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2nd International Conference on Next-Generation Computing and information systems, ICNGCIS 2023

作者： Kumar, Pramod Vashishtha, Ela Alghayadh, Faisal Yousef Wasim Bhatt, Mohammed Bala, Suchitra Sreejitha, T.S. Computer Science and Technology Ganga Institute of Technology and Management Haryana Kablana Jhajjar India Lead Analytics Texas Health Resources TX United States Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Model Institute of Engineering and Technology J&K Jammu India ICT & Cognitive Systems Sri Krishna Arts and Science College India St. Marys College Thrissur India

ISBN: (纸本)9781032738659

Research the mechanism of fake news refuting rumors in the real social network environment. Propose methods to evaluate the effect of rumor refuting and explore the factors that affect the impact of rumor refuting. Based on the current research results and assumptions, eight factors that affect the effect of rumor refuting are summed up, such as the proportion of the original fake news content. Experiments were conducted based on the rumor-refuting Weibo data. The experimental analysis showed that the proportion of the original fake news in the rumor-refuting information was negatively correlated with the rumor-refuting effect, and the explanation was positively correlated with the rumor-refuting effect suggestions for refuting rumors, including explaining why the original fake news is wrong to guiding social network fake news denying rumors. © 2025 The Author(s).

关键词： Sentiment analysis

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Crowdsourced Requirements engineering Challenges and Solutions:A software Industry Perspective

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computer systems science & engineering 2021年第11期39卷 221-236页

作者： Huma Hayat Khan Muhammad Noman Malik Youseef Alotaibi Abdulmajeed Alsufyani Saleh Alghamdi Department of Software Engineering National University of Modern LanguagesIslamabadPakistan Department of Computer Science National University of Modern LanguagesIslamabadPakistan Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkahSaudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityPO Box 11099TaifSaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia

software crowdsourcing(SW CS)is an evolving software development paradigm,in which crowds of people are asked to solve various problems through an open call(with the encouragement of prizes for the top solutions).Because of its dynamic nature,SW CS has been progressively accepted and adopted in the software ***,issues pertinent to the understanding of requirements among crowds of people and requirements engineers are yet to be clarified and *** the requirements are not clear to the development team,it has a significant effect on the quality of the software *** study aims to identify the potential challenges faced by requirements engineers when conducting the SW–CS based requirements engineering(RE)***,solutions to overcome these challenges are also *** data analysis is performed on the interview data collected from software industry ***,20 SW–CS based RE challenges and their subsequent proposed solutions are devised,which are further grouped under seven *** study is beneficial for academicians,researchers and practitioners by providing detailed SW–CS based RE challenges and subsequent solutions that could eventually guide them to understand and effectively implement RE in SW CS.

关键词： software crowdsourced requirements engineering software industry software development survey challenges

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Automated File Labeling for Heterogeneous Files Organization Using Machine Learning

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computers, Materials & Continua 2023年第2期74卷 3263-3278页

作者： Sagheer Abbas Syed Ali Raza MAKhan Muhammad Adnan Khan Atta-ur-Rahman Kiran Sultan Amir Mosavi School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Computer Science GC University LahorePakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Software Pattern Recognition and Machine Learning LabGachon UniversitySeongnam13120Korea Department of Computer Science College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam31441Saudi Arabia Department of CIT The Applied CollegeKing Abdulaziz UniversityJeddah31261Saudi Arabia John von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaBratislava81107Slovakia Faculty of Civil Engineering TU-DresdenDresden01062Germany

File labeling techniques have a long history in analyzing the anthological trends in computational *** situation becomes worse in the case of files downloaded into systems from the ***,most users either have to change file names manually or leave a meaningless name of the files,which increases the time to search required files and results in redundancy and duplications of user ***,no significant work is done on automated file labeling during the organization of heterogeneous user files.A few attempts have been made in topic ***,one major drawback of current topic modeling approaches is better *** rely on specific language types and domain similarity of the *** this research,machine learning approaches have been employed to analyze and extract the information from heterogeneous corpus.A different file labeling technique has also been used to get the meaningful and`cohesive topic of the *** results show that the proposed methodology can generate relevant and context-sensitive names for heterogeneous data files and provide additional insight into automated file labeling in operating systems.

关键词： Automated file labeling file organization machine learning topic modeling

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LEFAM: Lightweight scalable efficient failover authentication mechanism for fog-based IoT environment

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Peer-to-Peer Networking and Applications 2025年第4期18卷 1-18页

作者： Abdussami, Mohammad Dwivedi, Sanjeev Kumar Amin, Ruhul Lee, Cheng-Chi Al-Shehari, Taher Alfakih, Taha Alsalman, Hussain Department of Computer Science and Engineering SRM University AP Amaravati India Department of Computer Science and Engineering International Institute of Information Technology Naya Raipur (IIIT-NR) India Department of Computer Science and Engineering School of Technology Pandit Deendayal Energy University (PDEU) Gandhinagar India Department of Library and Information Science Fu Jen Catholic University New Taipei City Taiwan Department of Computer Science and Information Engineering Fintech and Blockchain Research Center Asia University Taichung City Taiwan Computer Skills Department of Self-Development Skill Common First Year Deanship King Saud University Riyadh Saudi Arabia Department of Information Systems College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia Department of Computer Science College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia

The integration of the Internet of Things (IoT) with fog computing has significantly enhanced the capabilities of IoT applications by extending their reach and reducing latency. Fog nodes, being transient and authorized service providers, necessitate reauthentication of IoT end devices when contact is lost, requiring the device to connect to a new fog node. In this research, we propose an innovative security protocol designed to ensure seamless failover among fog nodes. This protocol facilitates quick and easy failover authentication among IoT devices and fognodes using the key agreement among fog nodes. LEFAM overcomes the requirement for the end device to communicate with the fog node atleast once to get the security token in order to execute failover authentication. This work proposes efficient, scalable and robust scheme required for fog-IoT environment to provide solution in case of failure of the primary fognodes. Our approach effectively mitigates various security threats, as demonstrated through informal analysis. Furthermore, simulation results obtained using the Scyther tool confirm the robustness of our protocol in terms of security. A formal analysis conducted using the RoR model further substantiates the reliability of our protocol. Notably, our protocol involves fewer message exchanges, indicating a lower communication overhead compared to other existing schemes. The key finding indicates that the LEFAM scheme offers an average improvement of $$\approx $$ 76% and 1188 bits for computation and communication overheads, respectively. Overall, our protocol not only enhances security but also offers superior performance relative to existing protocols.

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Monkeypox Detection with Deep-Learning and Ensemble of Features: A Study 4

Monkeypox Detection with Deep-Learning and Ensemble of Featu...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Rajinikanth, Venkatesan Taniar, David Damaševičius, Robertas Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Division of Research and Innovation Department of Computer Science and Engineering TN Chennai602105 India Monash University Faculty of Information Technology MelbourneVIC3800 Australia Centre of Real Time Computer Systems Kaunas University of Technology Kaunas44249 Lithuania

ISBN: (纸本)9798350387490

There is a growing global prevalence of infectious diseases in the human population and it is imperative to have precise diagnosis and therapy in order to effectively cure these diseases. Monkeypox (Mpox) is an infectious disease that can cause varying degrees of skin infection, ranging from minor to severe. The recent spread of Mpox has been confirmed as Public Health Emergency of International Concern (PHEIC) due to its large infection rate. This study aims to develop a deep-learning (DL) scheme for Mpox identification. To enhance the detection accuracy, this study considered preprocessed skin images for the analysis and employed the binary classification with Ensemble of Deep-Features (EDF) using 5-fold cross validation. The outcome of this investigation validate that the suggested deep learning tool enables the attainment of a detection accuracy of 100% on the augmented images from the Monkeypox Skin Images Dataset (MSID). Compared to the existing results in the literature, this research provides a significant result on the chosen MSID database. © 2024 IEEE.

关键词： Classification Ensemble of deep-features Healthcare Illness Monkeypox

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Multi-VM-Workloads Learning-based Forecasting Model for Resilient Server Management in Industry Clouds

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Procedia computer science 2025年 260卷 923-929页

作者： Deepika Saxena Ashutosh Kumar Singh Division of Information Systems University of Aizu Aizuwakamatsu 965-0006 Fukushima Japan Department of Computer Science and Engineering Indian Institute of Information Technology Bhopal Bhopal 462003 Madhya Pradesh India

Forecasting server contention in large-scale cloud data center networks is crucial for efficient cloud resource management. The existing research primarily focuses on single-task machine learning methods for predicting resource-constrained VM failures, which often results in lower accuracy and higher computational costs. To overcome these challenges, this paper introduces a novel approach aimed at enhancing resilient server management in industry clouds. The proposed approach develops Multi-tasks-Learning-based Long Short-Term Memory for multiple VMs workload forecasting model named MVL-LSTM to simultaneously learn resource requirements of all VM hosted on a common server. It facilitates a single-step forecasting of workload resource requirements for all VM segments hosted on a server within a cluster. Experimental results using real-world Google Cluster VM datasets demonstrate that MVL-LSTM significantly improves forecasting accuracy while achieving faster execution and higher energy efficiency compared to existing single-task machine learning algorithms. Specifically, MVL-LSTM model achieves a 15% reduction in processing time and a 50% decrease in energy consumption compared with LSTM-based approach.

关键词： Cloud computing VM failure prediction MVL-LSTM Server management

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Efficient Drinking Water Quality Analysis using Machine Learning Model with Hyper-Parameter Tuning 7

Efficient Drinking Water Quality Analysis using Machine Lear...

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7th International Conference on Intelligent Computing and Control systems, ICICCS 2023

作者： Maheswari, M Uma Sudharsanan, R. Arthy, M. Jenefer, Anne Oormila, L. Samuthira Pandi, V. RMD Engineering College Department of Electrical and Electronics Engineering Tamil Nadu India SRM Institute of Science and Technology Department of Information Technology Tamil Nadu Ramapuram India SRM Institute of Science and Technology Department of Data Science and Business Systems Tamil Nadu Kattankulathur India Panimalar Engineering College Department of Electronics and Communication Engineering Tamil Nadu India Agni College of Technology Department of Computer Science Engineering Tamil Nadu India SRM Institute of Science and Technology Department of Electronics Ad Communication Engineering Tamilnadu Ramapuram India

ISBN: (纸本)9798350397253

The conventional approaches to determining the quality of water include costly and lengthy statistical and laboratory testing;hence, the concept of real-time monitoring is no longer applicable in the modern day. The disastrous implications of poor water quality demand that there be a remedy that is both more expedient and more realistic. This research study examines a variety of supervised machine learning algorithms in order to identify the quality of the water. A variety of variables, such as the pH value, the hardness, the solids, the chloramines, the sulfates, the conductivity, the organic carbon, the trihalomethanes, the turbidity, and the potability of the water, are essential for determining the quality of the water. Random Forest (RF) and Decision Tree (DT) are used in order to ascertain the caliber of the water that is suitable for human consumption. The standard laboratory approach for testing the quality of water is one that takes a lot of time and may be somewhat expensive at times. In a very short length of time, the algorithms that are proposed in this study are able to provide an estimation of the quality of drinking water. DT has a height accuracy F1 score of 99%, whereas RF has a score of 87.86% and a precision of 82.36%. The difference in these two scores is due to the fact that DT's precision is lower. The suggested method demonstrates its potential for usage in real-time water quality monitoring systems by achieving appropriate accuracy with a minimal set of parameters. To prove the usefulness of this program, this is essential. © 2023 IEEE.

关键词： Water quality

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Femoropopliteal Artery Stent Detection in Fluoroscopy Using Otsu's Preprocessing and CNN Segmentation 4

Femoropopliteal Artery Stent Detection in Fluoroscopy Using ...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Rajinikanth, Venkatesan Damaševičius, Robertas Taniar, David Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Division of Research and Innovation Department of Computer Science and Engineering TN Chennai602105 India Kaunas University of Technology Centre of Real Time Computer Systems Kaunas44249 Lithuania Monash University Faculty of Information Technolog MelbourneVIC3800 Australia

ISBN: (纸本)9798350387490

computer algorithm supported medical image analysis is a prevalent method in clinics, which effectively alleviates the diagnostic burden associated with traditional image assessment methods. This study aims to create a method that utilizes a Convolutional-Neural-Network (CNN) to accurately segment the Femoropopliteal Artery Stent (FAS) in Digital Fluoroscopy Images (DFI). In order to improve the accuracy of segmentation, this study used the combination of Otsu's tri-level thresholding and the Lévy-Flight Distribution Algorithm (LFDA). The suggested technique consists of many stages: collecting and resizing images and masks, boosting the DFI using Otsu and LFDA, using CNN-segmentation to extract the stent region, comparing segmented stent with mask, and calculating the required performance metrics. The efficacy of the suggested system is validated by analyzing the raw and thresholded DFI. The investigational results of this work affirm that this method achieves a higher level of accuracy (>96%) in segmenting images when the Otsu's preprocessed image is utilized. The experimental study employs the UNet-variants, and in the future, alternative CNN-segmentation methodologies can be utilized to validate the effectiveness of the presented scheme. © 2024 IEEE.

关键词： Convolutional neural networks

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Privacy framework for context-aware robot development

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Paladyn 2021年第1期12卷 468-480页

作者： Heuer, Tanja Schiering, Ina Gerndt, Reinhard Department of Computer Science Ostfalia University of Applied Sciences Institute of Information Engineering Wolfenbüttel Germany Department of Computer Science Ostfalia University of Applied Sciences Institute of Distributed Systems Wolfenbüttel Germany

Privacy is an essential topic in (social) robotics and becomes even more important when considering interactive and autonomous robots within the domestic environment. Robots will collect a lot of personal and sensitive information about the users and their environment. Thereby, privacy does consider the topic of (cyber-)security and the protection of information against misuse by involved service providers. So far, the main focus relies on theoretical concepts to propose privacy principles for robots. This article provides a privacy framework as a feasible approach to consider security and privacy issues as a basis. Thereby, the proposed privacy framework is put in the context of a user-centered design approach to highlight the correlation between the design process steps and the steps of the privacy framework. Furthermore, this article introduces feasible privacy methodologies for privacy-enhancing development to simplify the risk assessment and meet the privacy principles. Even though user participation plays an essential role in robot development, this is not the focus of this article. Even though user participation plays an essential role in robot development, this is not the focus of this article. The employed privacy methodologies are showcased in a use case of a robot as an interaction partner contrasting two different use case scenarios to encourage the importance of context awareness. © 2021 Tanja Heuer et al., published by De Gruyter.

关键词： Machine design

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User Acceptance of New HCI Technologies 6

User Acceptance of New HCI Technologies

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6th IEEE International Conference and Workshop Obuda on Electrical and Power engineering, CANDO-EPE 2023

作者： Katona, Jozsef Kovari, Attila Institute of Electronics and Communication Systems Obuda University Kandó Kálmán Faculty of Electrical Engineering Budapest Hungary 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 Kecskemét Hungary Institute of Digital Technology Eszterházy Károly Catholic University Faculty of Computer Science Eger Hungary Institute of Engineering University of Dunaujvaros Department of Natural Sciences Dunaujvaros Budapest Hungary

ISBN: (纸本)9798350328752

Human-computer interaction (HCI) is an evolving field of research that focuses on understanding and improving the communication and interaction between humans and computers. Over the past decades, we have seen many significant advances in this field, which have contributed to the widespread adoption and integration of technology into our daily lives. HCI research and development aims to design information technology systems to meet the needs and preferences of users. Usability, efficiency, accessibility and user satisfaction are important considerations in HCI design. This paper presents the results of a pilot study on user acceptance of HCIs. The results show that users are positive about and willing to use HCIs. © 2023 IEEE.

关键词： Human computer interaction

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