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1st International Conference on Artificial Intelligence of Things, ICAIoT 2023

作者： Singh, Akash Kumar Thakral, Prateek Department of Computer Science and Engineering and Information Technology Jaypee University of Information Technology Waknaghat Himachal Pradesh Solan India

ISBN: (纸本)9783031487804

Attrition means exit of employees from an organization due to any reason. The exit may be voluntary or involuntary in nature. Increased rate of employee attrition leads to loss of an organization’s resources in terms of time, cost, customer satisfaction as well as talent acquisition team’s efforts. The pattern of statistical approaches used in this paper to study the causes of attrition provides insights for the decision makers of organizations about the possible constraints due to which employees may think of leaving. This paper analyses an employee dataset and proposes a machine learning based model with an F1 Score of 0.99 and accuracy of 98.63% that can be used and deployed by the companies on their datasets to predict attrition. The timely prediction of attrition using this method will help the companies to prevent the possible attrition of employees and will help in retaining them for good. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Decision trees

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Cat Swarm Algorithm Generated Based on Genetic Programming Framework Applied in Digital Watermarking

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computers, Materials & Continua 2025年第5期83卷 3135-3163页

作者： Shu-Chuan Chu Libin Fu Jeng-Shyang Pan Xingsi Xue Min Liu School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210000China College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266000China Department of Information Management Chaoyang University of TechnologyTaichung40601TaiwanChina School of Information Science and Engineering Fujian University of TechnologyFuzhou350000China Key Laboratory of Hydrometeorological Disaster Mechanism and Warning of Ministry of Water Resource Nanjing University of Information Science and TechnologyNanjing210000China

Evolutionary algorithms have been extensively utilized in practical ***,manually designed population updating formulas are inherently prone to the subjective influence of the *** programming(GP),characterized by its tree-based solution structure,is a widely adopted technique for optimizing the structure of mathematical models tailored to real-world *** paper introduces a GP-based framework(GPEAs)for the autonomous generation of update formulas,aiming to reduce human *** modifications to tree-based GP have been instigated,encompassing adjustments to its initialization process and fundamental update operations such as crossover and mutation within the *** designing suitable function sets and terminal sets tailored to the selected evolutionary algorithm,and ultimately derive an improved update *** Cat Swarm Optimization Algorithm(CSO)is chosen as a case study,and the GP-EAs is employed to regenerate the speed update formulas of the *** validate the feasibility of the GP-EAs,the comprehensive performance of the enhanced algorithm(GP-CSO)was evaluated on the CEC2017 benchmark ***,GP-CSO is applied to deduce suitable embedding factors,thereby improving the robustness of the digital watermarking *** experimental results indicate that the update formulas generated through training with GP-EAs possess excellent performance scalability and practical application proficiency.

关键词： Cat swarm algorithm genetic programming digital watermarking update mode mode generation framework

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Network Anomaly Detection using Autoencoder on Various Datasets: A Comprehensive Review

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Recent Patents on engineering 2024年第9期18卷 63-77页

作者： Singh, Richa Srivastava, Nidhi Kumar, Ashwani Amity Institute of Information Technology Amity University Lucknow Campus Uttar Pradesh Lucknow India Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida India

The scientific community is currently very concerned about information and communication technology security because any assault or network anomaly can have a remarkable collision on a number of areas, including national security, the storage of private data, social welfare, economic concerns, and more. As a result, many strategies and approaches for this goal have been developed over time, making the anomaly detection domain a large research subject. The primary concern of this patent study is to review the most crucial elements relating to anomaly detection, including an overview of background analysis and a core study on the most important approaches, procedures, and systems in the field. To make the structure of this survey easier to understand, the domain of anomaly detection was examined along with five dimensions: Detection methods in network traffic, objectives of the patent paper, various datasets used, accuracy, and open issues/gaps. The gap which has been identified after the survey can be extended as a future scope might be helpful for the researcher. © 2024 Bentham Science Publishers.

关键词： Anomaly detection

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Ensemble Approach Combining Deep Residual Networks and BiGRU with Attention Mechanism for Classification of Heart Arrhythmias

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computers, Materials & Continua 2024年第7期80卷 341-359页

作者： Batyrkhan Omarov Meirzhan Baikuvekov Daniyar Sultan Nurzhan Mukazhanov Madina Suleimenova Maigul Zhekambayeva Department of Information Systems Al-Farabi Kazakh National UniversityAlmaty050040Kazakhstan Department of Mathematical and Computer Modeling International Information Technology UniversityAlmaty050040Kazakhstan Department of Software Engineering Satbayev UniversityAlmaty050013Kazakhstan

This research introduces an innovative ensemble approach,combining Deep Residual Networks(ResNets)and Bidirectional Gated Recurrent Units(BiGRU),augmented with an Attention Mechanism,for the classification of heart *** escalating prevalence of cardiovascular diseases necessitates advanced diagnostic tools to enhance accuracy and *** model leverages the deep hierarchical feature extraction capabilities of ResNets,which are adept at identifying intricate patterns within electrocardiogram(ECG)data,while BiGRU layers capture the temporal dynamics essential for understanding the sequential nature of ECG *** integration of an Attention Mechanism refines the model’s focus on critical segments of ECG data,ensuring a nuanced analysis that highlights the most informative features for arrhythmia *** on a comprehensive dataset of 12-lead ECG recordings,our ensemble model demonstrates superior performance in distinguishing between various types of arrhythmias,with an accuracy of 98.4%,a precision of 98.1%,a recall of 98%,and an F-score of 98%.This novel combination of convolutional and recurrent neural networks,supplemented by attention-driven mechanisms,advances automated ECG analysis,contributing significantly to healthcare’s machine learning applications and presenting a step forward in developing non-invasive,efficient,and reliable tools for early diagnosis and management of heart diseases.

关键词： CNN BiGRU ensemble deep learning ECG arrhythmia heart disease

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OSSIoT: An ontology-based Operational Security model for Social Internet of Things using Machine Learning Techniques

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2024年第10期51卷 1440-1453页

作者： Kumar, K.S. Santhosh Hanumanthappa, J. Prakash, S.P. Shiva Krinkin, Kirill Department of Studies in Computer Science University of Mysore Karnataka Mysuru India Department of Information Science and Engineering JSS Science and Technology University Karnataka Mysuru India School of Computer Science and Engineering Constructor University Bremen Germany

The Social Internet of Things (SIoT) is an innovative fusion of IoT and smart devices that enable them to establish dynamic relationships. Securing sensitive data in a smart environment requires a model to determine the relationships between devices through object profiling and ontological models. To address this need, we have proposed an ontology-based operational security model for the SIoT. In our approach, the interpolation method is used to establish relationships, while fiduciary relationships are employed to detect threats. Furthermore, the encryption of heterogeneous device data, coupled with the implementation of an operation-based intrusion detection system, proves highly effective in identifying potential *** relationships are identified as intruders and validated using machine learning techniques. Encrypting heterogeneous device data, along with an operation-based intrusion detection system, efficiently identifies threats in the ever-evolving dynamic nature of the SIoT environment. the encryption of heterogeneous device data, coupled with an operation-based intrusion detection system, effectively identifies threats. Invalid relationships are promptly identified as attackers and machine learning techniques are used to validate relationships encryption and machine learning stand as indispensable tools in the endeavor to secure sensitive information within the realm of the SIoT. The suggested model outperformed the results of the current model, as evidenced by its average accuracy of 85.67%, precision of 90.37%, recall of 92.06%, and F1-score of 91.03% when compared to the existing model. © (2024), (International Association of Engineers). All rights reserved.

关键词： Network intrusion

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A three-step SEM-Bayesian network approach for predicting the determinants of CloudIoT-based healthcare adoption

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International Journal of information technology (Singapore) 2024年 1-21页

作者： Altawaiha, Iyad Atan, Rodziah Yaakob, Razali Bin Abdullah, Rusli Bin Hj Department of Software Engineering and Information Systems Faculty of Computer Science and Information Technology Universiti Putra Malaysia (UPM) Serdang 43400 Malaysia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia (UPM) Serdang 43400 Malaysia

Adopting the CloudIoT-based healthcare paradigm provides various prospects for medical IT and considerably enhances healthcare services. However, compared to the advanced development of CloudIoT-based healthcare systems, their usage is relatively low among healthcare professionals in hospitals. Thus, a study on the healthcare professionals acceptance and adoption of CloudIoT-based healthcare is critical. This study explores factors influencing healthcare professionals adoption of CloudIoT-based healthcare solutions by extending the Unified Theory of Acceptance and Use of technology 2 model. Data was collected from 300 healthcare professionals in Jordan through an online questionnaire developed using the Google^® form application. The Bayesian Network and Structural Equation Modeling techniques were used to analyze and validate the proposed model. The results revealed that the healthcare professional’s behavioral intention is directly affected by seven factors: performance expectancy, effort expectancy, facilitating conditions, habit, trust, privacy, and security. The results also indicate that trust mediates the influence of privacy, facilitating conditions, and performance expectancy on behavioral intention. The research results will aid CloudIoT service providers, healthcare organizations, designers, and developers by offering comprehensive knowledge of the significant factors of CloudIoT-based healthcare adoption. A better understanding of these factors will help these stakeholder groups enhance their services and speed up the adoption of CloudIoT in the healthcare area. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Adoption Bayesian network CloudIoT Healthcare professionals Structural equation modeling UTAUT2

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Tweeting the Market: Leveraging LSTM for Stock Price Prediction 2

Tweeting the Market: Leveraging LSTM for Stock Price Predict...

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2nd International Conference on Advances in Computation, Communication and information technology, ICAICCIT 2024

作者： Modi, Vinni Agrawal, Nandini Sardana, Neetu Jain, Ishika Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology U.P Noida India

ISBN: (纸本)9798331541217

Investors and traders constantly seek an edge, a source of insight that can help them predict market movements more accurately. Over the past several years, the emergence of social media and the increase in use of platforms such as Twitter, Facebook, etc. has given birth to a new dimension of market analysis - sentiment analysis and its potential effect on stock prices. The mood dimensions that correlate with market movements can be complex and multifaceted. Only a few researches have studied the effect of social media on stock price predictions. Prior investigations have examined discussions surrounding the COVID-19 virus, its societal repercussions, and its potential sway over financial markets. This research has specifically scrutinized the effect of negative sentiments and their potential repercussions on stock prices of major tech companies like Microsoft, Google, and Tesla. The paper aims to identify the most effective model among ARIMA and LSTM for predicting stock prices by correlating them with sentiments expressed in social media posts. The motive of this paper is to help investors better analyze the market and adjust their strategies accordingly to earn more profits. © 2024 IEEE.

关键词： Tweets

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Efficient Collision-Free Motion Planning in Distributed Multi-Robot Systems 17

Efficient Collision-Free Motion Planning in Distributed Mult...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Sharma, Om Chakraborty, Nilotpal India Institute of Information Technology Department of Computer Science and Engineering Assam Guwahati India

ISBN: (纸本)9798331531195

In distributed multi-robot systems, ensuring collision-free motion planning is a complex challenge, especially in dynamic environments where multiple robots are operating simultaneously. Traditional path-planning algorithms are effective for individual robot navigation but struggle when dealing with the unpredictable movements of other robots in the system. To overcome these limitations, this research focuses on advanced motion planning techniques that ensure safe and efficient robot coordination. We model the overall motion planning problem into a two-dimensional grid and our objective is to minimize both the time and the energy taken by the robots to reach their destinations from their respective sources. We enhance standard path-finding algorithms such as A∗ search, along with Deep Reinforcement Learning (DRL) with suitable modifications to adapt to real-time changes. DRL allows robots to learn from their environment and make optimal decisions, improving their ability to avoid collisions and navigate efficiently in uncertain conditions. By integrating these algorithms and conducting a comparative analysis, we propose a framework that enables robots to dynamically adjust their paths, ensuring collision-free movement and optimal coordination. © 2025 IEEE.

关键词： Robot programming

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Predicting Student Behavior Using a Neutrosophic Deep Learning Model

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Neutrosophic Sets and Systems 2025年 76卷 288-310页

作者： Shitaya, Ahmed Mohamed Wahed, Mohamed El Syed Abd El Khalek, Saied Helemy Ismail, Amr Shams, Mahmoud Y. Salama, A.A. Department of Mathematics and Computer Science Faculty of Science Port Said University Egypt Department of Computer Science Faculty of computer and information sciences Suez Canal University Egypt Department. of Communication and Computer Engineering Air Defense University Egypt Department of Cybersecurity College of Engineering and Information Technology Buraydah Private Colleges Buraydah51418 Saudi Arabia Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt

We developed an information system using an object-oriented programming language and a distributed database (DDB) consisting of multiple interconnected databases across a computer network, managed by a distributed database management system (DDBMS) for easy access. An intelligent system was designed to assess the difficulty level of preliminary exams and select top-performing advanced students using a Neutrosophic Deep Learning Model. The dataset was randomly split into training (80%) and testing (20%) sets, and the model, trained with the Adam optimizer at a 0.001 learning rate over 50 epochs, incorporated early stopping based on validation loss. This system, implemented at a traditional Egyptian university, achieved a 95% accuracy in predicting student dropout. Student behavior, influenced by personal, environmental, and academic factors, is often evaluated subjectively, leading to inconsistent results. Traditional machine learning approaches struggle with the inherent uncertainty in behavioral data. To address this, we combined neutrosophic theory—a mathematical framework that accounts for truth, falsity, and indeterminacy—with deep learning, which excels at learning complex data relationships, to predict student outcomes such as dropout rates. Evaluating the model on student data, including attendance and grades, showed superior accuracy, achieving a determination coefficient of 0.95, demonstrating the approach's potential for identifying at-risk students and enabling targeted interventions. © 2025, University of New Mexico. All rights reserved.

关键词： Contrastive Learning

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A Comparative Analysis of Deep Learning Based Techniques for Cyber Security 1

A Comparative Analysis of Deep Learning Based Techniques for...

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1st International Conference on Software, Systems and information technology, SSITCON 2024

作者： Sangeetha, K. Kala, A. Femi, P. Sharon Priya, S. Baghavathi Rajalakshmi Engineering College Department of Information Technology Chennai India Sri Venkateswara College of Engineering Department of Information Technology Sriperumbudur India Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350352931

Over time, the need for cybersecurity has become a need. It consists of the entirety of actions involved in preventing hackers. It prevents the exploitation of not only the data in internet-connected systems but also the hardware and software entities from cyber threats. Deep learning (DL) techniques, with their ability to process vast data and detect complex patterns, offer significant advancements in identifying malware, intrusions, and anomalies. This study reviews various DL models like Deep Belief Networks (DBN), Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) applied to cybersecurity. Our comparative analysis of CNN and Long Short-Term Memory (LSTM) models reveals LSTM's superior accuracy in detecting cyber threats, achieving 9 6 %compared to CNN's 85%. The results highlight DL's potential in creating adaptive security systems, though challenges in dataset preparation and model testing persist. Future work should focus on refining these datasets to enhance DL's efficiency in cybersecurity applications. © 2024 IEEE.

关键词： Network intrusion

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