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An Empirical Study of Machine Learning Techniques for ODI Bowler Performance Evaluation 4

An Empirical Study of Machine Learning Techniques for ODI Bo...

引用

4th International Conference on computer, Communication, Control and Information technology, C3IT 2024

作者： Das, Biswaranjan Das, Nayan Ranjan Mondal, Bappaditya Sur, Ratul Paul, Sushmita Das, Basudha Maity, Sutanu Academy of Technology Department of Engineering Science & Humanities West Bengal Hooghly712121 India Academy of Technology Department of Computer Science & Engineering West Bengal Hooghly712121 India Academy of Technology Department of Computer Science & Business Systems West Bengal Hooghly712121 India

ISBN: (纸本)9798350389128

The objective of the paper is to construct an innovative ranking system based on various Machine Learning (ML) techniques specifically tailored for One Day International (ODI) male players. It aims to create a recommender system that produces bowler rankings closely aligned with the International Cricket Council (ICC) Rankings, with a focus on enhancing accuracy and reliability. The dataset encompasses a wide range of match parameters, performance metrics, and player statistics, providing a comprehensive foundation for insightful analysis. By employing data-driven techniques, the paper aims to highlight key performance indicators that significantly influence bowler rankings. We performed an analysis among five machine learning models that can produce rankings of bowlers and select the best model whose predictions are closest to the ICC's official rankings. The rankings obtained from the five machine learning algorithms have been compared with ICC rankings utilizing the Spearman Rank Correlation Coefficient. The results highlight that the Artificial Neural Networks (ANN) exhibit the best outcomes with a rank correlation of 0.7550. © 2024 IEEE.

关键词： Adversarial machine learning

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Securing Secret Information 1st

Securing Secret Information

引用

1st International Conference on Sci/Tech and engineering, ICSTE 2023

作者： Shamsi, Zeba Singh, Laiphrakpam Dolendro Department of Computer Science and Engineering National Institute of Technology Assam Silchar788010 India

ISBN: (纸本)9789819947126

The art of information concealment has become a crucial subject since information security has grown to be a primary concern in the online world. Using cryptography and steganography plays a significant role in secure data transfer. Steganography is a covert writing technique;the message is contained behind a cover medium. Cryptography encrypts messages to hide their contents. Two-dimensional bar codes called QR codes can store text strings. They can encode data both vertically and horizontally, encoding more information. By combining the ideas of cryptography, steganography, and QR codes, an innovative method for covert communication is proposed in this paper. Statistical and security analyses of the proposed method show that the proposed method provides better imperceptibility, security, and robustness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Steganography

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Deep Learning and Dynamic BPSO for Road Accident Severity Prediction using Twitter Data 15

Deep Learning and Dynamic BPSO for Road Accident Severity Pr...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Raul, Sanjib Kumar Rout, Rashmi Ranjan Somayajulu, Dvln National Institute of Technology Department of Computer Science and Engineering Telangana Warangal506004 India

ISBN: (纸本)9798350370249

The traffic and medical authorities utilize accident severity prediction through social media postings for better administration and robust emergency response. Deep learning-based models can be used for accident severity prediction using social media data. However, most of the deep learning methods can only perform well when trained with an optimal set of hyper-parameters. This necessitates the use of meta-heuristics in conjunction with deep learning. Moreover, the increase in depth of the networks warrants partial training of the network to make the training process efficient. In this work, we propose a novel partial training-based hybrid deep neural network on convolutional neural networks (CNN) and long short-term memory (LSTM), which is partially trained, and the hyperparameters are optimized using a dynamic version of binary particle swarm optimization (DBPSO) known as accident tweet hybrid deep dynamic BPSO (AT-HDBPSO). Experiments show that the proposed AT-HDBPSO turns out to be efficient in training compared to traditional deep learning-based training. Finally, the proposed method outperforms other baseline and state-of-the-art techniques in prediction accuracy. © 2024 IEEE.

关键词： Tweets

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A Key-Value pair Schema based Message Digest-5 Hash Algorithm for the Dynamic Data Masking 2

A Key-Value pair Schema based Message Digest-5 Hash Algorith...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Gowda, Bharath Tumkur Shankare Raju, Channa Krishna Sri Siddhartha Institute of Technology Department of Computer Science and Engineering Maraluru Tumkur India

ISBN: (纸本)9798350373110

Dynamic data masking (DDM) is an efficient data security system for protecting personal, as well as other data from attackers or third parties. Reversible Data Hiding (RDH) is a scheme that hides secret data in a cover image through steganography images. However, when hiding a large amount of data in an embedding process, the data can be lost, leading to security problems. Hence, this research proposes the Key-Value pair schema-based Message Digest-5 (MD5) algorithm for dynamic data masking. The MD5 algorithm produces fixed size message digest of 128-bit and makes the process irreversible, thereby enhancing the security level over unauthorized users. Data masking is introduced on selected database areas to hide sensitive data in the group of query outcomes. The proposed Key-Value pair schema-based MD5 approach embeds one or multiple secret attributes about various hiding attributes in a similar relational database. The proposed method is validated using various performance metrics like Peak Signal-to-Noise Ratio (PSNR) of 8.383dB and Embedding Rate of 0.0065, as opposed to the existing methods, Quantum Particle Swarm Optimization (QPSO) and Multi-direction Pixel Swapping Approach (MPSA). © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Deep Learning for Fraud Detection in Bitcoin Transactions: An AI Approach

Deep Learning for Fraud Detection in Bitcoin Transactions: A...

引用

2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Shravan, M. Ajitha, E. Arnone, Gioia Renu, Nekkanti Tiwari, Mohit Chouhan, Kuldeep Sr University School of Business Telangana Warangal India St. Joseph's Institute of Technology Computer Science and Engineering India Vrije Universiteit Brussel Department of Private and Economic Law Bruxelles Belgium Department of Computer Science and Engineering Duvvada India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi Delhi India Sharda University Sharda School of Engineering and Technology Department of Computer Science and Applications Greater Noida201310 India

ISBN: (纸本)9798331508685

This research looks into how deep learning methods can be used to find fake Bitcoin transfers. It's mostly about the unique problems and chances that come with trading in cryptocurrencies and blockchain. Deep learning models are used to look at transaction trends and find oddities that could be signs of fraud. This is because Bitcoin's open structure makes it harder to use traditional methods of fraud detection. We offer a structure that connects CNNs (convolutional neural networks) and RNNs (recurrent neural networks) so that strange patterns can be found in big sets of blockchain transactions. An AI-driven method aims to improve detection accuracy while lowering false positives by taking on scammers' complicated and always-changing strategies. Experienced researchers have shown that the model is good at finding strange things, which means it can be used to safely stop theft in Bitcoin networks in real time. It looks like deep learning could make digital money safer and give us data that can be used in bigger blockchain-based money systems. © 2025 IEEE.

关键词： Adversarial machine learning

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Enhancing Spatial Steganography for Optimal Data Concealment with Image Quality Preservation

Enhancing Spatial Steganography for Optimal Data Concealment...

引用

2024 International Conference on Recent Innovation in Smart and Sustainable technology, ICRISST 2024

作者： Padma, R. Yendapalli, Vamsidhar Gitam University Gitam School of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350309140

In the domain of steganography, the act of concealing data within an image poses a significant challengebalancing between the precision of the carrier image and the capacity for embedding information. This article introduces an innovative spatial steganography approach employing evolutionary algorithms. Proposed method introduces novel approaches to strengthen the matching of the LSB between the carrier and the concealed image. This process leads to an up-scaled concealing efficiency while minimizing deformation. The technique involved optimized pixel traversal in both row and column wise directions, pixel relocation, pixel swapping confidential bits, and inverting confidential data. A generic steganography model rooted on evolutionary algorithms is formulated to explore optimal solutions. Experimental investigations of the RECS [11] and SDID [16] method confirm that the performance of the proposed method is comparatively better than the traditional methods in terms of higher Embedding rate by 29%, PSNR by 19%, and SSIM by 15% respectively. © 2024 IEEE.

关键词： Embeddings

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Multi-classifier approach to detect Alzheimer's Disease based on Handwriting Analysis 3

Multi-classifier approach to detect Alzheimer's Disease base...

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Dadhania, Jatan K. Chaudhuri, Abhilasha Sardar Vallabhbhai National Institute of Technology Department of Computer Science and Engineering Surat India

ISBN: (纸本)9798350384598

Alzheimer's disease (AD) is a degenerative brain condition that predominantly impacts older individuals, resulting in a gradual decline in cognitive function and memory. Effective management and treatment of AD rely heavily on early detection. In this study, we propose a machine learning (ML) model for predicting AD using a dataset of handwriting tasks performed by test subjects with varying degrees of cognitive impairment. Handwriting analysis has shown potential as a low-cost, non-invasive method for early AD identification. These data were used to train various ML classifiers to classify individuals as either AD or non-AD. Baseline evaluations were conducted using multiple classifiers on the entire feature set and on individual tasks. The insights gained from these evaluations were then utilized to create a more optimized model combining the strength of multiple classifiers with the help of the TOPSIS method. The optimized model achieved a promising accuracy of 97% on the test set, demonstrating its potential for early AD detection. Our findings suggest that handwriting analysis, coupled with ML techniques, holds significant promise as a tool for early AD prediction and warrants further investigation. © 2024 IEEE.

关键词： Adversarial machine learning

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Security in VANETs with Insider Attack Resistance and Signature Aggregation 2nd

Security in VANETs with Insider Attack Resistance and Sig...

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2nd International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2023

作者： Lode, Vijaya Lasushe, Kekhelo Pinapati, Anil Department of Computer Science and Engineering National Institute of Technology Kerala Calicut673601 India

ISBN: (纸本)9789819995202

Vehicular Ad-hoc Networks (VANETs) are a type of network in which vehicles communicate with one another and exchange information so as to provide quality-of-life improvements to the vehicle users as well as the people belonging to the area. In VANET, vehicles share information such as the current status of the vehicle, the status of the traffic or the status of the road conditions to the other vehicles. All this information requires the network to be highly secure. Therefore various schemes have been proposed to secure the network. However, they suffer when a receiver has to verify multiple incoming message signatures. To reduce the verifying time and size of the signature The proposed scheme provides an efficient pairing-free aggregate signature, it will verify multiple messages by combining multiple signatures into a single signature called aggregate signature, and it is also resistant to insider attacks without using the tamper-proof device. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Vehicular ad hoc networks

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Enhancing Elephant Emotion Detection Using YOLOv5, YOLOv8, and YOLOv9: A Study on Performance and Explainability

Enhancing Elephant Emotion Detection Using YOLOv5, YOLOv8, a...

引用

2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Subair, Rubiya Benedict, Shajulin Indian Institute of Information Technology Kottayam Department of Computer Science and Engineering Kottayam India

ISBN: (纸本)9798331540821

Elephant emotion detection has marked a crucial component owing to the increased casualties due to human-elephant conflicts. The difficulty has increased due to the lack of algorithms that quickly identify emotions with explainable features. This paper proposes a holistic architecture that detects the emotions of elephants using YOLOv5, YOLOv8, and YOLOv9 models, The study aims to integrate explainable AI features such as Eigen-CAM to manifest the training and testing capabilities of models. In addition, we evaluated the capability of the proposed system using labeled datasets that distinguish elephant emotions to angry, happy, and sad. Our experimental results, conducted at the IoT Cloud Research laboratory, revealed training accuracy of 79.3% for YOLOv5, 79.5% for YOLOv8, and 81.1 % for YOLOv9 and test accuracy of 93%, 95%, and 95% for YOLOv5, YOLOv8, and YOLOv9. Additionally, we recorded the impact of resolutions while performing model evaluations. The observations learned from the experiments will benefit AI-assisted machines to detect elephant emotions that can be utilized by mahouts or people living near forest locations. © 2024 IEEE.

关键词： Elephant Emotion Explainable AI Yolo V5 Yolo V8 Yolo V9

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IoT-Based Pesticide Detection in Fruits and Vegetables Using Hyperspectral Imaging and Deep Learning 4th

IoT-Based Pesticide Detection in Fruits and Vegetables Usi...

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4th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2023

作者： Augustin, Anju Kiliroor, Cinu C. Department of Computer Science and Engineering Indian Institute of Information Technology Kottayam Kottayam India

ISBN: (纸本)9783031488870

Fruits and vegetables contain rich nutrients and vitamins. So that they are part of our daily diet. For proper cell growth and health, we need these nutrients. Today in most crops during their growth and post-harvesting preservation different kinds of pesticides were used. Normal usage of such pesticides not that much affects health. But the actual situation is beyond our control. From soil preparation to the post-harvesting stage, pesticides are being added at alarming rates. It affects our health in a harmful way and leads to major health issues. Various studies exist to detect the pesticide levels in fruits and vegetables. This article analyses different existing methods of pesticide detection and examines their features and problems. Through this study, it is understood that Hyperspectral Imaging (HSI) is a very good method, and with it, more accurate results can be obtained by Transfer Learning (few-shot learning). This paper proposes an architecture and algorithm based on HSI and few-shot learning. Future studies are needed in this area to convert an RGB image to a spectral image because the HSI device is very expensive. © 2024, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Nutrients

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