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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Goyal, Manish Kumar Haripriya Singh, Akhilendra Pratap Vivekananda Global University Department of Computer Science & Application 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)9798350343274

Dynamic Predictive Modeling is a method that has brought about a revolutionary change in the field of preventive maintenance in the industrial sector. Using the benefits of real-time data streams and flexible algorithms, this cutting-edge technology enhances predictive maintenance processes. In this investigation, we evaluate the efficacy of Dynamic Predictive Modeling to that of numerous more conventional methods, including vibration analysis, acoustic emission analysis, thermography, oil analysis, ultrasonic testing, and data analytics. Accuracy, resource optimization, adaptability, downtime reduction, cost-effectiveness, and preventative maintenance are just a few of the criteria we use to evaluate the methods. The findings indicate that Dynamic Predictive Modeling outperforms its predecessors across a range of performance metrics. It can better adapt to the ever-changing industrial environment, allowing for more precise and timely breakdown forecasts. The allocation of resources and the economy of operation are both enhanced by the fact that maintenance procedures may be adjusted according to the actual condition of the equipment. Predictive maintenance is the most effective method for minimizing unscheduled downtime and increasing output in heavy industry. In a comparison of several approaches to predictive maintenance, Dynamic Predictive Modeling comes out on top with the highest aggregate score. A study reveals how an unconventional approach might improve the effectiveness and reliability of industrial operations. Increased equipment reliability, decreased maintenance costs, and a potential advantage in the manufacturing industry might arise from the use of dynamic predictive modeling. © 2023 IEEE.

关键词： Cost effectiveness

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A self-driving solution for resource-constrained autonomous vehicles in parked areas

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High-Confidence Computing 2024年第1期4卷 32-42页

作者： Jin Qian Liang Zhang Qiwei Huang Xinyi Liu Xiaoshuang Xing Xuehan Li College of Information Engineering Taizhou UniversityTaizhou 225300China Engineering Research Center of Integration and Application of Digital Learning Technology Ministry of EducationBeijing 100039China School of Computer Science and Engineering Changshu Institute of TechnologyChangshu 215500China The School of Electronic and Information Engineering Beijing Jiaotong UniversityBeijing 100044China

Autonomous vehicles in industrial parks can provide intelligent,efficient,and environmentally friendly transportation services,making them crucial tools for solving internal transportation *** the characteristics of industrial park scenarios and limited resources,designing and implementing autonomous driving solutions for autonomous vehicles in these areas has become a research *** paper proposes an efficient autonomous driving solution based on path planning,target recognition,and driving decision-making as its core *** designs for path planning,lane positioning,driving decision-making,and anti-collision algorithms are *** analysis and experimental validation of the proposed solution demonstrate its effectiveness in meeting the autonomous driving needs within resource-constrained environments in industrial *** solution provides important references for enhancing the performance of autonomous vehicles in these areas.

关键词： Autonomous vehicles Path planning Target recognition Driving decision-making Self-driving

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Designing Architecture for Container-As-A-Service (CaaS) in Cloud Computing Environment: A Review 3rd

Designing Architecture for Container-As-A-Service (CaaS) in ...

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3rd International Conference on Machine Learning, Advances in Computing, Renewable Energy and Communication, MARC 2021

作者： Malviya, Anshita Dwivedi, Rajendra Kumar Department of Information Technology and Computer Application MMMUT Gorakhpur Gorakhpur India

ISBN: (纸本)9789811928277

Nowadays, every company is trying to become a tech company as we have seen that software is consuming the entire world. So, how the company approaches software development and serves their customers show how efficient their employees are or how agile their organization is. Therefore, it is of utmost importance for the companies to make correct decision about the architectural choices early in the software development life cycle. It has been observed that containers are taking place of virtual machines in the IT industry because they do not require to incorporate guest operating system in each instance like virtual machines, and they simply support the property and resources of host OS. This paper presents a survey on containers, and its architecture and how containers are better than virtual machines for software development. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Containers

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Replication-Based Query Management for Resource Allocation Using Hadoop and MapReduce over Big Data

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Big Data Mining and Analytics 2023年第4期6卷 465-477页

作者： Ankit Kumar Neeraj Varshney Surbhi Bhatiya Kamred Udham Singh Department of Computer Engineering and Application GLA UniversityMathura 281406India. College of Computer Sciences and Information Technology King Faisal UniversityHofuf 31982Saudi Arabia School of Computing Graphic Era Hill UniversityDehradun 248002India.

We live in an age where everything around us is being *** generation rates are so scary,creating pressure to implement costly and straightforward data storage and recovery *** model functionality is used for creating a cluster parallel,distributed algorithm,and large *** MapReduce strategy from Hadoop helps develop a community of non-commercial use to offer a new algorithm for resolving such problems for commercial applications as expected from this working algorithm with insights as a result of disproportionate or discriminatory Hadoop cluster *** results are obtained in the work and the exam conducted under this job;many of them are scheduled to set schedules,match matrices’data positions,clustering before determining to click,and accurate mapping and internal reliability to be closed together to avoid running and execution *** output and proponents have been implemented,and the map has been used to reduce the *** execution input key/value pair and output key/value pair have been *** paper focuses on evaluating this technique for the efficient retrieval of large volumes of *** technique allows for capabilities to inform a massive database of information,from storage and indexing techniques to the distribution of queries,scalability,and performance in heterogeneous *** results show that the proposed work reduces the data processing time by 30%.

关键词： big data hadoop mapreduce resource allocation query management

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The Integration and Implementation of the Healthcare Internet of Things and Its Comprehensive Analysis of Benefits, Challenges, and Future Prospects

The Integration and Implementation of the Healthcare Interne...

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2024 International Conference on Advances in Computing Research on Science Engineering and technology, ACROSET 2024

作者： Goyal, Manish Kumar Kannagi, A. Desai, Karishma Vivekananda Global University Department of Computer Science & Application Jaipur India Department of Computer Science and Information Technology Bangalore India Atlas SkillTech University Department of UGDX Mumbai India

ISBN: (纸本)9798350388800

Healthcare Internet of Things (HIoT) integration and deployment promise safe, real-time, and networked healthcare solutions, and as such, offer a fundamental paradigm change in modern healthcare. This study made use of a systematic examination of the suggested HIoT approach and a comparison to more conventional methodologies, each of which has its own advantages and disadvantages. Evaluation criteria including accuracy, precision, recall, and F1-Score were used to put a numerical value on the quality of the solutions. The story that the research uncovered is rather fascinating. The suggested HIoT technique beat traditional methods across all assessment criteria. In comparison to more established approaches like 'Blockchain Encryption' (8.4) and 'Data Validation' (7.9), the suggested method achieved a far higher accuracy rating (9.2). The recommended strategy demonstrated its excellence across several categories, receiving ratings of 8.8 for interoperability and 8.9 for real-time monitoring. Data accuracy, which had a score of 9.1, was another advantage of the recommended strategy that showed how helpful it was in avoiding medical errors. The study's findings demonstrate how HIoT may completely transform the way healthcare is provided. Although they are still there, worries about cyber security and data privacy are manageable. Only by working together will lawmakers, regulators, IT companies, and healthcare providers be able to fully reap the benefits of HIoT. With the introduction of tailored treatment plans, data-driven decision making, and real-time monitoring, the future of health information technology seems bright. The study's findings indicate that the recommended strategy has the potential to greatly enhance patient-centeredness, efficacy, and efficiency in healthcare. Healthcare has a bright future with the potential to save lives and enhance the quality of treatment thanks to the Internet of Things. © 2024 IEEE.

关键词： Data accuracy

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Research on Environmental Perception Based on YOLOv8 and LiDAR Data Fusion 4

Research on Environmental Perception Based on YOLOv8 and LiD...

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4th IEEE International Conference on information technology, Big Data and Artificial Intelligence, ICIBA 2024

作者： Luo, ZhenHong Li, HuiLing Li, Ting Liu, Jie Guangxi Vocational Normal University School of Computer and Information Engineering Nanning China Key Laboratory of Application Technology of Intelligent Connected Vehicle Nanning China

ISBN: (纸本)9798350363586

To enhance the sensing capability of driverless cars in complex dynamic scenarios, a fusion method of radar and depth camera is emphasized and a technical framework for integrating multi-source sensors is proposed. A YOLOv8 neural network detection model combined with a self-attention network structure is first used to achieve correct recognition and depth estimation of vehicles and pedestrians, then further distance data of dynamic environment is acquired by M10P LiDAR, and finally information from D435i camera and radar information are fused by matrix projection. In the experiments, the conventional YOLOv8 model was compared to the YOLOv8 model enhanced with self-attention, the camera and LIDAR measurements were evaluated against the fusion results. The experimental results demonstrate that the new model significantly improves the confidence and stability of target detection, while the fused distance measurements are more accurate than those from individual sensors. © 2024 IEEE.

关键词： Sensor data fusion

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Machine Learning to Predict Cardiovascular Disease: Systematic Meta-Analysis 6

Machine Learning to Predict Cardiovascular Disease: Systemat...

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6th International Conference on information Systems and computer Networks, ISCON 2023

作者： Shuja, Mirza Qtaishat, Ahmed Mishra, Hari Mohan Kumar, Mukesh Ahmed, Bilal Lovely Professional University School of Computer Application Phagwara India Sohar University Department of Information Technology Sohar Oman

ISBN: (纸本)9798350346961

People do not have much time to think about their health in today's fast-paced society because of how busy their lives are. The overscheduling of people's lives and a widespread disregard for their health are two factors that contribute to the rise in the incidence of disease. Additionally, the great majority of people have disability that prevents them from operating properly, such as cardiovascular illness. These people are unable to operate normally because of their disabilities. According to the statistics provided by the World Health organization (WHO), cardiovascular disease is responsible for more than one-third of all deaths. Because of this, it is very necessary for anybody working in the medical industry to be able to evaluate the chance that a patient may develop cardiovascular disease. However, because hospitals and the healthcare industry produce such a vast amount of data, it may be difficult to do research owing to the sheer amount of information that is available. The use of methods that are based on Machine Learning (ML) by medical professionals has the potential to cut down on the amount of time and effort spent formulating predictions and organising data. Because of this, we have been talking about the factors that lead to heart disease as well as the methods that are involved in machine learning. We evaluated and compared the efficacy of a wide variety of well-known ML algorithms that were utilised in the experiment, and we made inferences about cardiovascular disease using these techniques. In the end, the purpose of this research is to determine whether a ML system is capable of reliably predicting cardiac illness.. © 2023 IEEE.

关键词： Forecasting

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A Survey of Privacy Preserving Methods based on Differential Privacy for Medical Data 7

A Survey of Privacy Preserving Methods based on Differential...

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7th World Conference on Computing and Communication Technologies, WCCCT 2024

作者： Yan, Haicao Yin, Menghan Yan, Chaokun Liang, Wenjuan Henan University School of Computer and Information Engineering Kaifeng China Henan University Henan Engineering Research Center of Intelligent Technology and Application Kaifeng China

ISBN: (纸本)9798350372328

Using deep learning and federated learning in the medical domain has facilitated convenience in diagnosing and treating patients. However, the current privacy leakage problem has made patients more cautious about personal privacy security. Privacy security has become an urgent issue. Differential privacy is a cryptography technique that makes it impossible for attackers to identify personal data by adding noise to the training process. In this work, we first provide a complete overview of differential privacy technologies. Then, we will review existing differential privacy works in the medical field. Finally, we analyze the existing problems of differential privacy and propose possible avenues for further research. © 2024 IEEE.

关键词： Deep learning

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Multi-agent QoS-aware autonomic resource provisioning framework for elastic BPM in containerized multi-cloud environment

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Journal of Ambient Intelligence and Humanized Computing 2023年第9期14卷 12895-12920页

作者： Saif, Mufeed Ahmed Naji Niranjan, S.K. Murshed, Belal Abdullah Hezam Al-ariki, Hasib Daowd Esmail Abdulwahab, Hudhaifa Mohammed Department of Computer Applications Sri Jayachamarajendra College of Engineering VTU Mysore India Department of Studies in Computer Science University of Mysore Karnataka Mysore India Department of Computer Science College of Eng. &IT University of Amran Amran Yemen Department of Information Technology Faculty of Engineering and Information Technology Al-ata’a University for Science and Technology Taiz Yemen Department of Computer Networks Engineering and Technologies Sana’a Community College Sana’a Yemen Department of Computer Application Ramaiah Institute of Technology VTU Bangalore India

Cloud computing enables businesses to improve their market competitiveness, enabling instant and easy access to a pool of virtualized and distributed resources such as virtual machines (VM) and containers for executing their business operations efficiently. Though the cloud enables the deployment and management of business processes (BPs), it is challenging to deal with the enormous fluctuating resource demands and ensure smooth execution of business operations in containerized multi-cloud. Therefore, there is a need to ensure elastic provisioning of resources to tackle the over and under-provisioning problems and satisfy the objectives of cloud providers and end-users considering the quality of service (QoS) and service level agreement (SLA) constraints. In this article, an efficient multi-agent autonomic resource provisioning framework is proposed to ensure the effective execution of BPs in a containerized multi-cloud environment with guaranteed QoS. To improve the performance and ensure elastic resource provisioning, autonomic computing is utilized to monitor the resource usage and predict the future resource demands, then resources are scaled based on demand. Initially, the required resources for executing the incoming workloads are identified by clustering the workloads into CPU and I/O intensive, and the local agent achieves this with the help of an initialization algorithm and K-means clustering. Then, the analysis phase predicts the workload demand using the proposed enhanced deep stacked auto-encoder (EDSAE), further, the containers are scaled based on the prediction outcomes, finally, the multi-objective termite colony optimization (MOTCO) algorithm is used by the global agent to find suitable containers for executing the clustered workloads. The proposed framework has been implemented in the Container Cloudsim platform and evaluated using the business workload traces. The overall simulation results proved the effectiveness of the proposed approach compare

关键词： Quality of service

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Future Directions of Artificial Intelligence and Machine Learning in Healthcare: A Systematic Analysis and Mapping Study 6

Future Directions of Artificial Intelligence and Machine Lea...

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6th International Conference on information Systems and computer Networks, ISCON 2023

作者： Mishra, Hari Mohan Ahmed, Bilal Shuja, Mirza Qtaishat, Ahmed Kumar, Mukesh Lovely Professional University School of Computer Application Phagwara India Sohar University Department of Information Technology Sohar Oman

ISBN: (纸本)9798350346961

In order to organise previously conducted research on using machine learning in medical contexts, the authors of this paper employ a method known as systematic mapping. In order to do this, we looked through the abstracts of a variety of publications, including medical journals, healthcare periodicals, and conference proceedings, looking for the phrase 'use of artificial intelligence and machine learning in healthcare.' After doing a search on Google Scholar, which resulted in the retrieval of 500 papers, we classified these studies in accordance with their objectives, approaches, primary concerns, and illnesses. With the use of this technique, we were able to organise our findings into the five categories that are as follows: privacy and security;a framework for privacy and security;interpretable machine learning;medical image assessment;electronic health record processing;and transfer learning. In addition, we found that the evaluation of medical images is the topic that receives the most research, that interpretable machine learning and explainable artificial intelligence are becoming increasingly popular, and that most authors are primarily interested in cancer research. To restate our mission, we want to provide future generations of scholars with an accurate picture of where the field is now and where it is headed.. © 2023 IEEE.

关键词： Machine learning

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