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

作者： Sekhar, J Chandra Prasad, Arun B. Kanade, Shantanu Kanade, Anuradha Dhilipan, C. Bala, B Kiran Nri Institute of Technology Guntur India Institute of Law Nirma University Ahmedabad India Symbiosis International University Symbiosis School for online and Digital Learning Maharashtra Pune India Mit World Peace University School of Computer Science and Application Maharashtra Pune India Jain Deemed to be University Cms Business School Bangalore India College of Engineering Department of Ai & Ds Trichy India

ISBN: (纸本)9798350370249

Blockchain technology has revolutionized various industries by introducing decentralized and secure systems. Smart contracts, self-executing contracts with the terms of the agreement directly written into code, represent a significant advancement in automating and enhancing the efficiency of financial agreements. This paper proposes an evolutionary algorithm approach to further optimize the implementation of smart contracts on the blockchain for automated financial agreements. The evolutionary algorithm, inspired by the principles of natural selection and genetic algorithms, aims to enhance the adaptability and performance of smart contracts over time. Through a continuous process of selection, crossover, and mutation, the algorithm evolves the codebase of smart contracts to better suit the dynamic and evolving nature of financial markets. This approach not only improves the efficiency of automated financial agreements but also enables smart contracts to adapt to changing market conditions, regulatory requirements, and user preferences. Furthermore, the security implications and challenges associated with the evolutionary algorithm are examined, along with potential strategies for mitigating risks. In conclusion, the integration of evolutionary algorithms with blockchain-enabled smart contracts represents a promising avenue for advancing the automation of financial agreements. This approach not only addresses the challenges of adaptability and optimization but also contributes to the ongoing evolution of blockchain technology in the realm of decentralized finance. The findings of this research have implications for financial institutions, regulators, and developers seeking to leverage the full potential of smart contracts in an ever-changing financial landscape. © 2024 IEEE.

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A Comprehensive Approach to Identify Stroke based on Real-time Medical Data

A Comprehensive Approach to Identify Stroke based on Real-ti...

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Cognitive Robotics and Intelligent Systems (ICC - ROBINS), International Conference on

作者： M. Maragatharajan Shanmugapriya S M. B. Sudhan Deeptha R. Sankar Muthu Paramasivam School of Computing Science and Engineering VIT Bhopal University Kothrikalan Madhya Pradesh India Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Srivilliputhur Tamilnadu India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai Tamilnadu Department of Information Technology SRM Institute of Science and Technology Ramapuram Chennai Tamilnadu India Distinguished Software Engineer Palo Alto Networks Inc Santa Clara California USA

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372755

Strokes or cardiovascular disorders continue to be a major global health concern. Proactive intervention and timely detection greatly reduce the morbidity and death related to strokes. The goal of this research is to build a prediction model using machine learning techniques to assess a person’s risk of stroke based on a variety of sociodemographic and health characteristics. The dataset used in this study consists of a large collection of medical records. It contains important details including age, gender, BMI, smoking habits, heart disease, hypertension, and the type of residential property. Using this information, a Decision Tree Classifier is trained to predict the likelihood of strokes, enabling early detection and customized treatment plans. A Decision Tree Classifier with a predetermined maximum depth is used in the proposed methodology’s rigorous data preprocessing, which includes handling missing values, encoding categorical features, and developing models. Both the training and testing datasets are used to evaluate the model’s performance using evaluation measures including accuracy, precision, and recall. The model’s insights provide important characteristics that influence the prediction of stroke risk, assisting medical professionals in focused risk assessment and preventive intervention. The proposed method has the potential to improve healthcare practices by facilitating prompt interventions and personalized treatment for stroke risk individuals. The proposed model has resulted in 86.78% accuracy for this dataset by using the traditional machine learning algorithms. This work highlights the value of predictive modelling in the healthcare industry by demonstrating how it may improve early detection techniques and lessen the impact of strokes on public health.

关键词： Training Machine learning algorithms Machine learning Predictive models Stroke (medical condition) Real-time systems Risk management

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Customers' sentiment on food delivery services: An Arabic text mining approach

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International Journal of Information Management Data Insights 2024年第2期4卷

作者： Mustafa, Dheya Khabour, Safaa M. Shatnawi, Ahmed S. Department of Computer Engineering Faculty of Engineering The Hashemite University Zarqa 13133 Jordan Department of Information Systems Faculty of Information Technology and Computer Sciences Yarmouk University Irbid 21163 Jordan Department of Software Engineering Jordan University of Science & Technology Irbid 21110 Jordan

The Covid-19 pandemic has accelerated the shift in organizations' strategies toward innovative online services. Customer reviews on platforms for online ordering and delivery are a vital source of information about how well a business is performing. Businesses that provide food delivery services (FDS) seek to leverage consumer input to locate areas where customer satisfaction could be raised. Sentiment analysis (SA) has been the subject of an enormous amount of English-language research. Despite Arabic's increasing popularity as a writing language on the Internet, not much study has been conducted on sentiment analysis of Arabic up to this point, with a limited number of publicly available resources for Arabic SA such as datasets and lexicons. The present study collects FDS-related reviews in Arabic to conduct extensive emotion mining, taking advantage of Natural Language Processing, feature selection, and Machine Learning techniques to elicit personal judgments, identify polarity, and recognize customers’ feelings in the FDS domain. To demonstrate that the proposed approach is suitable for analyzing human perceptions of FDS, we designed and carried out excessive experiments that assess the utility of each phase. Our highest categorization accuracy was 90 % using Mutual Information with the SVM classifier. The study's findings provide various managerial insights for improving their plans and service delivery, as well as revealing the main reasons for consumer complaints. It also demonstrates how future academics might harness the power of online business reviews in Arabic using a variety of text-mining approaches. © 2024 The Author(s)

关键词： Arabic dialects Arabic sentiments resources Consumer sentiment Features selection Food delivery services Machine learning

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Comparative Analysis to identify the best Classifier for Parkinson Prediction 2

Comparative Analysis to identify the best Classifier for Par...

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2nd International Conference on Smart Electronics and Communication, ICOSEC 2021

作者： Shamrat, F.M. Javed Mehedi Bhowmik, Shohag Kumar Sultana, Zakia Hossain, Ahbab Amina, Mahdia Thapa, Sittal Daffodil International University Department of Software Engineering Dhaka Bangladesh Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh University of Liberal Arts Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh Punjab Agricultural University Department of Plant Breeding and Genetics Ludhiana India

ISBN: (纸本)9781665433686

Parkinson disease has become one of the most common diseases among people over the age of 65. Neurodegenerative disease affects movement, speech and other cognitive abilities. Among patients, the symptoms vary at a different rate, for which diagnosis of the disease sometimes takes years by when treatment is no longer an option. However, using machine learning algorithms to classify the symptoms among patients, it is possible for early detection of the disease. İn this paper, the performance of machine learning algorithms are measures that can detect Parkinson disease. Three different datasets are used for the study. Each dataset goes through various feature selection techniques. Machine learning classifiers such as KNN, LDA, NB, LR, SVM, DT, RT, RF and ANN are implemented on the datasets and their performance is measured. It is observed that SVM has a high accuracy rate of prediction over all the feature selection techniques in all the datasets. © 2021 IEEE.

关键词： Neurodegenerative diseases

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Usage of Blockchain Technology for Improving computer Security

Usage of Blockchain Technology for Improving Computer Securi...

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Information Technology (ACIT)

作者： Adai Almomani Al Refai Mohammed N Ahmad Aburomman Omar Khair Alla Alidmat Qotadeh Saber Fuad Alshariedeh Mohammed Khouj Department of Computer Science Zarqa University Zarqa Jordan Software Engineering Dep. Zarqa University Zarqa Jordan Department of Data Science and A Zarqa University Zarqa Jordan Department of Electronic Enginnering Univeristy of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Cyber threats have grown increasingly sophisticated, surpassing the capabilities of traditional security measures. Blockchain technology, with its decentralized and immutable structure, offers a potential solution to these challenges. This study examines the role of blockchain tools such as smart contracts, decentralized identity management, and secure data sharing in enhancing cybersecurity. A review of key literature and analysis of survey data from cybersecurity professionals demonstrate blockchain's effectiveness in improving data integrity, access control, and secure communications. The results show significant correlations between the adoption of blockchain technologies and the mitigation of threats like data breaches and unauthorized access. While blockchain presents promising advancements, scalability, regulatory barriers, and energy consumption remain challenges. The study provides valuable insights for researchers, practitioners, and policymakers on how blockchain can be integrated into modern cybersecurity strategies to create more secure digital environments.

关键词： Access control Industries Proof of stake Scalability Smart contracts Data protection Blockchains computer security Identity management systems Business

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Prediction of Motion Trajectory of a Moving Object

Prediction of Motion Trajectory of a Moving Object

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National Conference with International Participation (TELECOM)

作者： Ventsislav Nikolov Ivo Rakitin Computer Science and Engineering Departments Technical University of Varna Varna Bulgaria Software and Internet Technologies Department Technical University of Varna Varna Bulgaria

In this paper experimental result of prediction of a moving object in a net of discrete points is presented. The prediction is done by autoregressive model of X-axis and Y-axis independently. Such kind of prediction could be valuable for prediction of moving of autonomous robots, drones and other similar artificial objects which can have implemented some kind of intelligent behavior. Thus the moving object can predict reaching a dangerous area or erupting points in its path. The results are analyzed and conclusions are summarized.

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Language-driven Grasp Detection with Mask-guided Attention

Language-driven Grasp Detection with Mask-guided Attention

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Tuan Van Vo Minh Nhat Vu Baoru Huang An Vuong Ngan Le Thieu Vo Anh Nguyen FPT Software AI Center Vietnam Automation & Control Institute TU Wien Austria Austrian Institute of Technology (AIT) GmbH Austria Imperial College London UK Department of Computer Science & Computer Engineering University of Arkansas USA National University of Singapore Singapore Department of Computer Science University of Liverpool UK

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Grasp detection is an essential task in robotics with various industrial applications. However, traditional methods often struggle with occlusions and do not utilize language for grasping. Incorporating natural language into grasp detection remains a challenging task and largely unexplored. To address this gap, we propose a new method for language-driven grasp detection with mask-guided attention by utilizing the transformer attention mechanism with semantic segmentation features. Our approach integrates visual data, segmentation mask features, and natural language instructions, significantly improving grasp detection accuracy. Our work introduces a new framework for language-driven grasp detection, paving the way for language-driven robotic applications. Intensive experiments show that our method outperforms other recent baselines by a clear margin, with a 10.0% success score improvement. We further validate our method in real-world robotic experiments, confirming the effectiveness of our approach.

关键词： Visualization Attention mechanisms Accuracy Service robots Source coding Semantic segmentation Natural languages Grasping Transformers Feature extraction

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Empirical Evaluation of Stock Market Prediction System using Intelligent Learning Scheme with Data Processing Logic

Empirical Evaluation of Stock Market Prediction System using...

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Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), International Conference on

作者： S. Prabakar Ankit Kumar R Jayakarthik D. Venkatesh Pratheeba R S Bhola Khan Finance and Accounting ISBR Business School Bangalore Department of Computer Application Faculty of Science Swami Vivekanand Subharti University Meerut Dept of Computer science Saveetha College of Liberal Arts and Sciences SIMATS Assistant Professor of Commerce School of Commerce and Management Mohan Babu University Tirupati Department of Computer Science and Engineering Velammal Institute of Technology Panchetti Department of Regional Economics MJP Rohilkhand University Bareilly India

The Stock Market Prediction System using Intelligent Learning Scheme (SMS-ILS) presented in this study integrates advanced methodologies to enhance the accuracy and reliability of stock market predictions. The system encompasses a preprocessing phase, addressing missing data and outliers, followed by feature extraction utilizing Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) to capture intricate temporal dependencies. A Deep Belief Network (DBN) is employed for classification, categorizing stocks based on learned representations from historical and technical indicators. Grey Wolf Optimization (GWO) optimizes the system's parameters, ensuring adaptability to dynamic market conditions. Notably, the system achieves an impressive accuracy of 97%, emphasizing its efficacy in making correct predictions. This multi-model approach, integrating LSTM, RNN, DBN, and GWO, provides a comprehensive and adaptive framework for navigating the complexities of stock market prediction. The study emphasizes the importance of choosing metrics aligned with specific goals and conducting thorough analysis to ensure the robustness of the evaluation results in the challenging and dynamic context of financial markets.

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FogNet: Custom CNN with optimal feature selection-based combat model for secured fog computing environment

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e-Prime - Advances in Electrical engineering, Electronics and Energy 2024年 8卷

作者： Sahu, Dipti Prava Tripathy, Biswajit Samantaray, Leena Biju Patnaik University of Technology Department of Computer Science and Engineering Odisha Rourkela 769004 India Einstein College of Computer Application and Management Master of Computer Applications Odisha Khurda 752060 India Ajay Binay Institute of Technology Department of Electronics and Communication Engineering Odisha Cuttack 753014 India

In recent years, the increasing number of connected devices and the exponential growth of data generated by these devices have posed significant challenges for intrusion detection systems (IDS) in fog computing environments. However, conventional IDS failed to provide better attack detection performance in fog computing networks. So, this work proposes a custom convolutional neural network (CCNN) with optimal feature selection-based combat model for secured fog computing environment. Initially, preprocessing of the dataset aims to enhance the quality of the input data by removing attack labels, reducing dimensionality, and extracting relevant features. Then, comprehensive learning particle swarm-based effective seeker optimization (CLPS-ESO) is employed to extract features by leveraging a comprehensive learning strategy and the powerful optimization capabilities of effective seeker particle swarm optimization. To further enhance the feature selection performance, the random forest-dependent African buffalo optimization with weighted genetic algorithm, here after referred as RF-IABO-WGA, is utilized to identify the most discriminative features from the CLPS-ESO dataset. By selecting a subset of informative features, the computational complexity is reduced, and the detection accuracy is improved. Finally, the selected features are fed into a custom convolutional neural network (CCNN) classifier, which leverages the power of deep learning to accurately classify network traffic as either normal or malicious. The CCNN architecture can effectively learn complex patterns and capture the underlying characteristics of network data, enabling accurate intrusion detection in fog computing environments. Experimental results on a UNSW-NB dataset demonstrate that the proposed FogNet resulted in accuracy of 99.771878 %, precision of 99.770460 %, recall of 99.538589 %, and F1-score of 99.653512 %, which are superior performances compared to existing approaches. © 2024 The Author(s)

关键词： African buffalo optimization Effective seeker optimization Feature analysis Fog computing Intrusion detection system Malicious activity detection Resource utilization

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Formation control and coordination of swarm robotic systems 21

Formation control and coordination of swarm robotic systems

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7th International Conference on engineering and MIS, ICEMIS 2021

作者： Kyzyrkanov, Abzal Ermekbayuly Atanov, Sabyrzhan Kubeisinovich Aljawarneh, Shadi Abdel Rahman Computer and Software Engineering Department L.N. Gumilyov Eurasian National University Nur-Sultan Kazakhstan L.N. Gumilyov Eurasian National University Senior Lecturer of Computer and Software Engineering Department Eurasian National University Nur-Sultan Kazakhstan Software Engineering Department Jordan University of Science and Technology Al Ramtha Irbid Jordan

ISBN: (纸本)9781450390446

In many applications, a group of autonomous mobile robots must follow a given trajectory while maintaining a certain geometric structure. If the motion in formation is properly organized, many advantages over traditional systems can be obtained: system cost can be reduced, system reliability and efficiency can be increased, while ensuring the reconfigurability and flexibility of the system's structure. Movement while maintaining a certain geometric structure has a wide range of applications. For example, in military missions, autonomous vehicles must maintain a certain structure of formation to cover terrain and conduct reconnaissance;in Smart Highway systems, the capacity of the transportation network can be greatly increased if vehicles can be grouped together at the same speed while maintaining a certain distance between them. This paper provides an algorithm for movement with a certain geometric structure of a group of autonomous mobile robots. © 2021 ACM.

关键词： Military applications

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