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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Mallavarapu, Jashritha Reddy Navanithi, V.K. Bejjanki, Mohit Manomay Mithun Menon, M. Jeyakumar, G. Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350328202

This comprehensive review paper presents an in-depth exploration of diverse approaches employed in multi-objective personalized drug design, encompassing cutting-edge methodologies that leverage advanced technologies. The multifaceted landscape of drug design is scrutinized through the lenses of neural network approaches, stochastic modeling, decentralization, blockchain-based solutions, topological optimization strategies, genetic algorithm optimization, energy consumption optimization, and blockchain-based pharmaceutical supply chain management. Each approach's unique strengths and applications are thoroughly examined, shedding light on their respective contributions to the burgeoning field of personalized medicine. This review not only synthesizes the current state of research but also identifies opportunities for synergistic integration of these approaches to further enhance the precision and efficacy of personalized drug design, ultimately paving the way for a transformative era in healthcare. © 2024 IEEE.

关键词： Blockchain

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Evolving Towards Optimal Cloud Resource Allocation and Cost Management: An In-depth Analysis 1

Evolving Towards Optimal Cloud Resource Allocation and Cost ...

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1st IEEE International Conference on Advances in computing, Communication and Networking, ICAC2N 2024

作者： Pal, Sourabh Agrawal, Krishna Kant Kasi, Baskar Galgotias University School of Computing Science and Engineering Greater Noida India

ISBN: (纸本)9798350356816

This research paper explores the field of cloud computing optimisation, with the goal of tackling the two-fold difficulty of efficiently allocating resources and effectively managing costs. The primary objective is to achieve a balance between optimising the allocation of resources to enhance performance and minimising operational expenses in cloud systems. Our approach incorporates a systematic methodology that includes a thorough examination of existing literature, the development of theoretical models, and the execution of substantial experiments. The suggested analysis indicates that dynamic allocation, load balancing, and auto-scaling techniques have significant potential in reaching this balance. Dynamic allocation adjusts resources in real-time to accommodate varying workloads, while load balancing maintains equitable distribution of resources. Auto-scaling stands out as the top performer, with a significant 28% reduction in costs and a noteworthy 30% boost in performance. The proposed research expands upon previous studies by offering a comprehensive evaluation of resource allocation options within the existing approaches. It highlights the feasibility of obtaining cost reductions while concurrently improving performance in cloud systems. However, the suggested study recognises specific constraints, including the utilisation of synthetic workload data and simplified cloud infrastructures. The research has significant ramifications as it provides guidance to organisations on how to optimise their allocation of cloud resources and manage costs. This ultimately promotes efficient and cost-effective cloud operations. © 2024 IEEE.

关键词： Cloud platforms

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Crop Damage Detection using Machine Learning Approaches 6

Crop Damage Detection using Machine Learning Approaches

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6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Nair, Priyanka C Sampath, Nalini Reddy, P Praneeth Shruthi, P Sai Department of Computer Science and Engineering Amrita School of Computing Bengaluru India

ISBN: (纸本)9798350381689

Agriculture is of vital importance to human life as it is the main source of livestock production and contributes significantly to the country's employment opportunities and economy. Ensuring high standards of production quality is essential. Recent technological advances are merging agricultural and machine learning techniques to help improve crop quality. The main objective of this work is to make use of machine learning approaches to identify the healthy status of the crop. Multi-class classification is applied to the dataset which helps classify crop conditions. As there is an imbalance of data of the 3 classes, Random under- sampling and Tomek under-sampling methods are applied on the dataset. A combination of Tomek undersampling and Smote undersampling has also been performed on this dataset. Various algorithms like Gradient Boosting, Gaussian Naïve Bayes, Neural Network (MLP) and k-Nearest Neighbors were performed on the dataset which helped show that Randomforest was the best model in terms of accuracy and F1 score. © 2024 IEEE.

关键词： Adversarial machine learning

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Interplay of Technology and Social Dynamics: Community Management Applications in Sports Communities 5

Interplay of Technology and Social Dynamics: Community Manag...

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5th IEEE International Conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Sharma, Akshita Wadhwa, Aashima Gupta, Kushal Galgotias University School of Computing Science and Engineering Greater Noida India

ISBN: (纸本)9798350383522

In recent years, sports communities have witnessed significant growth, both in terms of participation and engagement. The advent of digital technology has provided a unique opportunity to enhance the dynamics within these communities, fostering stronger connections among members. To harness this potential, an increasing number of sports enthusiasts are turning to community management applications, which serve as central hubs for communication, organization, and engagement. This research paper explores the development and implications of a comprehensive community management application tailored to sports communities. © 2024 IEEE.

关键词： Central Hubs Digital Technology Online Community Building Online Platforms Participation Growth Social Interaction Community Dynamics Sports Enthusiasts Technology in Sports User Engagement

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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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DeepLeuk: a convolutional neural network pre-trained model for microscopic cell images-Based leukemia Cancer analysis

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Multimedia Tools and Applications 2025年第14期84卷 13809-13842页

作者： Yenurkar, Ganesh Keshaorao Mal, Sandip Thakur, Nileshsingh Dhomne, Shrawani Dhurve, Merula Patel, Mayank Kulmeti, Karan Dhurve, Harsh Yeshwantrao Chavan College of Engineering Wanadongri Nagpur India School of Computing Science & Engineering VIT Bhopal University Bhopal India VIT Bhopal University Bhopal India

The blood and bone marrow are affected by leukemia, a deadly kind of cancer, that significantly impacts the quality of life of those diagnosed. Early identification and precise diagnosis are crucial for improving survival rates. Fortunately, recent advancements in medical image analysis, particularly deep learning-based techniques, have greatly improved the ability to distinguish leukemia cells from healthy ones through microscopic cell images. This research introduces a deep learning-based leukemia cancer classifier, specifically a CNN pre-trained model, utilizing microscopic cell images to detect malignant cells. Using pre-processing techniques such as picture scaling, Region of Interest (RoI) extraction, and Improved Anisotropic Filtering (IAF) and feature extraction, the blood cell image dataset is first cleaned. After that leukemia-affected and healthy cells are evaluated using various classification algorithms and neural networks, with optimal features identified to improve classifier performance. The results suggest that neural networks function well as a classifier algorithm to detect whether the person is cancerous or non-cancerous, with the proposed CNN pre-trained model providing precision of 98.9%, which is higher than any other method mentioned. The proposed model prioritizes recall, a key performance metric, to reduce the number of false negatives. Accurate diagnosis and treatment are critical, as misdiagnosing a patient with cancer as not having cancer can lead to severe consequences. With the main objective of minimizing inadvertent mistakes made by physicians, the proposed model performs better than kNN, Decision Trees, Random Forest, SVM, and Logistic Regression models. Using deep learning-based techniques to improve cancer diagnosis and treatment is essential. Improving survival rates and the quality of life for individuals with leukemia requires early identification and accurate diagnosis. This research can help doctors make more accurate diagnos

关键词： Image classification

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Comparitive Analysis of Spectral Indices for the Identification of Plastic Patches in the Steam 1

Comparitive Analysis of Spectral Indices for the Identificat...

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1st IEEE International Conference on Advances in computing, Communication and Networking, ICAC2N 2024

作者： Shankar, Ansh Varshney, Dhruv Katiyar, Alok Mishra, Vikash Kumar Galgotias University School of Computing Science and Engineering Greater Noida India

ISBN: (纸本)9798350356816

The escalating problem of waste clusters in the planet's oceans has raised alarms, with the Indian Ocean not being spared from the growing predicament of aquatic refuse. In this exploration, we harnessed the power of remote sensing advances, utilizing Sentinel-2 spacecraft observations to track and assess the proliferation of waste clusters within the Indian Ocean. The Sentinel-2 spacecraft's Multi Spectral Instrument (MSI) endows us with critical data about the hues of the ocean, which is instrumental in identifying and outlining these zones of concentrated pollution. The scope of this paper encompasses a detailed examination of the Sentinel-2 satellite's capabilities in remotely surveying the trash accumulation in the Indian Ocean, with an emphasis on gathering, preparing, and evaluating MSI data. This research taps into Sentinel-2's distinct features to unlock a better comprehension of the patterns and evolution of waste buildup in the Indian Ocean, shedding light on a significant ecological challenge. The findings affirm the value of Sentinel-2 as a tool for overseeing and tackling marine debris in the Indian Ocean and furnishing crucial insights to decision-makers and environmental bodies dedicated to shaping effective countermeasures. © 2024 IEEE.

关键词： Satellite imagery

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Beyond Search: A Network-Based Exploration of Buyer Behavior in the OLX Car Market 4

Beyond Search: A Network-Based Exploration of Buyer Behavior...

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4th International Conference on Multimedia Processing, Communication and Information Technology, MPCIT 2024

作者： Ashvath, Y. Moin Ramya, G.R. Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350375466

The information on used cars offers a thorough analysis of the second-hand car industry. This dataset may be utilized to construct predictive models that can estimate the price of a used car based on its characteristics, including the make, model, year, mileage, fuel type, and condition. This dataset enables comprehensive data analysis to extract insights into market trends, pricing dynamics, and buyer-seller behavior. The main goals are to examine price patterns depending on different characteristics, assess the influence of fuel type and geographical factors on pricing and demand, investigate the correlation between mileage and resale value, and determine the most popular automobile models. Furthermore, the dataset enables the development of predictive models that can estimate both selling prices and the amount of time it takes to sell a product. Methodologies encompass the processes of data cleaning and exploratory data analysis (EDA). The results will assist in identifying communities or clusters within the dataset, uncovering groups of cars with similar attributes or market characteristics. The results aim to offer comprehensive market insights, improve comprehension of buyer behavior, and create predictive tools to assist sellers, buyers, and platform operators in decision-making. Ultimately, this will optimize strategies and operations in the used car market. © 2024 IEEE.

关键词： Sales

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Improving Long Short-Term Memory (LSTM)-Based Stock Market Price Predictions in the Machine Learning Era 5

Improving Long Short-Term Memory (LSTM)-Based Stock Market P...

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5th IEEE International Conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Kumar, Sarvesh Pal, Nitin Tripathi, Aanjey Mani Galgotias University School of Computing Science and Engineering Greater Noida India

ISBN: (纸本)9798350383522

Post covid, world especially India has seen enormous rise in stock market investor. Profits in stock market solely depends upon prediction - right prediction can help gain money. And predicting stock price can be very tedious task if done manually. Traditional methods are using stock price charts, exponential moving average (EMA), oscillators, indicators etc. While predicting stock price manually, human greed nature can lead to wrong decisions while choosing right stock. Latest technologies like machine learning and artificial intelligence can be used to predict future of stocks. Several research and works with different techniques have been already carried out to do the same. In this research explores a method, for predicting stock prices using machine learning, Long Short-Term Memory (LSTM) networks. These networks, which fall under the umbrella of learning provide a solution, by successfully capturing complex patterns from past stock data. As we explore the existing body of research and real-world implementations our goal is to participate in the discussion surrounding the utilization of machine learning, specifically LSTM networks to improve the precision and dependability of stock price forecasts. Looking ahead we anticipate a future where financial decision making becomes more informed and accurate. © 2024 IEEE.

关键词： deep learning financial forecasting lstm machine learning stock price prediction

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Face Recognition-Based Automated Attendance System 5

Face Recognition-Based Automated Attendance System

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5th IEEE International Conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Tripathi, Arunendra Mani Rai, Ashish Kumar Pandey, Divyanshu Galgotias University School of Computing Science and Engineering Greater Noida India

ISBN: (纸本)9798350383522

The attendance system is an essential part of any organization, and the traditional manual attendance system can be very prone to errors and very time-consuming. This paper introduces a sophisticated automated attendance system that combines Principal Component Analysis (PCA) algorithm and Artificial Intelligence (AI) for enhanced accuracy and efficiency. In contrast to error-prone and time-consuming manual attendance processes, our system leverages PCA for robust facial feature analysis, allowing precise identification of students or workers. The integration of AI further refines the recognition process, adapting and learning from diverse facial characteristics to improve system performance over time. © 2024 IEEE.

关键词： AI eigenfaces eigenvectors Facial Recognition FLTK OpenCV PCA

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