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THE INFLUENCE OF COVID-19 ON EMPLOYEES’ USE OF ORGANIZATIONAL information SYSTEMS

Interdisciplinary Journal of information, Knowledge, and Management 2023年 18卷 353-368页

作者： Nakash, Maayan Bouhnik, Dan Department of Information Science Bar-Ilan University Ramat Gan Israel Department of Computer Science and Engineering Jerusalem College of Technology Jerusalem Israel

Aim/Purpose COVID-19 was an unprecedented disruptive event that accelerated the shift to remote work and encouraged widespread adoption of digital tools in organizations. This empirical study was conducted from an organizational-strategic perspective, with the aim of examining how the COVID-19 pandemic outbreak affected employees’ use of organizational information systems (IS) as reflected in frequency. Background To date, only a limited effort has been made, and a rather narrow perspective has been adopted, regarding the consequences of the adoption of new work environments following COVID-19. It seems that the literature is lacking in information regarding employee use of organizational IS since the outbreak of the pandemic. Specifically, this issue has not yet been examined in relation to employees’ perception about the organization’s digital efforts and technological maturity for remote work. The present study bridges this gap. Methodology The public sector in Israel, which employs about a third of the Israeli workforce, was chosen as a case study of information-intensive organizations. During the first year of COVID-19, 716 questionnaires were completed by employees and managers belonging to four government ministries operating in Israel. The responses were statistically analyzed using a Chi-Square and Spearman’s Rho tests. Contribution Given that the global pandemic is an ongoing phenomenon and not a passing episode, the findings provide important theoretical and practical contributions. The period prior to the COVID-19 pandemic and the period of the pandemic are compared with regard to organizational IS use. Specifically, the study sheds new light on the fact that employee perceptions motivated increased IS use during an emergency. The results contribute to the developing body of empirical knowledge in the IS field in the era of digital transformation (DT). Findings More than half of the respondents who reported that they did not use IS before COVID-19 stated

关键词： COVID-19

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Know How Much Sensitive Precision and Recall Validity Measures Are? 27th

Know How Much Sensitive Precision and Recall Validity Measu...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Mishra, Sumit Swain, Srinibas Prakash, Ved Department of Computer Science and Engineering Rajiv Gandhi Institute of Petroleum Technology Amethi Jais India Department of Computer Science and Engineering Indian Institute of Information Technology Guwahati Guwahati India

ISBN: (纸本)9783031781650

For the performance evaluation of the clustering algorithm, evaluation metrics are used. For this purpose, the obtained set of clusters are compared with the actual set of clusters (or gold standard). Various evaluation metrics have been proposed in the past. One important question regarding these evaluation metrics is – how good are these metrics for evaluating the performance of the clustering algorithm? Wagner et al. have proposed some of the properties of these evaluation metrics. The evaluation metric should also have a high sensitivity value to capture the change in the clustering result/gold standard along with these properties. In this paper, we compute the sensitivity of two commonly used evaluation metrics – Precision and Recall. We also show that the sensitivity of Precision and Recall is polynomial with respect to the number of data-points. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Clustering algorithms

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Integrated Crowd Counting System Utilizing IoT Sensors, OpenCV and YOLO Models for Accurate People Density Estimation in Real-Time Environments 1

Integrated Crowd Counting System Utilizing IoT Sensors, Open...

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1st International Conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Jayasingh, Suvendra Kumar Naik, Pintu Swain, Satyaprakash Patra, Kumar Janardan Kabat, Manas Ranjan Institute of Management and Information Technology BPUT Department of Computer Science and Engineering Odisha Cuttack India Indian Institute of Technology Department of Computer Science and Engineering Jammu India

ISBN: (纸本)9798350361780

Real-time crowd monitoring plays a pivotal role in effectively managing public spaces and ensuring safety. This study investigates the fusion of IoT devices and the YOLO object detection model to accurately count crowds. IoT devices facilitate the instantaneous collection of data from cameras, while the YOLO model adeptly identifies individuals within recorded video frames. The study rigorously assesses the performance of three YOLO variants: YOLO V5, YOLO V8 and YOLO V8 NAS. Findings reveal that YOLO V8 NAS surpasses YOLO V5 and YOLO V8 in mean average precision (mAP), achieving an exceptional mAP of 95.1%. This heightened precision is attributed to the integration of Neural Architecture Search (NAS) into the YOLO V8 NAS model, fine-tuning its architecture specifically for crowd counting tasks. It analyzes various networking models proposed in earlier studies for analyzing crowded scenes in public spaces. It emphasizes the potential of a hybrid model involving an IP camera module and a Deep Neural Network for effective crowd sensing. In this setup, the IP camera captures video footage, while the DNN detects individuals and assesses crowd density based on the count of people recognized. This approach presents an encouraging solution for real-time crowd monitoring and management in public environments. © 2024 IEEE.

关键词： Face recognition

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Efficient Data Storage: Leveraging Blockchain and IPFS Techniques for Cost Minimization 10

Efficient Data Storage: Leveraging Blockchain and IPFS Techn...

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10th IEEE Smart World Congress, SWC 2024

作者： Chou, Shih-Fan Qiu, Che-Yu National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798331520861

The rapid growth of diverse information technology applications has increased the challenge of securing and efficiently managing large volumes of data. Innovative solutions like blockchain technology and distributed file systems such as InterPlanetary File System (IPFS) are emerging to address data heterogeneity, security and privacy from various applications. The fundamental differences between blockchain and IPFS, coupled with data characteristics, will influence data storage and management. Despite the blockchain's benefits of decentralization, immutability, and transparency in preserving crucial data, uploading to the chain involves significant time and financial costs, especially for large and frequently modified data. In comparison to blockchain, IPFS provides a cost-effective decentralized storage scheme. Towards this, we delve into developing a cost-effective data storage system, taking into account factors such as network bandwidth, IPFS storage usage, and blockchain busyness to design a data storage decision-making mechanism that integrates blockchain and IPFS to meet the specific data characteristics. Hence, the most appropriate data storage and management method can be tailored to various application scenarios. We then conduct a series of experiments to evaluate the performance of our proposed approach. Experimental results show the effectiveness of our approaches in terms of storage cost and scalability. © 2024 IEEE.

关键词： Storage management

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An optimized cluster validity index for identification of cancer mediating genes

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Multimedia Tools and Applications 2024年 1-21页

作者： Hazra, Subir Ghosh, Anupam Department of Information Technology Meghnad Saha Institute of Technology Kolkata700150 India Department of Computer Science & amp Engineering Netaji Subhash Engineering College Techno City Panchpota Garia Kolkata700152 India

One of the major challenges in bioinformatics lies in identification of modified gene expressions of an affected person due to medical ailments. Focused research has been observed till date in such identification, leading to multiple proposals pivoting in clustering of gene expressions. Moreover, while clustering proves to be an effective way to demarcate the affected gene expression vectors, there has been global research on the cluster count that optimizes the gene expression variations among the clusters. This study proposes a new index called mean-max index (MMI) to determine the cluster count which divides the data collection into ideal number of clusters depending on gene expression variations. MMI works on the principle of minimization of the intra cluster variations among the members and maximization of inter cluster variations. In this regard, the study has been conducted on publicly available dataset comprising of gene expressions for three diseases, namely lung disease, leukaemia, and colon cancer. The data count for normal as well as diseased patients lie at 10 and 86 for lung disease patients, 43 and 13 for patients observed with leukaemia, and 18 and 18 for patients with colon cancer respectively. The gene expression vectors for the three diseases comprise of 7129,22283, and 6600 respectively. Three clustering models have been used for this study, namely k-means, partition around medoid, and fuzzy c-means, all using the proposed MMI technique for finalizing the cluster count. The Comparative analysis reflects that the proposed MMI index is able to recognize much more true positives (biologically enriched) cancer mediating genes with respect to other cluster validity indices and it can be considered as superior to other with respect to enhanced accuracy by 85%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Lung cancer

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A comprehensive examination of load balancing algorithms in cloud environments: a systematic literature review, comparative analysis, taxonomy, open challenges, and future trends

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Iran Journal of computer science 2024年第3期7卷 663-698页

作者： Prity, Farida Siddiqi Hossain, Md. Maruf Department of Information and Communication Engineering Noakhali Science and Technology University Noakhali 3814 Bangladesh Department of Information and Communication Engineering Pabna University of Science and Technology Pabna 6600 Bangladesh Department of Computer Science and Engineering Shanto-Mariam University of Creative Technology Dhaka 1230 Bangladesh

Cloud computing is a robust paradigm that empowers users and organizations to procure services tailored to their needs. This model encompasses many offerings, including storage solutions, platforms for seamless deployment, and convenient access to web services. Load balancing, a fundamental pillar in cloud computing, is crucial in distributing requests across multiple servers to optimize resource utilization and reduce response times. However, load balancing presents a common challenge in the cloud environment, as it hampers the ability to maintain optimal application performance while adhering to the stringent requirements of Quality of Service (QoS) measurements and Service Level Agreement (SLA) compliance mandated by cloud providers to enterprises. The equitable workload distribution across servers poses a significant challenge for cloud providers. Hence, an efficient load-balancing technique should optimize resource utilization in Virtual Machines (VMs) to ensure maximum user satisfaction and overall system efficiency. However, existing review papers on load balancing in cloud environments often exhibit limitations, lacking in-depth analyses, graphical representations, and comprehensive evaluations of performance metrics. This review paper aims to fill these gaps by providing a novel taxonomy of load balancing algorithms divided into four categories (types of algorithms, nature of problem, metrics, and simulation tools) and thoroughly examining their objectives, parameters, and operational flows. It evaluates the strengths and weaknesses of these algorithms, considering their nature and type, and employs qualitative QoS parameter-based criteria for effectiveness evaluation. The paper also includes a comparative analysis of simulation tools, visual representations, and experimental results. By offering valuable insights, open issues, recommendations, and future directions, this review paper equips researchers, practitioners, and cloud service providers with the k

关键词： Algorithms Cloud computing Load balancing Taxonomy

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Unsupervised Classification of Hyper Spectral Images using Feature Extraction and Fuzzy Logic

Unsupervised Classification of Hyper Spectral Images using F...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Kannagi, A. Chaudhary, Chetan Seth, Jyoti Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India

ISBN: (纸本)9798350383348

Hyperspectral pictures are complicated information items with high spectral resolution, making their categorization and analysis timeingesting and challenging. Traditional strategies for classifying hyperspectral pix may be unreliable and gradually attributable to the presence of diverse noise resources and a high range of pixels. This paper proposes a new unsupervised classification approach for hyperspectral pictures using function extraction and fuzzy common sense. The method starts by first using feature extraction techniques on the hyperspectral pictures to lessen the dimensionality of the facts. Numerous characteristic extraction algorithms, including primary thing analysis (PCA) and impartial component evaluation (ICA), are tested to determine which function extraction algorithms yield satisfactory effects. The reduced function area is then used as an entry for the fuzzy category system. The bushy common sense device is used to classify the hyperspectral pix into distinctive classes according to the extracted capabilities. Experimental results display that the proposed method achieves proper effects for the category venture with classification accuracy accomplishing as high as 79%. The proposed technique demonstrates advanced performance over conventional category strategies in terms of each accuracy and speed. Hyperspectral pics (HSI) offer valuable statistics approximately the environment and the functions gift inside it. But, the sheer quantity of facts present in HSI makes guide evaluation of those photos a time-eating and exhausting project. As such, there is a growing demand for robust and reliable automated techniques to analyze HSI. In this context, unsupervised tactics for classifying HSI have gained interest due to their ability to examine facts without requiring manually categorized education facts. Fuzzy logic is one method being explored for unsupervised HSI type due to its capability to assign more than one label to pixels of the image and its r

关键词： analysis classification hyperspectral primarily satisfactory

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Automating Object Identification from Hyper Spectral Data with Markov Random Fields

Automating Object Identification from Hyper Spectral Data wi...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Chaudhary, Chetan Devrani, Rekha Aadil, Mir Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

Hyper Spectral statistics (HSD) is a type of fact acquired from optical sensors, which include satellites, scanners, and microscopes that captures and measures various wavelengths of energy. Object identification from HSD is problematic because of the high spectral variability in pixels of the image and the great variety of product classes usually present. Markov Random Fields (MRF) is a graphical model that may be used to version spatial dependencies among neighboring pixels which can assist in decorating the object identification process. This summary explores the feasibility of automating item identity from HSD through MRF. We introduce the idea of a priori information, which includes labels assigned to distinct spectral signatures within the image and a classification system that uses MRF as a priori information to enhance object identity accuracy. We describe and demonstrate the exact rules for this challenge via simulation. The consequences of the take look advocate that utilizing the MRF-based category gadget to improve item identification accuracy from HSD is possible. © 2024 IEEE.

关键词： classification consequences Hyper Spectral microscopes neighboring

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Automating Optimal Healthcare Delivery Through Hyper Spectral Imaging in Sustainable Medical Environments

Automating Optimal Healthcare Delivery Through Hyper Spectra...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Seth, Jyoti Kavitha, R. Yadav, Dhyan Chandra Vivekananda Global University Department of Computer Science & Engineering Jaipur India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350383348

Hyperspectral imaging (HSI) is a progressive imaging method being explored, which will aid in the automation of foremost healthcare transport in sustainable scientific environments. HSI requires an enormous variety of spectral bands compared to other spectral imaging methods, including visible mild imaging. This more statistics let in for a greater sure and correct evaluation of the spectral information within an image, presenting more remarkable sturdy outcomes while assessing the health of clinical situations. The information accrued thru HSI can be used to create automatic healthcare shipping models which could autonomously adjust and reply to changes in the clinical situation of an affected person. Similarly, this technology may be used to hit upon ailments, including cancer, offering a treasured perception of a patient's health and considering the selection of timely and appropriate treatments. Moreover, HSI can be used to create green and sustainable clinical surroundings, as it requires reduced personnel, area and fewer patient trips to and from the health facility. This technology can lower prices and improve patient consequences by providing automated selections for healthcare professionals. © 2024 IEEE.

关键词： autonomously Hyperspectral information progressive imaging

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Identifying Authentic Reviews on Amazon Dataset using Transformer-Based Models

Identifying Authentic Reviews on Amazon Dataset using Transf...

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2024 International Conference on IT and Industrial Technologies, ICIT 2024

作者： Azam, Hadiqa Chaudhry, Hassan Nazeer Majid, Muhammad Computer Department University of Engineering and Technology Taxila Pakistan Computer Science Department Barani Institute of Information Technology Rawalpindi Pakistan

ISBN: (纸本)9798331504465

Reviews and ratings significantly impact businesses, either boosting their reputation or harming competitors. To manipulate these outcomes, some businesses falsify product reviews. This paper introduces a transformer-based model that utilizes GloVe (Global Vectors for Word Representation) semantic embeddings to detect fraudulent reviews. Our approach captures subtle linguistic patterns indicative of fake reviews by fine-tuning a pre-trained GloVe model on a labelled dataset. The GloVe embeddings improve the model’s grasp of word semantics, while an attention-based neural network classifies reviews as genuine or fraudulent. Our model has surpassed traditional machine learning methods, achieving 98% accuracy across various datasets. © 2024 IEEE.

关键词： Adversarial machine learning

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