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Hybrid Whale Optimization Algorithm for Resource Optimization in Cloud E-Healthcare Applications

Computers, Materials & Continua 2022年第6期71卷 5659-5676页

作者： Punit Gupta Sanjit Bhagat Dinesh Kumar Saini Ashish Kumar Mohammad Alahmadi Prakash Chandra Sharma School of Computing&Information Technology Manipal University JaipurJaipur303007India School of Basic Sciences Manipal University JaipurJaipur303007India Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah23218Saudi Arabia

In the next generation of computing environment e-health care services depend on cloud *** Cloud computing environment provides a real-time computing environment for e-health care *** these services generate a huge number of computational tasks,real-time computing and comes with a deadline,so conventional cloud optimizationmodels cannot fulfil the task in the least time and within the *** overcome this issue many resource optimization meta-heuristic models are been proposed but these models cannot find a global best solution to complete the task in the least time and manage utilization with the least simulation *** order to overcome existing issues,an artificial neural-inspired whale optimization is proposed to provide a reliable solution for healthcare *** this work,two models are proposed one for reliability estimation and the other is based on whale optimization technique and neural network-based binary *** predictive model enhances the quality of service using performance metrics,makespan,least average task completion time,resource usages cost and utilization of the *** as compared to existing algorithms the proposedANN-WHOalgorithms prove to improve the average start time by 29.3%,average finish time by 29.5%and utilization by 11%.

关键词： Cloud computing whale optimization health care resource optimization

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LALISA: Adaptive Bitrate Ladder Optimization in HTTP-based Adaptive Live Streaming 36

LALISA: Adaptive Bitrate Ladder Optimization in HTTP-based A...

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36th IEEE/IFIP Network Operations and Management Symposium, NOMS 2023

作者： Tashtarian, Farzad Bentaleb, Abdelhak Amirpour, Hadi Taraghi, Babak Timmerer, Christian Hellwagner, Hermann Zimmermann, Roger Alpen-Adria-Universitat Klagenfurt Christian Doppler Laboratory Athena Austria Concordia University Department of Computer Science and Software Engineering Canada National University of Singapore School of Computing Singapore

ISBN: (纸本)9781665477161

Video content in Live HTTP Adaptive Streaming (HAS) is typically encoded using a pre-defined, fixed set of bitrate-resolution pairs (termed Bitrate Ladder), allowing play-back devices to adapt to changing network conditions using an adaptive bitrate (ABR) algorithm. However, using a fixed one-size-fits-all solution when faced with various content complexities, heterogeneous network conditions, viewer device resolutions and locations, does not result in an overall maximal viewer quality of experience (QoE). Here, we consider these factors and design LALISA, an efficient framework for dynamic bitrate ladder optimization in live HAS. LALISA dynamically changes a live video session's bitrate ladder, allowing improvements in viewer QoE and savings in encoding, storage, and bandwidth costs. LALISA is independent of ABR algorithms and codecs, and is deployed along the path between viewers and the origin server. In particular, it leverages the latest developments in video analytics to collect statistics from video players, content delivery networks and video encoders, to perform bitrate ladder tuning. We evaluate the performance of LALISA against existing solutions in various video streaming scenarios using a trace-driven testbed. Evaluation results demonstrate significant improvements in encoding computation (24.4%) and bandwidth (18.2%) costs with an acceptable QoE. © 2023 IEEE.

关键词： Bandwidth

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3D Model Management and Dynamic Loading based on GIS Database

3D Model Management and Dynamic Loading based on GIS Databas...

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2022 International Conference on Algorithms, Data Mining, and Information Technology, ADMIT 2022

作者： Wang, Jiaxing Wang, Wei Yue, Yong Zhu, Xiaohui Xi'an Jiaotong-Liverpool University Department of Computing Suzhou China School of Software Engineering Hebei Normal University Shijiazhuang China

ISBN: (数字)9781665454728

ISBN: (纸本)9781665454728

This paper proposes a new method based Binary Large Object (BLO) to more effectively manage and load a massive number of three-dimensional (3D) models in the Geographic Information System (GIS). Different from those methods of 3D model storage and loading based on file systems, the proposed method focuses on integrating 3D model management and dynamic loading from a database. In the proposed method, the 3D model is split into several nodes and dynamically loaded with the GIS while the database keeps these nodes in a binary format. It aims to significantly improve the model loading efficiency. In order to verify the loading efficiency, a loading method of data stored in a database with the file system is introduced as a baseline. Typical examples show that the proposed BLO method is supreme over the baseline in terms of loading time for rendering 3D scenes, proving its high efficiency for loading large-scale scenes. © 2022 IEEE.

关键词： Efficiency

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Reinforcement Learning-based Contention Window Adjustment for Wireless Body Area Networks 4

Reinforcement Learning-based Contention Window Adjustment fo...

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4th International Conference on Big Data Analytics and Practices, IBDAP 2023

作者： Kwon, Jung-Hyok Kim, Dongwan Kim, Eui-Jik Hallym University Smart Computing Laboratory Chuncheon Korea Republic of Dong-A University Department of Electronic Engineering Busan Korea Republic of Hallym University Division of Software Chuncheon Korea Republic of

ISBN: (纸本)9798350300192

This paper presents a reinforcement learning-based contention window adjustment for wireless body area networks (WBANs), abbreviated RL-CWA. The RL-CWA uses Q-learning to adaptively adjust the contention window according to the acknowledgment reception, thereby reducing the number of collisions. In addition, RL-CWA maintains multiple Q-tables to take into account minimum and maximum contention windows that vary depending on the user priority (UP) of the traffic. To verify the superiority of RL-CWA, an experimental simulation was conducted. The results showed that the RL-CWA obtains better performance than the existing IEEE 802.15.6 MAC. © 2023 IEEE.

关键词： Reinforcement learning

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SMART Garbage Bin Kit: Expandable and Intelligent Waste Management System using Deep Learning and IoT for Modern Organizations 21

SMART Garbage Bin Kit: Expandable and Intelligent Waste Mana...

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21st International Conference on Advances in ICT for Emerging Regions, ICter 2021

作者： Hewagamage, Pasindu Mihiranga, Amith Perera, Dananjaya Fernando, Rahul Thilakarathna, Thusithanjana Kasthurirathna, Dharshana Faculty of Computing Department of Software Engineering Malabe Sri Lanka

ISBN: (纸本)9781665466837

According to published statistics, Sri Lanka produces garbage around 7000MT per day, and every organization directly contributes this national amount depending on the waste management practices. 'Waste contamination' is a critical issue that affects waste management, and it should be addressed during the garbage collection process. This has led to environmental hazards resulting in health and other social issues. Hence, it is a responsibility of an organization to separate the garbage during the collection process using a suitable technique. In this paper, we are proposing a smart garbage bin kit that automates the separation of garbage collection, which minimizes human error using AI-based technologies. IoT-based devices connected to a smart garbage bin kit guide the user to the correct bin. At the same time, our proposed system can be easily expanded for new special waste categories as well. The other important issue of the current garbage management is improper time management of the garbage removal process in organizations. This happens due to the lack of real-time data on waste bins, and collection is based on the fixed time interval irrespective of the status and location of garbage bins. In the proposed system of SMART Garbage Bin Kit, the group of all interconnected garbage bins is monitored in real-time to identify the optimum collection path considering the location and the status of garbage bins using an optimized algorithm. Hence, the study presented in this paper integrates several intelligent approaches together with IoT based network to build a cutting-edge device, declared as SMART Garbage Bin kit. The prototype system has been built as a part of the research study to demonstrate its feasibility and sustainability. © 2021 IEEE.

关键词： Edge computing

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Metamorphic Relation Patterns for Metamorphic Testing, Exploration and Robustness

Metamorphic Relation Patterns for Metamorphic Testing, Explo...

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作者： Ying, Zhihao Towey, Dave Bellotti, Anthony Chua, Caslon Quan Zhou, Zhi School of Computer Science University of Nottingham Ningbo China Zhejiang Ningbo China Department of Computer Science and Software Engineering Swinburne University of Technology HawthornVIC Australia School of Computing and Information Technology University of Wollongong WollongongNSW Australia

Metamorphic testing (MT) is an effective software quality assurance method;it uses metamorphic relations (MRs) to examine the inputs and outputs of multiple test cases. Metamorphic exploration (ME) and metamorphic robustness testing (MRT) are advanced additions to the MT literature: ME can enable better understanding, and thus testing, of systems;MRT can test software robustness without an oracle. Metamorphic relation patterns (MRPs) are abstractions or templates for multiple concrete MRs. The performance of MT/ME/MRT relies on the quality of MRs, but the identification of MRs is often a manual task requiring some knowledge of MRPs, creative thinking and a good understanding of the system. In other words, the effective identification of MRs remains a challenge and requires further study. In this paper, we propose formal definitions to describe the relationship between multiple MRPs in order to classify them. We also identify a series of new MRPs and propose a new MT framework that can guide the identification and application of MRPs. Based on our definitions, we present the notion of MRP family trees and further propose two trees by classifying previously published MRPs and our proposed MRPs, in order to make it easier for users to find their target MRPs for reuse, reference or inference. Finally, we report on case studies using the MRPs and our framework to guide the identification of MRs for MT/ME/MRT and successfully detect multiple violations. © 2025 John Wiley & Sons Ltd.

关键词： software testing

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Recent Studies on the Effects of Refactoring in software Quality: Challenges and Open Issues 2

Recent Studies on the Effects of Refactoring in Software Qua...

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2nd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2022

作者： Almogahed, Abdullah Omar, Mazni Zakaria, Nur Haryani Taiz University Faculty of Engineering and Information Technology Department of Software Engineering Taiz Yemen Universiti Utara Malaysia School of Computing Sintok06010 Malaysia

ISBN: (数字)9781665461818

ISBN: (纸本)9781665461818

Refactoring is widely thought to improve software quality. Several studies, however, claimed that the effect of refactoring on software quality could be positive, negative, or have no effect. When using refactoring techniques to improve software quality, software developers face difficulties in selecting appropriate techniques. There has been no research into the contradictions in the effect of refactoring on software quality. Therefore, this study reviewed 30 recent empirical studies on the effect of various refactoring techniques on various internal and external software quality attributes. This review has three goals: first, to identify related empirical studies;second, to identify the applied refactoring techniques, internal and external quality attributes, and third, to analyze the relationship between refactoring and software quality. There were 18 studies that found that refactoring improves software quality, and 12 studies that found the opposite. More research is needed to understand the trade-off between improving and worsening various attributes. Current challenges and open issues that need further investigation have been identified and discussed. © 2022 IEEE.

关键词： Computer software selection and evaluation

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Identification of Candidate Drugs and Molecular Pathways for Pulmonary Arterial Hypertension and Chronic Kidney Disease 5

Identification of Candidate Drugs and Molecular Pathways for...

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5th International Conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Hosen, Md. Faruk Yasmin, Mst. Farjana Basar, Md. Abul Trisha, Arifa Ferdoushi Shadhin, Khairul Alam Mollah, Md. Sarwar Uddin, Muhammad Shahin Department of Computing and Information System Ashulia Dhaka Bangladesh Department of Computer Science and Engineering Purbachal American CityKanchon 1460 Bangladesh Department of Software Engineering Ashulia Dhaka Bangladesh Mawlana Bhashani Science and Technology University Department of Information and Communication Technology Tangail1902 Bangladesh

ISBN: (纸本)9798350394290

Pulmonary arterial hypertension (PAH) is biggest preventable cause of early deaths, and hence one of the global preferences of World Health Organization for determent. PAH also induces the vast majority of patients with chronic kidney disease (CKD). Both PAH and CKD are inherently connected, but the common molecular pathways and genes of these two disorders have not yet been demonstrated. In this study, we aimed to determine the similar molecular pathways and therapeutic hub proteins in PAH and CKD that may be used to anticipate the progression of the disease. We used Limma package to conduct differential analysis of gene transcripts of PAH and CKD obtained from the Gene Expression repository. The functional annotations of the genes were determined with the use of pathway analysis. Then proteinâprotein interaction (PPI) networks were constructed to determine the hub proteins, and a clustering technique was applied to determine the most significant PPI elements. We separately evaluated the PAH and CKD gene expression-based datasets and discovered 30 differentially expressed genes (DEGs) shared by both PAH and CKD. The pathways of KEGG revealed that the most prevalent DEGs are associated with the Malaria and African trypanosomiasis. Gene ontology (GO), TF and miRNA analysis and module investigation is treated the forthcoming work of this study. Finally, various drug compounds have been suggested based on the concordant DEGs. © 2023 IEEE.

关键词： Proteins

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Systematic analysis of artificial intelligence in the era of industry 4.0

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Journal of Management Analytics 2023年第1期10卷 89-108页

作者： Weiru Chen Wu He Jiayue Shen Xin Tian Xianping Wang Department of Information Technology&Decision Science Old Dominion UniversityNorfolkVAUSA Department of Computer&Information Science Arkansas Tech UniversityRussellvilleARUSA Department of Engineering Technology SUNY Polytechnic InstituteUticaNYUSA College of Computing and Software Engineering Kennesaw State UniversityMariettaGAUSA Computer Information Technology&Graphics Department Purdue University NorthwestHammondINUSA

Artificial Intelligence has been playing a profound role in the global economy,social progress,and people’s daily *** the increasing capabilities and accuracy of AI,the application of AI will have more impacts on manufacturing and service areas in the era of industry *** study conducts a systematic literature review to study the state-of-the-art on AI in industry *** paper describes the development of industries and the evolution of *** paper also identifies that the development and application of AI will bring not only opportunities but also challenges to industry *** findings provide a valuable reference for researchers and practitioners through a multi-angle systematic analysis of *** the era of industry 4.0,AI system will become an innovative and revolutionary assistance to the whole industry.

关键词： Industry 4.0 AI deep learning neural network supervised learning unsupervised learning reinforced learning

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Client Segmentation and Customization in E-Commerce: Applications of Machine Learning from a Management Perspective

Client Segmentation and Customization in E-Commerce: Applica...

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2024 International Conference on Communication, Computer Sciences and engineering, IC3SE 2024

作者： Sriprasadh, K. Palit, Shamik Pravallika, B. Manjunatha Lenka, Reena Singla, Atul Srm Institute Of Science And Technology Faculty Of Science And Humanities Department Of Computer Science And Applications Chennai Vadapalani India University Of Stirling Rak Campus Department Of Computing Science And Software Engineering United Arab Emirates Institute Of Aeronautical Engineering Dundigal Hyderabad India New Horizon College Of Engineering Department Of Mechanical Engineering Bangalore India University Pune India Lovely Professional University Phagwara India

ISBN: (纸本)9798350366846

A machine learning-based e-commerce personalised recommendation system helps address the issue of information overload that inevitably arises when consumers have more and more options in e-commerce, leading to an increasingly complex structure. Customer segmentation is a crucial component of contemporary marketing strategy since it enables companies to effectively adjust their advertising campaigns and customise their communications with customers. Through the analysis of enormous quantities of consumer behaviour data and the discovery of trends that can be utilised for dividing customers into segments, ML algorithms provide a potent tool for automating the procedure of customer segmentation. We report our research and results for identifying a customer's segment. In order to perform machine learning, we integrate the Python Tensorflow library with Pandas for manipulating data frames. To address any discrepancies or gaps in the collected client data, it is refined. Key features are found, and to find hidden patterns and relationships, an exploratory analysis of the data is done. After that, we decide on a suitable machine learning technique to divide the clientele into various groups. We also experiment with several clustering models, including conventional machine learning techniques. © 2024 IEEE.

关键词： Python

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