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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Reddy, Gogireddy Meghanath Vasanth, Movva Reddy, Pasam Chandra Shekara Sai Balaji Abhishek, Jala Venkateswarlu, B. Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798350368413

The spread of false information in the digital age is a major concern. Traditional machine learning approaches are useful, but they often struggle to deal with the dynamic nature of incorrect information. This project aims to develop a hybrid fake news detection system that combines traditional machine learning, generative AI, and real-time online search comparison. Our approach begins with a logistic regression classifier that uses TF-IDF properties to quickly filter potentially incorrect content. The article's linguistic and contextual coherence is then evaluated by an advanced analysis that makes use of OpenAI's generative AI through the LangChain framework. A web search tool cross-references the content of the article with up-to-date online data to further assure accuracy. This multi-layered method increases detection accuracy and provides a comprehensive analysis of news veracity. Our results show significant improvements over existing methods and show the potential of combining web data, machine learning, and generative AI for precise fake news detection. © 2024 IEEE.

关键词： Adversarial machine learning

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Enhancing Cloud Data Sharing Security and Efficiency with Attribute-Based Encryption and Blockchain Integration 8th

Enhancing Cloud Data Sharing Security and Efficiency with ...

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8th International Conference on Inventive Communication and Computational Technologies, ICICCT 2024

作者： Velmurgan, A.K. Aravinth, S.S. Vivek, S. Vineela, P.V.S.T. Aditya, G. Praveen, B. Department of Computer Science and Engineering Koneru Lakshmaiah Educational Foundation Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9789819777099

This paper presents a ground-breaking method that harnesses blockchain technology to establish a highly secure protocol for sharing data in cloud environments. The process begins with a trusted certificate authority, which issues globally unique identity IDs and digital certificates. These certificates are based either on user-provided identity information or attributes sourced from an authority. Simultaneously, an attribute authorization center is responsible for assigning attribute-specific public and private key pairs to authorized users, streamlining attribute distribution. Data intended for sharing undergoes encryption to ensure confidentiality during storage and transmission within the *** a user seeks access to this encrypted data, a decryption process is initiated. The user’s attribute key set is then evaluated against the data’s access criteria, and an attribute-based decryption algorithm generates the necessary decryption key for secure data retrieval. This approach significantly enhances data sharing security and efficiency, even in untrusted environments. Additionally, it offers robust monitoring capabilities to ensure compliance with data access regulations, providing a comprehensive solution for secure cloud data sharing. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cloud security

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Upsampling Algorithm for V-PCC-Coded 3D Point Clouds

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ACM Transactions on Multimedia Computing, Communications and Applications 2024年第12期20卷 1-23页

作者： Lin, Ting-Lan Su, Bing-Wei Shen, Po-Cheng Chen, Ding-Yuan Liang, Chi-Fu Chen, Yan-Cheng Wen, Yangming Shahid, Mohammad Taipei Taiwan Electronic Arts Redwood CityCA United States Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh India

Point cloud (PC) compression is crucial to immersive visual applications such as autonomous vehicles to classify objects on the roads. The Motion Picture Experts Group (MPEG) standardization group has achieved a notable compression efficiency, called video-based PC compression (V-PCC), which consists of an encoder-decoder. The V-PCC encoder takes original 3D PC data and projects them onto multiple 2D planes to generate several 2D feature images. These images are then compressed using the well-established High-Efficiency Video Coding (HEVC) method. The V-PCC decoder uses compressed information and decoding techniques to reconstruct the 3D PC. However, the PCs produced by V-PCC are often sparse, non-uniform, and contain artifacts. In many practical applications, it is necessary to recover complete PCs from partial ones in real time. This article presents a method for enhancing decoded PCs as a post-processing step in the V-PCC with reduced computational time. Our approach involves a 2D upsampling for the V-PCC occupancy image, which increases the density of the PC, and a 2D high-resolution auxiliary information modification algorithm for the 2D-3D conversion of high-resolution 3D PCs, which improves the uniformity and reduces the noise in the PC. The 3D high-resolution PC has been further enhanced using the developed 3D outlier removal and point regeneration algorithm. Our proposed work can significantly simplify the state-of-the-art super resolution methods for PCs and reduce the time complexity of 61-75% while maintaining a high level of quality in PCs. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Motion Picture Experts Group standards

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Impacting of COVID-19 on Stock Market Exchange using ML 8

Impacting of COVID-19 on Stock Market Exchange using ML

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8th International Conference on Inventive Systems and Control, ICISC 202

作者： Srinadh, Bathina Bollepalli, Venkata Rao Rao, G. Siva Nageswara Vinay, Kollu Swami, Panidapu Narayana Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Guntur AP Vaddeswaram India

ISBN: (纸本)9798350386578

This study analyses the impact of COVID-19 pandemic on stock market exchange by using Machine Learning (ML) techniques. The main aim of this study is to understand how the crisis has influenced stock prices and market dynamics. This study proposes a comprehensive methodology to analyse extensive datasets, select relevant machine learning algorithms, and carefully analyse various features and variables. The findings highlight the key trends and patterns on how machine learning models are predicting market movements during the times of crisis. The research implications offer valuable perspectives for investors, policymakers, and researchers. Moreover, this study not only contributes to the understanding of the complex relationship between COVID-19 and stock market behavior but also highlights the efficacy of Machine Learning (ML) in visualizing complex financial dynamics. © 2024 IEEE.

关键词： Challenges for investors COVID-19 pandemic Economic downturn Food Consumption Global financial markets Stock prices Unprecedented impact

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Reviewing Cheating and Detection Techniques in Massively Multiplayer Online Role-Playing Games: A Systematic Analysis

Reviewing Cheating and Detection Techniques in Massively Mul...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Mulakaluri, Tulasidhar Puppala, Ramya Yasmina, Chebrolu Madhavi, Pandi Bindu Balaji K. S, Vijay Guru Koneru Lakshmaiah Education Foundation Department of Computer science and Engineering Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798331540685

In recent years, despite the shift of massively multiplayer online role-playing games (MMORPGs) from PC to mobile platforms, fraudulent and illegal activities persist within the online gaming market. These issues hinder players' immersion and enjoyment of the game, potentially leading to their disengagement from the gaming community, thereby negatively impacting the industry as a whole. This systematic review offers an in-depth examination of existing studies concerning cheating behaviors, countermeasures, and detection methods in MMORPGs. Our focus is on understanding the underlying reasons for cheating behavior within the structural framework of state-full game servers and exploring the fundamental mechanisms of online gaming, particularly within the context of MMORPGs. Through extensive literature research, we have organized and categorized countermeasures and detection methods systematically. The findings of this review contribute to a deeper understanding of effective strategies for managing and combating cheating behaviors within MMORPGs. © 2024 IEEE.

关键词： Anomaly detection

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A Comprehensive Study on Multi-Factor Crop Yield Prediction 8

A Comprehensive Study on Multi-Factor Crop Yield Prediction

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8th International Conference on Inventive Systems and Control, ICISC 202

作者： Saiteja, Paruchuri Sai, Potla Likith Krishna, Goli Leela Venkata Sai Siddhardha, Yalavarthi Joseph, Iwin Thanakumar Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798350386578

This study investigates the complex dynamics affecting crop production prediction using a multi-faceted method that incorporates temperature, rainfall, and pesticide application. With the use of cutting-edge machine learning models including S VR, Random Forest, Lasso, and XGBoost, the study carefully examines how these variables affect agricultural productivity. The yield-to-rainfall ratio is a new statistic established to measure agricultural production efficiency in relation to average rainfall patterns. The generated models are rigorously optimized using grid search approaches, which demonstrate their strong predictive power. The study reveals the complex links between crop yields, climatic conditions, and pesticides through detailed visualizations and correlation analyses. Among the regression models, the XGBoost model performs better than the others and is found to be the best predictor. The findings provide useful insights for maximizing crop output in a range of environmental circumstances and greatly advance our understanding of important factors influencing agricultural productivity. A fresh viewpoint on crop efficiency is offered by the yield-to-rainfall ratio, which makes it easier to identify crops and areas with the highest productivity. © 2024 IEEE.

关键词： Agricultural Productivity Crop Yield Prediction Environmental Optimization Machine Learning Models Pesticides Application Rainfall Patterns Temperature Variations XGBoost Regression Yield-to-Rainfall Ratio

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Quantifying Stellar Energy Exchange: Image Analysis with OpenCV and Machine Learning 5

Quantifying Stellar Energy Exchange: Image Analysis with Ope...

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Venkteswarulu, B. Kanagala, Chaitanya Snehitha Kakumanu, Naga Venkata Sai Mohana Raghavarapu, Laxmi Narasimha Charan Sai Peravali, Himavath Lakshmi Visveswar Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798350361155

Stellar energy exchange, fundamental to the dynamics of celestial systems, remains a captivating yet challenging area of study in astronomy. This paper introduces an innovative methodology for quantifying stellar energy exchange through sophisticated image analysis techniques, harnessing the power of OpenCV and machine learning. Our approach integrates OpenCV's robust image processing capabilities with state-of-the-art machine learning algorithms to automate the intricate task of analyzing astronomical images and predicting energy exchange rates within stellar constellations. The methodology begins with the preprocessing of astronomical images using OpenCV, where radiometric and atmospheric corrections are applied to ensure accurate representations of stellar luminance. Subsequently, features relevant to energy exchange dynamics are extracted from the processed images, employing advanced computer vision techniques. These features encapsulate intricate spatial, spectral, and radiometric characteristics of stars, providing rich representations for subsequent analysis. Machine learning algorithms are then employed to predict the energy exchange rates of stars based on the extracted features. Leveraging the power of convolutional neural networks and ensemble learning techniques, our model learns intricate patterns within the data and delivers accurate estimations of energy exchange dynamics. Through extensive experimentation on real astronomical datasets, our approach demonstrates exceptional performance, achieving precise predictions of energy exchange rates across diverse stellar systems. © 2024 IEEE.

关键词： Spectrum analysis

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Neural Prognostication of Thyroid Carcinoma Recurrence an Interdisciplinary Inquiry into Predictive Modelling and Computational Oncology 8th

Neural Prognostication of Thyroid Carcinoma Recurrence an In...

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8th International Conference on Inventive Communication and Computational Technologies, ICICCT 2024

作者： Sireesha, Ravva Amara Lakshmi Nandini, Kandula Geetha Vyshnavi, Srimathkandala Ch V. S. Bhanu, Pasam Shaik, Mohammed Gouse Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9789819777099

This research study proposes a novel predictive modelling method for detecting thyroid cancer recurrence using a wide range of patient data. The research makes use of a neural network model with a dataset that is loaded with diverse characteristics, including tumour features, medical history, and patient demographics. This neural network can accurately identify challenging correlations in the data, providing precise estimation of the risk of cancer recurrence. The approach is based on the methodology which includes careful feature selection, proper preprocessing techniques, and purposeful oversampling using Synthetic Minority Oversampling Technique (SMOTE) to reduce the class imbalance. With the neural network model, remarkable test accuracy is achieved. The findings revealed new information about the future therapeutic applications of the proposed technique and demonstrated its efficacy. The paper concludes by suggesting prospective ways for further research and establishing the framework for future advancements in the field of computational oncology. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Neural network models

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UAV Based YOLOV-8 Optimization Technique to Detect the Small Size and High Speed Drone in Different Light Conditions 2

UAV Based YOLOV-8 Optimization Technique to Detect the Small...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Sen, Chelsi Singh, Pranita Gupta, Keshav Jain, Amit Kumar Jain, Arpit Jain, Abhishek Ajay Kumar Garg Engineering College Department of It Ghaziabad India Roorkee Institute of Technology Department of Computer Science and Engineering Roorkee India Grd Institute of Management and Technology Department of Computer Science and Engineering Dehradun India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaddeswaram India Uttaranchal University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350371055

Public safety and other sectors are paying more and more attention to uncrewed aerial vehicle ( UA V) identification as UAVs become more commonplace in commercial and industrial settings. Methods for object detection by UAVs are progressing rapidly, too. Nevertheless, researchers still need to overcome substantial obstacles in this field because of the tiny size of drones, complicated airspace backdrops, and fluctuating light conditions. Addressing these issues, this work suggests a small UAV detection approach that utilizes the improved YOLOv8. The first step is to upgrade the device's recognition capabilities for small marks by addition a high-resolution recognition head According to the study, the YOLOv8 network's large target prediction layer, feature extraction, and fusion layers are eliminated. Only four, eight, or sixteen-sampled feature maps are stored for UAV prediction. the upgraded network architecture, which feeds feature maps from the third C2f layer straight into SPPF for multi-scale feature extraction using 16-times down-sampling. After leaving the Upsample-ConcatC2f module, fused feature maps are immediately attached to the next component. Our approach boosts performance compared to the baseline model by 11.9% for accuracy, 15.2% for recall, and 9% for mean average precision (MAP). The model size is decreased by 57.9%, and the number of limits is decreased by 60%. Also, our approach is better suited for engineering deployment and real-world UAV object recognition system applications, and it shows apparent benefits in comparative studies and experiments using self-built datasets. © 2024 IEEE.

关键词： Accuracy Mean Average Precision Recall Uncrewed Aerial Vehicle YOLOv8

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Synergistic Genomic Profiling and Predictive Modeling for Health Outcome Anticipation: A Holistic Strategy Leveraging Gene Expression Analysis 4

Synergistic Genomic Profiling and Predictive Modeling for He...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Krishna, Nerella Venkata Anil Rama Devendra, Vicharapu Krishna, Dogiparthi Leela Srikanth, Godavari Haritha, D. Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798350349900

This paper presents a thorough way to disease prediction that integrates powerful machine learning algorithms with microarray analysis of cancer gene expression. The loading, recording, and cleaning of genetic microarray data is the primary stage in the study's approach. A collection of genes crucial for illness prediction is discovered when traits are chosen based on statistical significance. The research evaluates how many classifiers, such as Gaussian Naive Bayes, Decision Tree, K-Nearest Neighbors, Multi-layer Perceptron, and Extra Trees, are influenced by increasing the number of individual genes. The selecting of essential genes and models is lead by extensive evaluation methodologies and crossvalidation procedures. The research also provides a theoretical framework, underlining the relevance of gene expression patterns in understanding and identifying disorders. The relevance of feature selection and how it influences model performance are underlined in the research. It also looks at the selected strategies and how well they function with genetic data management. The methodology's success is proved by a comparative analysis of error rates and the choice of the bestperforming software and gene set. © 2024 IEEE.

关键词： Diseases

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