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8th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2024

作者： Hussain, Shahid Wang, Wen-Li Khan, Saif Ur Rahman Rahim, Muhammad Abdul Basit Ur Ibrahim, Naseem Zhao, Jie Department of Computer Science and Software Engineering Penn State University Erie United States Department of Software Engineering Shifa Tameer-e-Millat University Islamabad Pakistan Department of Computer Science California State University Long Beach United States

ISBN: (纸本)9789819757985

The long-term participation of trained and competent employees is required to improve the morale, productivity, safety, value, and autonomy of an organization. In small and medium-sized software industries, the long-term involvement of developers will not only boost the efficiency of software development but also help train their team members. Although software industries have managed to stimulate developers’ productivity and reduce their turnover with a better compensation plan, the incentive through money has caused contradictory arguments. Hence, there is a need to employ a more systematic way to explore, analyze, and calibrate the developer’s needs, so that the managerial roles can make informed decisions and incorporate influential motivators to increase a developer’s job satisfaction level. In this respect, we are conducting an empirical study using the basics of Maslow's theory, a motivational theory in psychology, to (1) explore the developer’s motivation level in software industries, (2) benchmark the developer’s needs for job satisfaction at each level, and (3) analyze the job-hunting factors for developers’ turnover. We conducted a questionnaire-based study to gather feedback from 128 developers in 10 software industries with four types of analyses being conducted. The results indicate that satisfaction levels can vary across software industries, but the management can anticipate developers’ high-level needs from their low-level needs to determine influencing and motivating factors for job satisfaction improvements. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Job satisfaction

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Predicting the Spread of Covid-19 Using Machine as a Service (MaaS) Through Modified Apriori Algorithm

Predicting the Spread of Covid-19 Using Machine as a Service...

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International Conference on Smart Systems and Wireless Communication, SSWC 2024

作者： Sett, Samir Kumar Sett, Aishik Ghosh, Anupam Department of Computer Application Techno India Kolkata India ITC Infotech Kolkata India Department of Computer Science and Engineering and Department of Artificial Intelligence & Machine Learning Netaji Subhash Engineering College Kolkata India

ISBN: (纸本)9789819613472

The cloud computing and machine learning is one of growing technology in the field of computer science. In recent years, the word "Cloud" is like a cup of tea in our daily lives. Every day, a significant volume of data is produced by different IT sectors, banking sectors, e-commerce websites like flipkart. It provides the organization with consistency, scalability, and strong enumerating power. It also delivers eminence service by generating proper outcomes in a brief amount of time. The objective of this paper is to predict the growth of Covid-19 virus in near future using Machine as a Service (MaaS) through modified Apriori algorithm. In this paper, we will obtain the working graph of the algorithm. The result is also analyzed by obtaining the graphs for confidence against lift, support against confidence. At last, a running time comparative graph for modified apriori and apriori is shown for a certain duration of time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Apriori Cloud computing Confidence Covid-19 MaaS Support

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Decentralized and Network-Aware UAV Service Deployment for Dependency-Driven applications

Decentralized and Network-Aware UAV Service Deployment for D...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Sari, Talip Tolga Quadri, Christian Seçinti, Gökhan Trotta, Angelo University of Bologna Department of Computer Science and Engineering Italy Istanbul Technical University Department of Computer Engineering Turkey Università degli Studi di Milano Department of Computer Science Italy Bts - Digital Twin Application and Research Center Istanbul Technical University Turkey

ISBN: (纸本)9798350368369

Unmanned Aerial Vehicle (UAV) swarms enable the rapid deployment of IoT services in dynamic and challenging environments. While these swarms offer flexibility and close proximity to sensing and actuation points, efficiently deploying interdependent services at scale remains a core challenge. Traditional centralized methods struggle to handle the complexity of large UAV networks, leading to increased latency and limited reliability. In this paper, we propose a decentralized approach to service deployment in UAV swarms. Our method relies on local information at each node, allowing UAVs to make their own assignment decisions. Over time, these decisions are iteratively refined as nodes exchange status updates and adapt to network changes. This process avoids the bottlenecks of centralized coordination and enables more responsive resource allocation. Simulation results show that our approach supports the successful deployment of a high number of tasks while maintaining low latency. These findings indicate that decentralized methods with local resource knowledge, improves both scalability and responsiveness in UAV-based IoT systems. © 2025 IEEE.

关键词： Target drones

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EdgeMob: A Context-Aware Dynamic Service Migration for Wearable IoT in Healthcare 1

EdgeMob: A Context-Aware Dynamic Service Migration for Weara...

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1st International Conference on Computing, Internet of Things and Microwave Systems, ICCIMS 2024

作者： Raza, Imran Hussain, Syed Asad Latif, Sana COMSATS University Islamabad Lahore Department of Computer Science Lahore Pakistan Department of Software Engineering Lahore Pakistan

ISBN: (纸本)9798350351736

Edge Computing (EC) is a distributed network architecture offering computation and storage resources at the network edge, addressing cloud computing limitations by optimizing latency and enhancing data safety and privacy for real-time services. It also supports intelligent solutions for IoT-based smart healthcare systems through Deep Learning (DL). DL aids in resource allocation optimization and disease prediction. However, the surge in wearable device usage introduces challenges in user mobility and migration, leading to increased latency and service inconsistencies. To tackle this, we propose EdgeMob, an intelligent and context-aware scheme deployed as a federation of edge servers. EdgeMob utilizes Q-learning-based reinforcement learning for personalized service migration decisions based on user mobility and Quality of Service (QoS). We evaluate our system against existing solutions, considering parameters such as user-perceived latency, migration cost, and network bandwidth utilization. © 2024 IEEE.

关键词： Electronic health record

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Empirical Analysis of Digital Currency Herding and Contagion Effect Using Deep Learning Approaches 3

Empirical Analysis of Digital Currency Herding and Contagion...

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3rd Odisha International Conference on Electrical Power engineering, Communication and Computing Technology, ODICON 2024

作者： Tripathy, Nrusingha Hota, Sarbeswara Mishra, Debahuti Dash, Biswajit Nayak, Subrat Kumar Tripathy, Jogeswar Deemed to Be University Department of Computer Science and Engineering Siksha 'O' Anusandhan Odisha Bhubaneswar India Deemed to Be University Department of Computer Application Siksha 'O' Anusandhan Odisha Bhubaneswar India Raajdhani Engineering College Department of Computer Science and Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798350354379

In recent years, the use of virtual or cryptocurrency-based money has grown. Blockchain, a digital accounting system, is the foundation of cryptocurrency. The markets for cryptocurrencies are eminent for their high volatility and quick price swings. People tend to be more likely to follow the lead of others at times of notable price fluctuations out of a concern of losing money or missing out on possible gains. In this work, we taking the Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), the hybrid combinations of LSTM-Convolutional Neural Network (CNN), LSTM-Gated Recurrent Unit (GRU) and for Facebook Prophet (Fb-Prophet) model smoothing, triple exponential smoothing (Holt-Winters Method) is utilized, which adds seasonality into the forecasting model. In this work bitcoin exchanges providing real-time information casing the period from January 2012 to July 2020 is utilized. The price of cryptocurrencies fluctuates significantly throughout a range of time periods, from short-term intraday swings to long-term patterns that last for weeks or months. By altering the network layer design and the duration of the input sequences, LSTM-GRU models configured to adapt to various time horizons. In contrast to previous iterations, the hybrid LSTM-GRU performs well. When compared to other models, the model's Mean Absolute Error (MAE) score of 0.056 and Root Mean Square Error (RMSE) score of 0.073 are quite acceptable. Forecasting the value of cryptocurrencies will help traders and investors in making better-informed choices when purchasing, disposing of, and keeping assets. © 2024 IEEE.

关键词： Convolutional neural networks

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Deep Learning with a Novel Concoction Loss Function for Identification of Ophthalmic Disease

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computers, Materials & Continua 2023年第9期76卷 3763-3781页

作者： Sayyid Kamran Hussain Ali Haider Khan Malek Alrashidi Sajid Iqbal Qazi Mudassar Ilyas Kamran Shah Department of Computer Science TIMES InstituteMultan60000Pakistan Department of Software Engineering Faculty of Computer ScienceLahore Garrison UniversityLahore54000Pakistan Department of Computer Science Applied CollegeUniversity of TabukTabukSaudi Arabia Department of Information Systems College of Computer Sciences and Information TechnologyKing Faisal UniversityAl Hofuf31982Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl HofufSaudi Arabia

As ocular computer-aided diagnostic(CAD)tools become more widely accessible,many researchers are developing deep learning(DL)methods to aid in ocular disease(OHD)*** eye diseases like cataracts(CATR),glaucoma(GLU),and age-related macular degeneration(AMD)are the focus of this study,which uses DL to examine their *** imbalance and outliers are widespread in fundus images,which can make it difficult to apply manyDL algorithms to accomplish this analytical *** creation of efficient and reliable DL algorithms is seen to be the key to further enhancing detection *** the analysis of images of the color of the retinal fundus,this study offers a DL model that is combined with a one-of-a-kind concoction loss function(CLF)for the automated identification of *** study presents a combination of focal loss(FL)and correntropy-induced loss functions(CILF)in the proposed DL model to improve the recognition performance of classifiers for biomedical *** is done because of the good generalization and robustness of these two types of losses in addressing complex datasets with class imbalance and *** classification performance of the DL model with our proposed loss function is compared to that of the baseline models using accuracy(ACU),recall(REC),specificity(SPF),Kappa,and area under the receiver operating characteristic curve(AUC)as the evaluation *** testing shows that the method is reliable and efficient.

关键词： Deep learning multi-classification focal loss CNN eye disease

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An ensemble deep learning model for cyber threat hunting in industrial internet of things

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Digital Communications and Networks 2023年第1期9卷 101-110页

作者： Abbas Yazdinejad Mostafa Kazemi Reza M.Parizi Ali Dehghantanha Hadis Karimipour Cyber Science Lab School of Computer ScienceUniversity of GuelphOntarioCanada Department of Electrical Engineering Faculty of EngineeringShahed UniversityTehranIran College of Computing and Software Engineering Kennesaw State UniversityGAUSA School of Engineering University of GuelphOntarioCanada

By the emergence of the fourth industrial revolution,interconnected devices and sensors generate large-scale,dynamic,and inharmonious data in Industrial Internet of Things(IIoT)*** vast heterogeneous data increase the challenges of security risks and data analysis *** IIoT grows,cyber-attacks become more diverse and complex,making existing anomaly detection models less effective to *** this paper,an ensemble deep learning model that uses the benefits of the Long Short-Term Memory(LSTM)and the AutoEncoder(AE)architecture to identify out-of-norm activities for cyber threat hunting in IIoT is *** this model,the LSTM is applied to create a model on normal time series of data(past and present data)to learn normal data patterns and the important features of data are identified by AE to reduce data *** addition,the imbalanced nature of IIoT datasets has not been considered in most of the previous literature,affecting low accuracy and *** solve this problem,the proposed model extracts new balanced data from the imbalanced datasets,and these new balanced data are fed into the deep LSTM AE anomaly detection *** this paper,the proposed model is evaluated on two real IIoT datasets-Gas Pipeline(GP)and Secure Water Treatment(SWaT)that are imbalanced and consist of long-term and short-term dependency on *** results are compared with conventional machine learning classifiers,Random Forest(RF),Multi-Layer Perceptron(MLP),Decision Tree(DT),and Super Vector Machines(SVM),in which higher performance in terms of accuracy is obtained,99.3%and 99.7%based on GP and SWaT datasets,***,the proposed ensemble model is compared with advanced related models,including Stacked Auto-Encoders(SAE),Naive Bayes(NB),Projective Adaptive Resonance Theory(PART),Convolutional Auto-Encoder(C-AE),and Package Signatures(PS)based LSTM(PS-LSTM)model.

关键词： Internet of things IIoT Anomaly detection Ensemble deep learning Neural networks LSTM

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Enhancing Password Security: Analyzing Password Management Practices Among IT Students 7

Enhancing Password Security: Analyzing Password Management P...

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7th IEEE International Conference and Workshop Obuda on Electrical and Power engineering, CANDO-EPE 2024

作者： Kollar, Anita Mihalovicsne Katona, Jozsef Institute of Computer Engineering University of Dunaujvaros Department of Software Development and Application Dunaujvaros Hungary John Von Neumann University GAMF Faculty of Engineering and Computer Science Kecskemet Hungary Budapest Business University Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest Hungary

ISBN: (纸本)9798331531119

Today, with the digital world in its full swing, strong password management is key to protecting sensitive information from both internal and external threats. The current research explores the passwords managing behavior of the IT students at the University of Dunaujvaros. A survey made in May 2024 questioned 68 students, 59 males and 9 females, about behaviors such as using passwords with complexity, not repeating them for multiple accounts or machines, changing them regularly, and which applications they use for password management. The findings have depicted that, as a whole, students appear to be moderately sensitive about the password security instructions. Nonetheless, most continue to indulge in unsafe practices, including easily remembered or weak passwords and infrequent password changes. Furthermore, approximately half of the responders use password management applications, indicating room for significant improvement in the future. The results suggest that intervention and special education measures should be taken to enhance the practice of password security among future IT experts. This research has good potential to contribute toward strategies designed for people behaving safely in password management, and hence promote a secure digital environment. © 2024 IEEE.

关键词： Students

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Optimal Multi-Bitrate Video Caching and Processing in Edge Computing: A Stackelberg Game Approach

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IEEE Internet of Things Journal 2025年第13期12卷 25059-25076页

作者： Guo, Min Zhang, Di Xing, Weiwei Shao, Xun Liu, Zhi Zhang, Yaoxue Beijing Jiaotong University School of Software Engineering Beijing100044 China Toyohashi University of Technology Department of Electrical and Electronic Information Engineering Aichi441-8580 Japan The University of Electro-Communications Department of Computer and Network Engineering Tokyo182-8585 Japan Tsinghua University Department of Computer Science and Technology Beijing100084 China

By caching and transcoding video files on edge servers, video edge caching (VEC) can alleviate network congestion and improve user experience. To achieve this, VEC needs to address resource allocation and pricing problems and develop caching and transcoding strategies. However, existing works have not effectively coordinated and resolved these issues. Towards this end, we propose a game-theoretic multi-bitrate video caching and processing method for VEC. Specifically, to address these interdependent issues concurrently, we first incorporate multidimensional strategies into the system model and formalize the profit maximization problems of the network service provider and multiple video providers (VPs) as a constrained multi-objective optimization problem. Second, we transform the problem into a Stackelberg game and identify a Nash equilibrium as the solution. To solve the game problem, we decompose the game process into two stages focusing on resource competition and price determination, respectively. Then, we address the problems of transcoding and caching strategies development, and resource allocation in parallel across varying resource price conditions. The transcoding strategy is determined based on the constraints imposed by delay, value, and the source video. For the caching strategy development and resource allocation, we transform them into two grouped knapsack problems and apply the "dominant strategy» from game theory to identify the "feasible strategy» of VPs. Finally, we determine the resource prices in a traversal manner. Theoretical analysis shows that our proposed method achieves the optimal solution. Experimental results show that our algorithm achieve a 6.04% increase in overall profit compared to the greedy strategy algorithm. © 2025 IEEE.

关键词： Costs

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Classification of Online Sentiment Data tweets using Various Approaches in Machine Learning

Classification of Online Sentiment Data tweets using Various...

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2023 International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Rawat, Vandana Bisht, Paritosh Suryakant Kothiyal, Shruti Singh, Neelam Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Department of Computer Application Dehradun India Department of Computer Science and Engineering France

ISBN: (纸本)9798350323917

Online data is the data that is present on an online platform such as social media. Social media contains various information through which it can analyze users' attitudes & behavior. Sentimental analysis enables the automatic analysis of expression whether it is positive or negative. It can be about places, people, events, organizations, brands, products, etc. By using tools provided by machine learning & natural language processing with different approaches sentiment analysis can be made possible. The ubiquitous use of the Online World has given people a new method for expressing their emotions. It also functions as a platform with a shedload of knowledge where viewers may examine other users' thoughts, which are categorized into so many sentiment classifications and have become more and more essential in judgment. The representation of the number of comments that contain complex multidimensional sentiments that are either good or bad or anything in between the two can really be analyzed by means of this paper's approach to sentiment classification for consumer review classification. In this research, sentiment contribution in the field of online data tweets are collected, and using various machine learning algorithms results are identified as collective actions using different accuracy. © 2023 IEEE.

关键词： Sentiment analysis

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