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Survey on Task Scheduling Optimization Strategy under Multi-Cloud Environment

computer Modeling in Engineering & Sciences 2023年第6期135卷 1863-1900页

作者： Qiqi Zhang Shaojin Geng Xingjuan Cai School of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan030024China College of Electronics and Information Engineering Tongji UniversityShanghai201804China

Cloud computing technology is favored by users because of its strong computing power and convenient *** the same time,scheduling performance has an extremely efficient impact on promoting carbon ***,scheduling research in the multi-cloud environment aims to address the challenges brought by business demands to cloud data centers during peak ***,the scheduling problem has promising application prospects under themulti-cloud *** paper points out that the currently studied scheduling problems in the multi-cloud environment mainly include independent task scheduling and workflow task scheduling based on the dependencies between *** paper reviews the concepts,types,objectives,advantages,challenges,and research status of task scheduling in the multi-cloud *** scheduling strategies proposed in the existing related references are analyzed,discussed,and summarized,including research motivation,optimization algorithm,and related ***,the research status of the two kinds of task scheduling is compared,and several future important research directions of multi-cloud task scheduling are proposed.

关键词： Cloud computing task scheduling workflow review multi-cloud environment

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Privacy-preserving multi-keyword hybrid search over encrypted data in cloud

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 261-274页

作者： Singh, Niharika Kumar, Jitendra Singh, Ashutosh Kumar Mohan, Anand School of Computer Sciences Bennett University Uttar Pradesh Noida India CISIR Department of Electrical and Electronics Engineering University Teknologi Petronas Perak Seri Iskandar Malaysia Department of Computer Applications National Institute of Technology Tiruchirappalli Tamilnadu Tiruchirappalli India Department of Computer Applications National Institute of Technology Kurukshetra Haryana Kurukshetra India Department of Electronics Engineering Indian Institute of Technology-BHU Uttar Pradesh Varanasi India

Data outsourcing to the cloud is gaining popularity day by day in cloud computing. It requires a dynamic architecture that avoids loopholes and stops intruders from entering the cloud environment. To retrieve the enciphered documents from the cloud, the latest technique that is enrolled effectively is multi-keyword fuzzy search. The proposed research work uses this technique along with the n-gram corpus algorithm that helps to achieve relevancy score and sub-linear search time. This algorithmic combination is able to predict the privacy ranking measure of the retrieved data. To validate the opposed approach, simulation analysis has been performed to preserve user-oriented privacy in a cloud environment. The ranking precision of the proposed method is compared with (Fu et al. in IEEE Trans Inf Forensics Secur 12(8):1874–1884. https://***/10.1109/TIFS.2017.2692728, 2017) which uses a stemming algorithm and a matching algorithm based on the inner product. The results express the efficiency of the proposed approach by showing a relative improvement in security precision up to 55%. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.

关键词： Privacy-preserving techniques

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Text Simplification Using Transformer and BERT

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computers, Materials & Continua 2023年第5期75卷 3479-3495页

作者： Sarah Alissa Mike Wald College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal UniversityDammamSaudi Arabia School of Electronics and Computer Science University of SouthamptonSouthamptonUnited Kingdom

Reading and writing are the main interaction methods with web *** simplification tools are helpful for people with cognitive impairments,new language learners,and children as they might find difficulties in understanding the complex web *** simplification is the process of changing complex text intomore readable and understandable *** recent approaches to text simplification adopted the machine translation concept to learn simplification rules from a parallel corpus of complex and simple *** this paper,we propose two models based on the transformer which is an encoder-decoder structure that achieves state-of-the-art(SOTA)results in machine *** training process for our model includes three steps:preprocessing the data using a subword tokenizer,training the model and optimizing it using the Adam optimizer,then using the model to decode the *** first model uses the transformer only and the second model uses and integrates the Bidirectional Encoder Representations from Transformer(BERT)as encoder to enhance the training time and *** performance of the proposed model using the transformerwas evaluated using the Bilingual Evaluation Understudy score(BLEU)and recorded(53.78)on the WikiSmall *** the other hand,the experiment on the second model which is integrated with BERT shows that the validation loss decreased very fast compared with the model without the ***,the BLEU score was small(44.54),which could be due to the size of the dataset so the model was overfitting and unable to generalize ***,in the future,the second model could involve experimenting with a larger dataset such as the *** addition,more analysis has been done on the model’s results and the used dataset using different evaluation metrics to understand their performance.

关键词： Text simplification neural machine translation transformer

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PERFORMANCE ENHANCEMENT OF CIRCULARLY POLARIZED MICROSTRIP ANTENNA USING SINGLE-LAYER FOAM SUBSTRATE FOR 5.8 GHZ ISM BAND APPLICATIONS

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Telecommunications and Radio Engineering (English translation of Elektrosvyaz and Radiotekhnika) 2025年第2期84卷 43-65页

作者： Hake, Shahadev Jaiswal, Rupesh Moon, Sunil Nikam, Pritam Kumar, Jayendra Department of Electronics and Telecommunication Engineering SCTR’s Pune Institute of Computer Technology Pune India Department of Electronics and Telecommunication Engineering KIT’s College of Engineering Kolhapur India School of Electronics Engineering VIT-AP University Andhra Pradesh India

Since last decade, a microstrip patch antenna has played a very important role in industrial, scientific, and medical (ISM) band applications, but single-layer antennae suffer from low gain and low radiation efficiency. For an ISM band, there is always a trade-off between these parameters, so maintaining it on a single-layer microstrip antenna is a big challenge. A circularly polarized (CP) microstrip antenna using a single-layer foam substrate has been designed for 5.8 GHz ISM band which offers high gain, improved radiation efficiency, and a lightweight design. It is constructed as a low-profile, single coaxial feed microstrip antenna on a foam substrate, achieving circular polarization by integrating two slots into the radiating patch antenna has dimensions of 0.98 λ0 × 0.98 λ0 × 0.096 λ0, impedance bandwidth of 600 MHz (5.56–6.16) GHz, which underscores its adaptability across a wide range of frequencies within the specified band. Furthermore, it elaborates the axial ratio bandwidth which extends over 130 MHz (5.77–5.90) GHz, showing the antenna’s ability to maintain circular polarization characteristics over a significant frequency range with a gain of 8.87 dB. The experimental and simulation results confirmed a remarkable radiation efficiency of 95.62%, showing its superior effectiveness compared to any existing single-layer microstrip antenna. © 2025 by Begell House, Inc.

关键词： Slot antennas

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Image based Android Malware Detection System using ResNet50 and VGG16 15

Image based Android Malware Detection System using ResNet50 ...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Gundla, Rajeshwari Gengaje, Sachin R. Department of Computer Science & Engineering Walchand Institute of Technology Solapur India Department of Electronics and Computer Engineering Walchand Institute of Technology Solapur India

ISBN: (纸本)9798331300579

Attacks on mobile devices, such as smartphones and tablets, have increased as a result of their rising popularity. One of the most significant risks is mobile malware, which may lead to both security breaches and financial losses. Mobile malware will probably continue to develop and spread since it is used to commit different types of cybercrimes on mobile devices. As the Android operating system has gained popularity, mobile malware targets it especially. Users' security is seriously jeopardised by the quick spread of Android malware apps, which also makes it challenging to manually and statically analyse harmful files. Therefore, it is essential to accurately identify and categorise Android harmful files. There has been some discussion of Convolutional Neural Network (CNN) based approaches in this area, however there is still opportunity for performance enhancement. In this work, using two well-known deep learning models, ResNet-50 and VGG16, we present a transfer learning strategy to effectively detect the Android malware files. By converting malicious and benign APK files into grayscale images, the proposed model is trained on the AndroHealthCheck dataset. On the AndroHealthCheck dataset, our model outperforms state-of-the-art works in terms of accuracy, recall, precision, and F1 measures. © Grenze Scientific Society, 2024.

关键词： Android malware

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COVID-19 Pandemic: A Worldwide Critical Review with the Machine Learning Model-Based Prediction

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Journal of The Institution of Engineers (India): Series B 2025年第1期106卷 339-349页

作者： Misra, Neeraj Kumar Das, Sanchali Satpathy, Sambit Addula, Santosh Reddy Trivedi, Munesh Chandra School of Electronics Engineering VIT-AP University Andhra Pradesh Amaravathi522237 India School of Computer Science Engineering and Technology Bennett University Greater Noida India Computer Science and Engineering Galgotias College of Engineering and Technology Greater Noida India Information Technology University of the Cumberlands Williamsburg United States Computer Science and Engineering PSSCIVE Bhopal Bhopal India

Machine learning (ML) with data analysis has many successful applications and is widely employed daily. Additionally, they have played a significant role in combating the global coronavirus (COVID-19) outbreak. International occurrences of the COVID-19 pandemic have resulted from the rapid global spread of the SARS-CoV-2 virus. In this study, we examine the ML and data analysis techniques for diagnosis, treatment, and impact of COVID-19, which are based on artificial intelligence (AI). Moreover, we provide an overview of the current state of the art in AI-based machine learning and deep learning techniques as well as the datasets, tools, and performance of the tools that can be used to combat COVID-19. In this survey, we have attempted to provide a comprehensive overview of the most recent state-of-the-art approaches available to researchers in Mathematical and Statistical analysis and the larger medical community as a whole. It also includes information on how data and ML can be used to improve COVID-19 status and encourage further research to prevent COVID-19 outbreaks. Future directions and problems are also described in detail. © The Institution of Engineers (India) 2024.

关键词： COVID-19

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A Survey of Machine Learning Models used in Development of Medical Chatbots 15

A Survey of Machine Learning Models used in Development of M...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Shah, Priyank Pratapwar, Manik Padia, Suraj Gandhe, Sanjay T. SCTR's Pune Institute of Computer Technology/Electronics and Telecommunication Pune India

ISBN: (纸本)9798331300579

A new era of patient care and health care has started because of the integration of artificial intelligence (AI) and machine learning (ML) technologies. This paper analyzes significant developments achieved in the fieldof medical chatbots, which are now efficient instruments for improving patient engagement, increasing access to healthcare, and accelerating medical operations. The capacity of medical chatbots, powered by AI and ML algorithms, to offer timely and individualized healthcare information, symptom assessment,and even early diagnoses, has been established. They use naturallanguage processing (NLP) methods to converse in human-like ways, making them accessible for individuals with different degrees of digital literacy. Additionally, they can be integrated with other healthcare systems and electronic health records (EHRs), facilitating seamless exchange of data while improving the client's experience. © Grenze Scientific Society, 2024.

关键词： Electronic health record

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Business Response Prediction based on Consumer Behaviour Patterns 9

Business Response Prediction based on Consumer Behaviour Pat...

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9th IEEE International Conference for Convergence in technology, I2CT 2024

作者： Pokharkar, Vishwa Edgaonkar, Omkar Shinde, Aryajeet Nirmal, Nilesh Pune Institute of Computer Technology Electronics & Telecommunications Engineering Department Pune India

ISBN: (纸本)9798350394474

This survey paper presents a comprehensive analysis of predictive models for business response in the hospitality industry, focusing on cafes and restaurants. Leveraging data from Zomato, a popular restaurant aggregator platform, and supplemented with location data, we explore effective strategies for preprocessing and clustering based on key parameters, including location, cuisine, nearby institutions, parking, traffic, bestsellers, cost, ratings, delivery options, and establishment type (cloud kitchen/dine out). We investigate a variety of machine learning and statistical approaches tailored to distinct clusters of cafes and restaurants. Our evaluation involves training and testing models to predict business responses, considering varying customer foot traffic and preferences. The findings emphasize the importance of precise clustering and customized predictive models for improved accuracy in response forecasting. The insights derived from this survey are anticipated to significantly impact decision-making processes for businesses in the hospitality sector. © 2024 IEEE.

关键词： Forecasting

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Edgraph: Dynamic GNN Inference with Multi-edge Clouds Collaboration 4

Edgraph: Dynamic GNN Inference with Multi-edge Clouds Collab...

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4th International Conference on computer Science, Electronic Information Engineering and Intelligent Control technology, CEI 2024

作者： Shi, Chenglong Xiao, Wenjing Chen, Miaojiang Wang, Rui Hao, Yixue Hu, Long Guangxi University School of Computer Electronics and Information Nanning China Huazhong University of Science and Technology School of Computer Science and Technology Wuhan China

ISBN: (纸本)9798350378542

In edge computing (EC) scenarios such as traffic flow prediction and social relationship recommendations, the emerging Graph Neural Networks (GNNs) is the promising deep learning model to tackle these applications. However, since GNNs can cause a large amount of cross-server communication during their inference process, there is still a big challenge to provide cost-efficient intelligent services in EC for GNN processing. To address this issue, this paper proposes Edgraph, an efficient EC architecture for GNN computing, with the goal of minimizing the system's energy and time cost for processing user tasks. Specifically, at each time step, the architecture first perceives the user topology and represents their data association as a graph layout. Then the graph layout is optimized by calling our proposed hierarchical traversal graph cutting algorithm (HTGC). Finally, based on the optimized graph layout, our proposed deep reinforcement learning (DRL) based graph offloading algorithm (DRLGO) is executed to obtain the optimal offloading strategy for the tasks of users. Based on this strategy, the tasks of users are offloaded to edge servers and processed by the deployed GNN model. Experimental results show the effectiveness of our proposed architecture because it can fully learn dynamic scenario information and minimize the energy and time cost of the GNN-based EC system. © 2024 IEEE.

关键词： Graph neural networks

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A Survey on Dynamic Load Balancing Techniques in Cloud Computing 2nd

A Survey on Dynamic Load Balancing Techniques in Cloud Compu...

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2nd International Conference on Emerging electronics and Automation, E2A 2022

作者： Sharmah, Daisy Bora, Kanak Chandra Department of Computer Science and Electronics University of Science and Technology Meghalaya Baridua India

ISBN: (纸本)9789819968541

Cloud computing is an area which is used by most of recent areas including business, commercial, educational and personal. The main advantage of using this is the lack of storage and database. There are various applications of cloud computing like backup and recovery, big data analytics which makes the cloud computing technology most popular. All the needed data are available in the cloud storage that can be directly accessed whenever needed. But to make the data available to various consumers, different virtual machines are used. These virtual machines are controlled by the load balancers. In this paper, we are discussing about the aim of load balancing in cloud computing. The various challenges of load balancing are also mentioned in this chapter. The types of load balancing are mentioned and they are differentiated as static and dynamic. The dynamic load balancing is focused in this paper and the basic algorithms of dynamic load balancing are discussed. The comparison study of the basic algorithms is stated based on various matrices in this paper. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Virtual machine

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