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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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IoT-Based Smart Kitchen with Enhanced and Automated Safety Measures 11

IoT-Based Smart Kitchen with Enhanced and Automated Safety M...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Murali, S. Anchitaalagammai, J.V. Kavitha, S. Jerlin Sugirtha, C. Iraimalar, B. Velammal College of Engineering and Technology Cse Madurai India Velammal College of Engineering and Technology Computer Science and Engineering Madurai India

ISBN: (纸本)9798350350357

In today's world, many technologies have emerged for the implementation of Smart homes, Smart Cities, Smart Farming, etc. We are very excited and involved in decorating our house with Smart TVs, Smartphones, Home theatres, and computers but we forget about the kitchen which is the most important part of our house. Nowadays, Liquified Petroleum Gas (LPG) is the fuel that is frequently used for cooking in the kitchen which is filled as a liquid in a cylinder. Many accidents happen because of the blast of these cylinders, mainly due to gas leakage in the kitchen. Due to LPG's high level of flammability, even a minor gas leak can pose a serious hazard to the environment. So, in order to avoid this, we must find, warn about, and stop gas leaks. Not only is the detection of gas leakage important but the control and prevention of leakage are also most important. The Internet of Things (IoT) technology is utilized in this project to lower the dangers in our kitchen. Both hardware and software will be utilized to implement this process. We use various sensors and mobile applications to detectand control gas leakage. © 2024 IEEE.

关键词： Gases

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Pattern Analysis and Regressive Linear Measure for Botnet Detection

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computer Systems science & engineering 2022年第10期43卷 119-139页

作者： B.Padmavathi B.Muthukumar Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyChennaiIndia Department of Computer Science and Engineering Easwari Engineering CollegeChennaiIndia Department of Computer Science and Engineering United Institute of TechnologyCoimbatoreIndia

Capturing the distributed platform with remotely controlled compromised machines using botnet is extensively analyzed by various ***,certain limitations need to be addressed *** provisioning of detection mechanism with learning approaches provides a better solution more broadly by saluting multi-objective *** bots’patterns or features over the network have to be analyzed in both linear and non-linear *** linear and non-linear features are composed of high-level and low-level *** collected features are maintained over the Bag of Features(BoF)where the most influencing features are collected and provided into the classifier ***,the linearity and non-linearity of the threat are evaluated with Support Vector Machine(SVM).Next,with the collected BoF,the redundant features are eliminated as it triggers overhead towards the predictor ***,a novel Incoming data Redundancy Elimination-based learning model(RedE-L)is built to classify the network features to provide robustness towards BotNets *** simulation is carried out in MATLAB environment,and the evaluation of proposed RedE-L model is performed with various online accessible network traffic dataset(benchmark dataset).The proposed model intends to show better tradeoff compared to the existing approaches like conventional SVM,C4.5,RepTree and so ***,various metrics like Accuracy,detection rate,Mathews Correlation Coefficient(MCC),and some other statistical analysis are performed to show the proposed RedE-L model's *** F1-measure is 99.98%,precision is 99.93%,Accuracy is 99.84%,TPR is 99.92%,TNR is 99.94%,FNR is 0.06 and FPR is 0.06 respectively.

关键词： BotNet threat intrusion features linearity and non-linearity redundancy regressive linear measure classification redundancy eliminationbased learning model

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Performance Analysis of ZF and RZF in Low-Resolution ADC/DAC Massive MIMO Systems

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China Communications 2024年第8期21卷 115-126页

作者： Talha Younas Shen Jin Muluneh Mekonnen Gao Mingliang Saqib Saleem Sohaib Tahir Mahrukh Liaqat School of Electrical and Electronic Engineering Shandong University of TechnologyZibo 255000China Department of Electrical and Computer Engineering COMSATS University IslamabadSahiwal Campus Department of Electrical and Computer Engineering College of EngineeringDhofar UniversitySalalah 211Oman Department of Electrical and Computer Engineering Addis Ababa Science and Technology UniversityAddis AbabaEthiopia Department of Electrical Engineering College of Electrical and Mechanical EngineeringNUSTIslamabadPakistan

Large number of antennas and higher bandwidth usage in massive multiple-input-multipleoutput(MIMO)systems create immense burden on receiver in terms of higher power *** power consumption at the receiver radio frequency(RF)circuits can be significantly reduced by the application of analog-to-digital converter(ADC)of low *** this paper we investigate bandwidth efficiency(BE)of massive MIMO with perfect channel state information(CSI)by applying low resolution ADCs with Rician *** start our analysis by deriving the additive quantization noise model,which helps to understand the effects of ADC resolution on BE by keeping the power constraint at the receiver in *** also investigate deeply the effects of using higher bit rates and the number of BS antennas on bandwidth efficiency(BE)of the *** emphasize that good bandwidth efficiency can be achieved by even using low resolution ADC by using regularized zero-forcing(RZF)combining *** also provide a generic analysis of energy efficiency(EE)with different options of bits by calculating the energy efficiencies(EE)using the achievable *** emphasize that satisfactory BE can be achieved by even using low-resolution ADC/DAC in massive MIMO.

关键词： low-bit analog-digital converter massive(multiple-input-multiple-output)MIMO minimum mean square error(MMSE) regularized zero forcing zero forcing

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Enhanced Framework for Authentic and Anonymous Data Sharing Over Cloud Storage Service

Enhanced Framework for Authentic and Anonymous Data Sharing ...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Naik, Archana Anjali, M. Ushashree, P. Sooda, Kavitha Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bangalore India Geethanjali College of Engineering and Technology Department of Computer Science and Engineering Hydrabad India B.M.S. College of Engineering Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350360660

This study introduces the concept of anonymous data exchange in banking applications using cloud computing. By intertwining innovative methodologies, the proposed framework bolsters security while introducing novel dimensions to the authentication landscape. Key to the system's architecture is the integration of RSA and SHA algorithms within the framework. In the contemporary digital landscape, the banking sector confronts escalating challenges in establishing robust authentication and security measures to safeguard sensitive customer data and transactions. Color-coded passwords offer a pioneering approach, enhancing traditional authentication methods with added security. Furthermore, the system advocates for integrate multi-factor authentication techniques, thereby reinforcing the authentication process. This integration ensures the confidentiality, integrity, and authenticity of sensitive data transmitted between clients. The performance of this integrated algorithm is compared with other encryption algorithms, and it is observed that the proposed mechanism encryption time is 10% faster. The encrypted message was tested on an online simulator to attempt deciphering and could not be decrypted, confirming its security. © 2024 IEEE.

关键词： Authentication Protocol

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Early Diagnosis of Lung Tumors for Extending Patients’ Life Using Deep Neural Networks

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computers, Materials & Continua 2023年第7期76卷 993-1007页

作者： A.Manju R.Kaladevi Shanmugasundaram Hariharan Shih-Yu Chen Vinay Kukreja Pradip Kumar Sharma Fayez Alqahtani Amr Tolba Jin Wang Department of Computer Science and Engineering SRM Institute of Science and TechnologyRamapuramChennaiIndia Department of Computer Science and Engineering Saveetha Engineering CollegeChennaiIndia Department of Computer Science and Engineering Vardhaman College of EngineeringHyderabadIndia Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin 64002Taiwan Intelligence Recognition Industry Service Research Center National Yunlin University of Science and TechnologyYunlin 64002Taiwan Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjabIndia Department of Computing Science University of AberdeenAberdeenUK Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh 12372Saudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadh 11437Saudi Arabia School of Computer&Communication Engineering Changsha University of Science&TechnologyChangshaChina

The medical community has more concern on lung cancer *** experts’physical segmentation of lung cancers is time-consuming and needs to be *** research study’s objective is to diagnose lung tumors at an early stage to extend the life of humans using deep learning ***-Aided Diagnostic(CAD)system aids in the diagnosis and shortens the time necessary to detect the tumor *** application of Deep Neural Networks(DNN)has also been exhibited as an excellent and effective method in classification and segmentation *** research aims to separate lung cancers from images of Magnetic Resonance Imaging(MRI)with threshold *** Honey hook process categorizes lung cancer based on characteristics retrieved using several *** this principle,the work presents a solution for image compression utilizing a Deep Wave Auto-Encoder(DWAE).The combination of the two approaches significantly reduces the overall size of the feature set required for any future classification process performed using *** proposed DWAE-DNN image classifier is applied to a lung imaging dataset with Radial Basis Function(RBF)*** study reported promising results with an accuracy of 97.34%,whereas using the Decision Tree(DT)classifier has an accuracy of 94.24%.The proposed approach(DWAE-DNN)is found to classify the images with an accuracy of 98.67%,either as malignant or normal *** contrast to the accuracy requirements,the work also uses the benchmark standards like specificity,sensitivity,and precision to evaluate the efficiency of the *** is found from an investigation that the DT classifier provides the maximum performance in the DWAE-DNN depending on the network’s performance on image testing,as shown by the data acquired by the categorizers themselves.

关键词： Lung tumor deep wave auto encoder decision tree classifier deep neural networks extraction techniques

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Anomaly Detection in Image Processing using Artificial Intelligence

Anomaly Detection in Image Processing using Artificial Intel...

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2023 IEEE International Conference on Device Intelligence, Computing and Communication Technologies, DICCT 2023

作者： Jain, Swati Choudhary, Naveen College of Technology and Engineering Department of Computer Science and Engineering Udaipur India College of Technology and Engineering The Department of Computer Science and Engineering Udaipur India

ISBN: (纸本)9781665474924

Any method that looks for samples that deviate from anticipated patterns is sometimes referred to as anomaly detection. Many outlier finding methods are established on the types of irregularities, various types of data labels that are available, and the applications. The aim of this paper is to give an overview and systematic evaluation of outlier finding research. We believe that the paper contributes to a better understanding of the numerous fields in various types of research done on this subject. The approaches for anomaly detection have been separated into many groups. We discuss the pros and cons of each category and look at how computationally complex the various methods are in terms of real-world application domains. We concluded the paper by providing research gaps and constraints we faced while applying artificial intelligence algorithms in practical applications. This review paper examines several outlier detection and treatment techniques in images, as well as gives better performance of various AI approaches. © 2023 IEEE.

关键词： Anomaly detection

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VSMCNN-dynamic summarization of videos using salient features from multi-CNN model

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Journal of Ambient Intelligence and Humanized Computing 2023年第10期14卷 14071-14080页

作者： Nair, Madhu S. Mohan, Jesna Artificial Intelligence & Computer Vision Lab Department of Computer Science Cochin University of Science and Technology Kerala Kochi682022 India Department of Computer Science and Engineering Mar Baselios College of Engineering and Technology Nalanchira Kerala Thiruvananthapuram695015 India

A dynamic video summarization system detects key parts of the input video to generate its compact representation. The summaries can be used for efficient management of video data. This paper proposes an approach, Video summarization based on multi-CNN model (VSMCNN), that exploits major aspects of human cognition to generate meaningful summaries from videos. As the method focuses on dynamic summarization, the input video is divided into a set of shots. A multi-CNN model, which is a combination of different pre-trained models of CNN, is used for feature extraction from shots. The salient features are extracted from high dimensional feature vector using an unsupervised feature reduction technique applied in multiple subspaces to rank features in the vector. The distance measure between feature vectors is then thresholded to detect prime parts of the tested video. Experiments are performed on SumMe dataset and the results prove that our approach is successful in detecting portions of the tested video that has an essential message. The analysis shows that the method outperforms the state-of-the-art methods in the literature. Further evaluation on comparison with human-generated summaries in the ground truth proves the effectiveness of the proposed method. The paper also presents a detailed analysis to show which combination of pre-trained models of CNN is best suitable for generating dynamic summaries. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Video recording

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Leveraging Machine Learning Models for Proactive Disaster Forecasting 1

Leveraging Machine Learning Models for Proactive Disaster Fo...

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1st International Conference on AIML-Applications for engineering and technology, ICAET 2025

作者： Affan, Daimi Syed Ahmed, Askari Arslaan Maaz, Ahmed Syed Asma, Iqbal Computer Science and Engineering Muffakham Jah College of Engineering and Technology Hyderabad India Electronics and Communication Engineering Deccan College of Engineering and Technology Hyderabad India

ISBN: (纸本)9798350355611

The increasing frequency and intensity of natural disasters, such as earthquakes, tsunamis, floods, and forest fires, necessitate the development of advanced early warning systems. Current disaster prediction systems are hampered by limitations in sensor technology, which is often expensive and primarily effective for large-scale disasters. This project aims to enhance disaster prediction accuracy and timeliness by utilizing real-time data and advanced machine learning algorithms, including XGBoost, Voting classifier, and Random Forest classifier. Additionally, recognizing the long-term health impacts of poor air quality, the project extends its scope to predict the Air Quality Index (AQI). The project seeks to enable proactive risk management and effective disaster response by integrating real-time environmental data and machine learning models. We aim to improve proactive risk management and disaster response by employing real-time data. The outcomes of this project promise significant advancements in disaster preparedness, ultimately safeguarding human lives and infrastructure. © 2025 IEEE.

关键词： Risk management

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Comparative analysis of twelve transfer learning models for the prediction and crack detection in concrete dams,based on borehole images

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Frontiers of Structural and Civil engineering 2024年第10期18卷 1507-1523页

作者： Umer Sadiq KHAN Muhammad ISHFAQUE Saif Ur Rehman KHAN Fang Xu Lerui CHEN Yi LEI School of Computer and Information Science Hubei Engineering UniversityXiaogan 432000China Institute for AI Industrial Technology Research Hubei Engineering UniversityXiaogan 432000China College of Water Conservancy and Hydropower Engineering Hohai UniversityNanjing 210098China School of Computer Science and Engineering Central South UniversityChangsha 410083China College of Aviation Zhongyuan University of TechnologyZhengzhou 451191China School of Civil Engineering Central South UniversityChangsha 410083China

Disaster-resilient dams require accurate crack detection,but machine learning methods cannot capture dam structural reaction temporal patterns and *** research uses deep learning,convolutional neural networks,and transfer learning to improve dam crack *** deep-learning models are trained on 192 crack *** research aims to provide up-to-date detecting techniques to solve dam crack *** finding shows that the EfficientNetB0 model performed better than others in classifying borehole concrete crack surface tiles and normal(undamaged)surface tiles with 91%*** study’s pre-trained designs help to identify and to determine the specific locations of cracks.

关键词： concrete dam borehole closed-circuit television deep learning models crack detection water resources management management

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