Institute of Technology Bengaluru India Department of Information Science Sambhram Institute of Technology Bengaluru India

Efficient operations in distributed environments can be obtained by load balancing (LB). LB has turned out to be a vital and interesting research area with respect to the cloud owing to the swift augmentation of cloud computing, and the more services together with better outcomes demand of the clients. The work has developed a framework named an efficient optimal LB (EOLB) for distributed files system to beat the challenges faced in LB. LB was done by means of the framework centred on node distribution together with task distribution. Centred upon the data aspects as well as cloud servers, say CPU in addition to memory usage, together with disk IO occupancy rate, etc., it renders task distribution. Experimental analysis exhibits that the framework attains a better response rate of 74.68 ms, and a processing time (PT) of 0.43 ms, in addition, remains to be efficient when weighed with the prevailing methods. © 2024 Inderscience Enterprises Ltd.

关键词： Distributed cloud

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Gauging Deep Learning Archetypal Effectiveness in Haematological Reclamation

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SN computer science 2024年第7期5卷 963页

作者： Pradeep kumar, B.P. Ravikumar, J. Dept. of Computer Science and Design Atria Institute of Technology VTU Karnataka Bangalore India Dept. of Computer Science and Engineering Atria Institute of Technology VTU Karnataka Bangalore India

This paper project an system for reclamation of medical images in an effective way for the extraction and retrieving of data from large archives is critical. This study aims to improve haematological image reclamation by combining visual-based image reclamation (VBIR) approaches with the GLCM algorithm, specifically for characteristics related to the prostate gland. While previous research has focused on computer-aided methods for Gleason grading, our technique focuses on early-stage feature reclamation across 14 paired glands. The experimental findings, including Bicubic Interpolation The resolution Enrichment with a standard deviation of 12.37, kurtosis of 122.83, and skewness of 2.92, demonstrate improved reclamation performance. Our suggested methodology uses a handmade learning approach that includes critical components such as GLCM contrast and correlation. This extensive feature extraction step includes descriptors for morphology, texture, fractals, and contextual information, which are then subjected to in-depth statistical analysis to identify significant features. Using an RNN, our technique improves performance somewhat but significantly when compared to alternatives, with time required for execution offering insights into the intricate interaction of stages and the effect of sample variability. These findings not only encourage more research, but also help to advance standard reviews in haematological analysis of images. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Gland reclamation Gray-level co-occurrence matrix (GLCM) Haematological Haematological image Prostate gland

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An approach to on-stream DDoS blitz detection using machine learning algorithms

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Materials Today: Proceedings 2023年 80卷 3492-3499页

作者： Manjula, H.T. Neha Mangla Atria Institute Of Technology Bengaluru India Department Of Computer Science & Engineering CMRIT Bengaluru India Department of Information Science & Engineering Atria Institute of Technology Bengaluru India

Distributed Denial of service (DDoS) attacks is an enormous threat to today's cyber world, cyber networks are compromised by the attackers to distribute attacks in a large volume by denying the service to legitimate users. The toughest and challenging task in today's network and network security engineers is to identify compromised traffic (attacked) and legitimate (normal) traffic. The main goal of the paper is to detect DDos attacks using classification algorithms. To achieve the goal the proposed system uses attacking tool to initiate attacks using Loic attacking tool with the data set extracted from open source tool Wireshark and transferring the dataset to apache Spark for detection analysis. The system also uses Apache spark machine learning algorithms (MLib), classification algorithms to classify the dataset. We use Naive Bayes, KNN and Random forest classification algorithms to classify normal traffic and attacked traffic. Our system is capable of detecting attacks with respect to any traffic protocols ICMP, TCP, or UDP. The accuracy of detection is compared on three classification algorithms and noted that random forest gives the accuracy of 96.75%. © 2021

关键词： Denial-of-service attack

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SMSavvy - A System to Enhance the Capabilities of Feature Phones Using Automation 1

SMSavvy - A System to Enhance the Capabilities of Feature Ph...

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1st IEEE International Conference on Electronics, Computing, Communication and Control technology, ICECCC 2024

作者： Datta, Aryaman Pinto, Joel Atria Institute of Technology Department of Computer Science Bengaluru India

ISBN: (纸本)9798350371802

A large part of our daily lives is spent online, consuming services provided over the Internet. Most Institutions and Corporate Organizations both private and public rely on the Internet to discern critical information, services and utilities. The Internet has thus become a central figure governing human life. Connectivity to the Internet however requires relevant hardware and network infrastructure. Basic knowledge of the web is also critical to navigating through the Internet. Access to Internet capable devices, adequate network infrastructure and basic knowhow of using the web are the main hindrances faced by developing countries like India in extending the outreach of the Internet. Individuals within lower income brackets are notably impacted by the three aforementioned factors. This results in a scenario where they possess feature phones or outdated mobile devices that lack compatibility with contemporary internet standards. Additionally, the financial constraints they face often render them unable to afford high-speed internet data plans. This digital disparity significantly restricts their access to the advantages of the digital landscape. In this Paper we aim to develop a prototype, which we call SMSavvy that can help provide access to Artificial Intelligence powered chatbots such as ChatGPT through short message service (SMS) interface in feature phones. We do this by using Desktop Automation and Webscraping. An Automation script running on a remote server connected to the internet carries out the request received through SMS from the feature phone. It scrapes the response from the AI chatbot and encapsulates it into an SMS before sending it back to the user. By doing this, reliance on fast internet is overcome and so is the need to purchase expensive internet capable devices curtailed. This prototype can pave the way for more sophisticated and larger cloud based SMS automation systems. © 2024 IEEE.

关键词： Automation

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Enhanced perceptual wavelet packet features for spontaneous Kannada sentence recognition under uncontrolled conditions

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International Journal of Speech technology 2025年第1期28卷 153-174页

作者： Shanthamallappa, Mahadevaswamy Pradeep Kumar, B.P. Department of Electronics and Communication Engineering Vidyavardhaka College of Engineering Mysuru Visvesvaraya Technological University Karnataka India Department of Computer Science and Design Atria Institute Of Technology Bengaluru Visvesvaraya Technological University Karnataka India

A Robust Automatic Speech Recognition (ASR) system is proposed through a hybrid combination of Perceptual Wavelet Packet features, Deep Neural Network-Hidden Markov Model (DNN-HMM) acoustic models, and n-gram language models for recognizing Spontaneously spoken Kannada sentences. Two Experiments are performed on own Kannada speech corpus and the widely recognized TIMIT corpus. First one is the experiment on the clean speech datasets and the second one is on the speech degraded by noise data chosen from Noise-US dataset. The proposed system performance is evaluated through the metric Word Error Rate (WER). A comparative study on the performance analysis is presented. The results of the experimental investigations achieved an average reduction in Word Error Rate (WER) of 1.8% over Mel Frequency Cepstral Coefficients (MFCCs) and 2.1% over Perceptual Linear Prediction (PLP) features. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Hidden Markov models

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Identification and Classification of Intestinal Parasitic Eggs in Animals Through Microscopic Image Analysis 3rd

Identification and Classification of Intestinal Parasitic Eg...

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3rd International Conference on Emergent Converging Technologies and Biomedical Systems, ETBS 2023

作者： Mishra, Ketan Kavitha, C. Kannan, Devi Department of Computer Science and Engineering Atria Institute of Technology Bangalore India

ISBN: (纸本)9789819986453

Intestinal parasitic infections in animals can cause a range of symptoms, including diarrhea, weight loss, anemia, and malnutrition. This project aims to classify parasitic eggs belonging to the Monezia and Strongyles species using microscopic images from a veterinary hospital. The dataset consisted of stool samples from animals infected with these parasites. Image processing and machine learning techniques, including data preprocessing, feature extraction, and feature selection are used to detect and classify the eggs. The goal was to accurately classify the eggs into either the Monezia or Strongyles species. To achieve the goal, two classification algorithms: K-Nearest Neighbors (KNN) and Decision Tree have been used. Using these algorithms, the model achieved accuracies of 91% and 60%, respectively. However, by incorporating Alyuda NeuroIntelligence for classification, a significantly improved accuracy of 92% was obtained. The results demonstrate that the use of Alyuda NeuroIntelligence in conjunction with machine learning techniques can greatly enhance the accuracy of parasitic egg classification. This approach can aid in the diagnosis and treatment of animal infections, improving animal health and well-being. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Feature extraction

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Emotion Classification in Text Using Machine Learning and LSTM

Emotion Classification in Text Using Machine Learning and LS...

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2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Kumar, B. P. Pradeep Ravi Kumar, J. Biswas, Arpita Hafeeza, Bi Bi Dhaanya, D. Visvesvaraya Technological University Department of Computer Science Design Atria Institute of Technology Karnataka Bangalore India Visvesvaraya Technological University Dept. of Computer Science Engineeering Atria Institute of Technology Karnataka Bangalore India

ISBN: (纸本)9798331538538

Emotion categorization is used in domains like customer service, social media content moderation, and interaction between humans and computers. Classification of emotion from text is a fundamental issue in NLP, or natural language processing. This study examines both advanced deep learning models (LSTM networks with embedding layers) and traditional machine learning techniques (Naive Bayes, Logistic Regression, SVM, Random Forest) for the classification of the six primary emotions: joy, sorrow, anger, fear, love, and surprise. The study uses a preprocessed emotion-tagged text dataset, and metrics including accuracy, precision, recall, and F1-score are utilized to evaluate the models. LSTMs perform better than conventional models, which find it difficult to grasp language subtleties and contextual correlations, by processing sequential data and contextual information with ease. © 2025 IEEE.

关键词： Emotion Recognition

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Unveiling the web of privacy: A comprehensive review of threats to information security in social networks 4th

Unveiling the web of privacy: A comprehensive review of thre...

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4th International Conference on Computational Methods in science and technology, ICCMST 2024

作者： Singh, Shalini Baloni, Dev Sarkar, Manash Department of Computer Science and Engineering Quantum University Roorkee India Department of Computer Science and Engineering Atria Institute of Technology Banglore India

ISBN: (纸本)9781032911571

This review paper explores emerging threats to information privacy and security within the dynamic landscape of Online Social Networks (OSNs), which serve as repositories of vast amounts of user data. The rise in social media use emphasizes how crucial it is to inform users about the dangers that come with it, like cyber-attacks and privacy violations. Even though various social media platforms have been made possible by technological advancements, consumers’ ignorance of these platforms creates serious security risks. Although academics have come up with creative security solutions for social networks, there are obstacles in the way of their actual application. It is essential to continuously assess security vulnerabilities in light of technology improvements. This study examines several dangers to online social networks and suggests countermeasures based on a variety of models, frameworks, and encryption methods. Despite advancements, privacy concerns remain unaddressed, requiring parents to actively supervise their children’s online activity. © 2025 the Author(s).

关键词： Differential privacy

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Diagnosis of Breast Cancer Using Vision Transformers (ViT)

Diagnosis of Breast Cancer Using Vision Transformers (ViT)

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2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Ravikumar, J. Prithvi Prabhu Pani, V. Tejushree, R. Varshitha, Sunnampalli Atria Institute of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331538538

Breast cancer is one of the leading causes of death in women. Regular testing is of paramount importance since early detection ensures more treatment options and better prognosis rate. Biopsies, x-rays and ultrasounds are some of the diagnostic tools used to detect breast cancer. Out of these, ultrasounds being portable and ionizing radiation free provide the most feasible and harmless solution in areas which cannot accommodate expensive equipment that require rigorous safety standards to be met. Our detection system utilizes Vision Transformer (ViT) technology, i.e., transfer learning to classify tumors of the breast into malignant, benign and normal categories. Data enhancement has been done using Contrast Limited Adaptive Histogram Equalization (CLAHE). Furthermore, we have categorized them based on BI-RADS criteria for greater accuracy and precision scores. We have chosen ViT over the more commonly used Convolution Neural Networks (CNN) since they offer a more flexible approach, capturing global relationships in images, and you can use it for larger datasets. © 2025 IEEE.

关键词： Ultrasonic imaging

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Email Validator Dashboard: A Comprehensive Tool for Accurate Email Validation

Email Validator Dashboard: A Comprehensive Tool for Accurate...

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2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Chand, Syed Riyyan Ahmed, Uzair Ismail, T Mohammed Kumar, B.P. Pradeep Chand, Syed Bilal Chaitra, P. Design Atria Institute of Technology Dept. of Computer Science Bangalore India Atria Institute of Technology Dept. of Computer Science Engineering Bangalore India HostUp Cloud Technologies Private Limited Bangalore India

ISBN: (纸本)9798331538538

Email authentication is of the utmost importance in maintaining the reliability and quality of email communication, specifically in database management and bulk email marketing. The centerpiece of the project is the development of an Email Validator Dashboard, which is a very user-friendly functionality filled with other useful parts that guarantee the accurate verification of email addresses. The dashboard really presents valuable inputs quickly in terms of deliverability, risk factors, and domain reliability due to its dynamic visualization along with real-time email validation procedures. The project's structure is such that the users get real-time data in the form of interactive charts and graphs, which make them see the transformations occurring as validation is going on, thus distinguishing it from the traditional tools. It comes with cutting-edge back-end data integrity validation processes, such as mailbox verification, domain DNS checks, and syntax verification, alongside powerful analytics. This project is really useful for organizations and developers who work with large email lists and at the same time can ensure high deliverability and compliance with data standards. © 2025 IEEE.

关键词： Marketing

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