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Research Square 2023年

作者： Jha, Rajiv Rani, Richa Rajput, Monali Dept. of Computer Application Vivekanand Education Society’s Institute of Technology Mumbai India

In the game of football (soccer), evaluation of players for transfers, scouting, team building, and strategic planning are essential. Due to the huge pool of grassroots players, brief career span, varying performance over the course of a person's career, various play circumstances, positions, and variable club resources, it is difficult to determine a player's total performance *** order to approach this difficult topic analytically, our Player Performance Prediction System draws knowledge from a range of football player abilities and traits. It takes into account the football player's skill set values and forecasts a performance value that shows the player's potential for progress as well as his or her *** objective of this system is to help team managers and coaches both at the amateur and professional levels find the football players who have the best chance of succeeding in the future without being influenced by arbitrary variables like club finances, league competition, or player importance © 2023, CC BY.

关键词： Machine learning

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Classification of Pulmonary Tuberculosis using Mathematical Modeling and Machine Learning 1

Classification of Pulmonary Tuberculosis using Mathematical ...

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1st International Conference on Machine Learning, computer Systems and Security, MLCSS 2022

作者： Das, Subhalaxmi Pradhan, Sateesh Kumar Mishra, Sujogya Patra, Narayan Pradhan, Sipali Utkal University Department of Computer Science and Application Bhubaneswar India Odisha University of Technology and Research Department of Mathematics Odisha Bhubaneswar India SoA Deemed to Be University Dept. of Computer Science & Information Technology Bhubaneswar India Rbvrr Women's College Dept. of Computer Science Hyderabad India

ISBN: (纸本)9781665454933

Many research works have been conducted and developed to diagnose pulmonary Tuberculosis in recent years. There are thousands of common symptoms for several diseases, making it very difficult to find which symptoms are responsible for pulmonary Tuberculosis. In this paper, we have proposed a comparative analysis between local covering technique and the support confidence method to generate an association rule that gives correct information regarding symptoms of pulmonary Tuberculosis. We have validated our claim using the chi-square test. Then K- Nearest Neighbor (KNN), Support Vector Machines (SVM), and Logistic Regression were used to classify the presence or absence of pulmonary tuberculosis in the chest x-ray dataset which is available at Kaggle. © 2022 IEEE.

关键词： Support vector machines

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Exploring Deep Learning Techniques for Automated Cataract Detection in Digital Camera Imagery

Exploring Deep Learning Techniques for Automated Cataract De...

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Advances in Modern Age Technologies for Health and Engineering Science (AMATHE), International Conference on

作者： Sujatha Kamepalli Veeranjaneyulu N Jyostna Devi Bodapati Dept. Department of Information Technology And Computer Application Vignan's Foundation for Science Technology and Research Guntur India Dept. Department of Advnced Computer Science and Engineering Vignan's Foundation for Science Technology and Research Guntur India

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371574

Cataracts, a prevalent eye condition impacting vision, demand accurate and prompt identification for effective risk reduction and blindness prevention. Traditional methods of cataract diagnosis rely heavily on manual examination by ophthalmologists, which can be time-consuming and subject to human error. With the advent of digital imaging technology, there is an opportunity to enhance the efficiency of cataract detection. The deep neural network models VGG19 and ResNet-50 are recommended in this paper for automatic cataract identification on digital camera images. To optimize network performance, adjustments to loss and activation functions were made, incorporating minimal kernels, reduced training parameters, and layers. This resulted in a ResNet-50 model with significantly decreased computational cost and average operating time compared to the cutting-edge models. The proposed ResNet 50 model achieved an average accuracy of 93.41%, outperforming the VGG 19 model, which had an accuracy of 91.13%. This research contributes to the evolution of cataract detection methodologies, emphasizing the potential of deep learning models, particularly ResNet-50, in improving diagnostic accuracy and operational efficiency for enhanced eye health outcomes.

关键词： Cataracts Deep learning Training Accuracy Computational modeling Prevention and mitigation Medical services

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Efficient Human Detection in Real- Time Using Vision Transformers: Balancing Performance and Latency for Security and Autonomous applications

Efficient Human Detection in Real- Time Using Vision Transfo...

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Next Generation Communication & Information Processing (INCIP), International Conference on

作者： Jeremiah Ocheni Immanuel Johnraja Jebadurai Getzi Jeba Leelipushpam Paulraj Difference Finbarrs Dept. of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India Dept. of Business Admin and Computer Application Rathinam College of Arts and Sciences Coimbatore Tamil Nadu India

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

With applications in security and surveillance, au-tonomous driving, human-computer interaction, the application of Vision Transformers (ViTs) is a major breakthrough in deep learning techniques, outperforming more conventional methods such as Convolutional Neural Networks (CNNs). Human detection is an essential component of computer vision. Using a custom dataset, this paper analyses the use of Vision Transformers (ViT) for human detection. We introduce a thorough approach to training and evaluating models that makes use of PyTorch. To strike a balance between the computational efficiency and performance with balancing accuracy, and the latency as part of the study to evaluate the effectiveness of the approach in real-time applications. The findings show that Vision transformer model with a cross-entropy loss function and the Adam optimizer can attain competitive performance in terms of accuracy 84% and minimal latency of 0.11 secs, and Equal Error Rate of 0.1845 indicating that it has potential uses in human detection and can be use in the real time applications.

关键词： Deep learning computer vision Visualization Accuracy Computational modeling Transformers Real-time systems Computational efficiency Security Convolutional neural networks

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Using Ant Colony Optimised Algorithms for Shortest Path Exploration by Robots

Using Ant Colony Optimised Algorithms for Shortest Path Expl...

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Interdisciplinary Approaches in technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Aashdeep Singh Sakshi Dhawan Jyoti Verma Gurpreet Singh Amanpreet Kaur Dept. of Computer Science & Engineering Punjab Institute of Technology Rajpura (MRSPTU Bathinda) Rajpura Punjab India Dept. of Computer Application Punjab Institute of Technology Rajpura (MRSPTU Bathinda) Rajpura Punjab India Dept. of Computer Science & Engineering Chitkara University Institute of Engineering & Technology Rajpura Punjab India

In the realm of robotics and autonomous systems, efficient path planning is a critical aspect for optimizing resource utilization and achieving mission objectives. This study explores the application of Ant Colony Optimized (ACO) algorithms to enhance the efficiency of robots in navigating and exploring unknown environments. Inspired by the foraging behavior of ants, ACO algorithms are employed to find the shortest paths by leveraging pheromone communication and decentralized decision-making. Ant Colony Optimization is then introduced as a bio-inspired solution to address these challenges. The experimental results demonstrate the capability of the ACO algorithm to efficiently explore complex environments, adapt to changes in the surroundings, and converge to optimal paths. Insights gained from these analyses contribute to the refinement of the ACO algorithm for diverse robotic applications. The findings of this research have practical implications for the development of autonomous robots operating in real-world scenarios, such as search and rescue missions, environmental monitoring, and surveillance. This study provides a valuable foundation for the integration of bio-inspired algorithms in robotics, advancing the state-of-the-art in autonomous exploration and path planning.

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Industrial IoT Smart Manufacturing Systems based on Cloud Computing Energy Efficiency Analysis using Machine Learning Algorithms 3

Industrial IoT Smart Manufacturing Systems based on Cloud Co...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Chinnasamy, P. Khan, Patan Feridoz Sasikala, C. Swathi, Mavalluru Kumar, M. Prema Yadav, Ajay Kumar Kalasalingam Academy of Research and Education School of Computing Department of Computer Science and Engineering Tamilnadu India Mohan Babu University School of Computing Department of Computer Science and Engineering Andhra Pradesh Tirupati India Dept of Cse Andhra Pradesh Ananthapuramu India Audisankara College of Engineering and Technology Andhra Pradesh India Dept. of Ece Andhra Pradesh Bhimavaram India United Institute of Management Department of Computer Application Prayagraj India

ISBN: (纸本)9798331509675

With open connectivity and emergent computing, the Industrial Internet of Things (IIoT) combined with cloud computing resources provides significant breakthroughs in the field of industrial automation. These developments lead to the evolution of supplemental services, which improve automation in the production and industrial sectors. This research proposes novel technique in smart manufacturing system based on cloud computing with IIoT in energy efficiency using machine learning. Here the smart manufacturing process has been analysed based on IIoT with cloud computing network, where the energy efficiency is analysed using virtual machine-based reinforcement markov chain based linear regression. Experimental analysis has been carried out in terms of latency, energy efficiency, task response time, accuracy. proposed technique obtained 97% of Latency, 95% of Energy efficiency, 98% of Accuracy, 94% of Task response time. © 2025 IEEE.

关键词： Smart manufacturing

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Designing Blockchain Based Consensus Mechanism for Smart Healthcare IoT

Designing Blockchain Based Consensus Mechanism for Smart Hea...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Trishla Kumari Rakesh Kumar Rajendra Kumar Dwivedi Dept. of Computer Science and Engineering Madan Mohan Malviya University of Technology Gorakhpur India Dept. of Information Technology and Computer Application Madan Mohan Malviya University of Technology Gorakhpur India

In today's world, healthcare system is improving day by day by using smart IoT based system. Blockchain based system is totally a decentralized system due to which attackers are unable to access the confidential records easily. Blockchain play an important role in controlling and management of sensitive information in healthcare system and insurance sector. Consensus mechanisms used in blockchain technology to validate whichever block is going to be added next in the blockchain network. In this paper we proposed a hybrid based system which solves trust issues arises in the blockchain. Hybrid consensus mechanism provides reputation according to the activities of blocks in a blockchain network. Four layer architecture is designed in Hybrid model. Five algorithms are used to create, validate, fork handling, reward and punishment scheme and Markle tree is constructed respectively. This model is implemented on Ethereum based platform which shows that HCM model is better than a PoXR model as it consume less memory (50 percent) and consume less CPU(below 2 percent).

关键词： Computational modeling Memory management Insurance Medical services Consensus protocol

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Heart Disease Detection Using Machine Learning Models

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Procedia computer Science 2024年 235卷 937-947页

作者： Amrit Singh Harisankar Mahapatra Anil Kumar Biswal Madhumita Mahapatra Debabrata Singh Milan Samantaray Department of Computer Science and Engineering NIST Institute of Science and Technology (Autonomous) Berhampur India Dept. of Computer Science Udayanath Autonomous College of Science and Technology Cuttack India Dept. of Computer Science and Engineering Delhi Technical Campus Greater Noida UP India Department of Computer Application Institute of Technical Education and Research (ITER) Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar India Department of Computer Science and Engineering Silicon Institute of Technology Bhubaneswar India

An enormous number of deaths occur every year as a result of heart disease, making it a major concern in world health. Improving patient outcomes and lowering death rates, early detection and correct diagnosis of cardiac disease play a key role. When the heart's arteries become blocked, oxygen-poor blood cannot reach the heart properly, resulting in coronary heart disease. Early detection of cardiac disease is viable because it reduces medical costs and potentially saves the patient's life. Recently presented methods have improved heart failure detection accuracy on testing data without sacrificing accuracy on training data, yet most of these algorithms are suffering from the issue of overfitting. Models that were created end up fitting the test data too well. In this study, we create a novel diagnostic system to address this issue, and the resulting system demonstrates high intelligence and excellent performance on both training and testing data. Machine learning (ML) algorithms have demonstrated promising potential in assisting healthcare professionals with timely and accurate diagnosis. In this paper, is based on supervised machine learning methods are decision tree (DT), random forest (RF), Support vector Machine (SVM), Principal Component Analysis(PCA). We compare their accuracy with each other by using bar plot.

关键词： Coronary Heart Disease Blood Pressure Health Care Machine Learning Imbalanced Data Decision Tree Random Forest SVM PCA

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TWIRL: A Steady Device to Detect the Malicious Fragment Using Various Machine Learning Approaches

TWIRL: A Steady Device to Detect the Malicious Fragment Usin...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Poojarini Mitra Suseta Datta Subhasree Bhattacharjee Pritam Kundu Dept. of CSE - Data Science Techno Main Salt Lake Kolkata India Dept. of Computer Application Narula Institute of Technology Kolkata India Dept. of AI Cloud Tata Consultancy Services Ltd. Kolkata India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Malware has become a major worry as the use of platforms in sensitive applications such as banking has grown. The arduousness of apprehending such malware engenders a range of challenges due to the inhibited resources available and the circumscribed skills supplied to the utilizer, but it also offers up incipient possibilities in the needed information associated with each entreaty. Malware detection predicated on machine learning algorithms is widely used to forfend and bulwark unique data such as personal portal passwords, personal information, and online transactions. Machine learning classifiers are successful at detecting and ceasing malware assaults. This paper describes some machine learning algorithms that are trained to distinguish between legitimate setup files and malware samples. Some error values have been quantified based on malware samples and legitimate setup files, and 99.99% of endeavor precision has been computed, primarily predicated on the discombobulation matrix of a finely geared-up approach. To make this project more user-friendly, a tool has been constructed as an interactive and dynamic web page. In the raw dataset, attributes that distinguish malware domains from regular domains have been used as examples. The malware sample in the body of the message, including the logo and other legitimate content, is designed to appear to be heading for a fraudulent company.

关键词： Radio frequency Machine learning algorithms Accuracy Data visualization Web pages Tag clouds Malware Tokenization Random forests Portals

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Employing Sequence-to-Sequence Stacked LSTM Autoencoder Architecture to Forecast Indian Weather

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Procedia computer Science 2024年 235卷 2258-2268页

作者： Arpita Maharatha Ratnakar Das Jibitesh Mishra Soumya Ranjan Nayak Srinivas Aluvala Dept. of Computer Science & Application Odisha University of Technology and Research Bhubaneswar 751029 India School of Computer Engineering KIIT Deemed to be University Bhubaneswar 751024 Odisha India Department of Computer science and artificial intelligence SR University Warangal Telangana -506371 India

One of the buzz words in current research depends for a weather forecast is the atmospheric temperature, and this value may change at any time. Predicting air temperature exactly is necessary since it is particularly important for various communities and events. In today’s world of massive amounts of data and unforeseen data fluctuations, long-short term memory (LSTM), a sort of neural network method, is becoming more and more popular. The present work employed a sequence to sequence LSTM autoencoder to forecast Indian weather patterns. The prediction model was trained using historical data obtained from the Khordha district administration. The proposed method primarily emphasizes two phases of the procedure. The present study involves an examination of a proposed model, wherein a comparison is made between four distinct variants of LSTM techniques: vanilla LSTM, stacked LSTM, bidirectional LSTM, and convLSTM. The objective of this analysis is to predict temperature patterns. Additionally, this paper utilizes an LSTM autoencoder model to forecast the temperature for the subsequent two-year period in the Khordha district of Odisha, India. The experimental investigation demonstrates that the utilization of Stacking encoder-decoders with bidirectional LSTM cells leads to a notable enhancement in the accuracy of the model, resulting in maximum efficacy. A notably lower Mean Absolute Error (MAE) result suggests that the 9-layer stacked autoencoder model has superior performance in predicting both the minimum and maximum temperature.

关键词： LSTM CNN Weather Forecasting Temperature

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