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Enabling Clinical Decision-Making in Smart Healthcare through AI/ML Algorithms

Enabling Clinical Decision-Making in Smart Healthcare throug...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Vikas Kumar Kharbas Y R Meena Praveen Kumar Thakur Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Civil Engineering School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India Department of Law ARKA JAIN University Jamshedpur Jharkhand India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Using AI/ML algorithms in clever healthcare has emerged as a promising improvement to facilitate green and effective medical choice-making. This novel era uses gadgets mastering and predictive analytics to extract deep insights from numerous assets of healthcare statistics, together with clinical statistics, development, imaging, and laboratory results. through leveraging huge datasets and effective algorithms, AI/ML-primarily based structures are capable of extra as it should be and suddenly become aware of correlations between elements that contribute to the development of diverse sicknesses. it can permit the implementation of more evidence-based interventions and customized treatments tailored to an individual-affected character’s health profile. moreover, AI/ML algorithms can also robotize the scientific workflow, contemplating the automated detection and prognosis of sure illnesses and medical conditions. As a result, AI/ML structures can seriously lessen healthcare prices and enhance patient consequences. in the end, using AI/ML algorithms in smart healthcare can empower clinical vendors to make more informed decisions approximately the analysis and treatment of their patients.

关键词： Accuracy Medical services Prediction algorithms Approximation algorithms Natural language processing Prognostics and health management Robots Recommender systems Cancer Biomedical imaging

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CacheTrimmer: Adaptive Cache File Trimming for Optimized Performance and Lifetime on Mobile Devices

CacheTrimmer: Adaptive Cache File Trimming for Optimized Per...

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IEEE International Conference on computer Design: VLSI in computers and Processors, (ICCD)

作者： Yi Zhang Yunpeng Song Wentong Li Yiyang Huang Dingcui Yu Mengyang Ma Liang Shi MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application East China Normal University Shanghai China School of Computer Science and Technology East China Normal University Shanghai China

ISBN: (数字)9798350380408

ISBN: (纸本)9798350380415

Mobile devices always cache numerous files during application runtime, which can be trimmed to improve the user experience. However, existing cache file trimming methods are unaware of the cleaning cost within the file system and storage devices, which degrades the system performance and storage lifetime, resulting in low benefits of trimming cache files. Motivated by this, an adaptive cache file trimming (CacheTrimmer) scheme is proposed to trim cache files for performance and lifetime improvement. The basic idea is to determine the trimming timing based on the cleaning cost of the file system and storage device, maximizing the benefit of trimming cache files. Specifically, CacheTrimmer includes two components: First, a cleaning cost-aware trimming method is proposed to trim cache files by recording the index information of cache files in a list and determining the timing and size of file trimming. Second, to avoid trimming-induced intra-segment fragmentation and improve trimming efficiency, a log-structured cache scheme is further proposed to maintain the cache files in separate segments. We prototype CacheTrimmer with a real mobile platform. Experimental results under real workloads show that CacheTrimmer achieves encourage performance and lifetime improvement compared to the state-of-the-art.

关键词： Performance evaluation Costs Runtime File systems System performance Cleaning Mobile handsets User experience Timing Recording

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Assessing the Robustness of Machine Learning Algorithms against Emerging Privacy and Security Threats in Medical applications

Assessing the Robustness of Machine Learning Algorithms agai...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Srikrishna Baskar Rao Sarbojeet Goswami Manish Kumar Goyal Department of Aerospace Engineering School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India Department of optometry ARKA JAIN University Jamshedpur Jharkhand India Department of Computer Science & Application Vivekananda Global University Jaipur India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Using gadgets to realize algorithms in medical packages can provide new insights and talents, including new approaches to diagnose illnesses or reveal treatments. Rising privacy and security threats pose potential dangers to those structures that can restrict their efficacy and average performance. To make sure tool mastering algorithms are resilient to those threats, it is critical to assess their robustness. This summary offers an assessment of techniques to evaluate the robustness of tool-getting-to-know algorithms against emerging privacy and protection threats in medical programs. First, we check the kinds of threats and discuss how they’ll affect the general performance of machine studying algorithms. Secondly, we compare a selection of strategies for measuring the robustness of gadgets and gaining knowledge of algorithms, which include static, dynamic, and adverse check strategies. Ultimately, we speak about techniques for mitigating threats in scientific packages, which provides for incorporating relaxed layout thoughts in tool studying structures and utilizing smoke assessments and strain tests. Through this assessment, we aim to provide more facts on the techniques for assessing the robustness of gadget-studying algorithms against rising privacy and security threats in scientific programs.

关键词： Privacy Machine learning algorithms Heuristic algorithms Medical services Robustness Security Protection Medical diagnostic imaging Monitoring Strain

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Correlation Between 3D Magnetic Resonance Imaging and Clinical Outcomes in Traumatic Brain Injury

Correlation Between 3D Magnetic Resonance Imaging and Clinic...

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International Conference on Computing and Networking Technology (ICCNT)

作者： H. Srikantha Ginu Anie Joseph Surendra Yadav Department of Civil Engineering School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India Department of Nursing ARKA JAIN University Jamshedpur Jharkhand India Department of Computer Science & Application Vivekananda Global University Jaipur India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This article identifies a correlation between the 3D Magnetic Resonance Imaging (MR) of patients with worrying brain harm (TBI) and the patient’s medical results. The research team compares 3-D MR imaging outcomes with the patient’s observe-up go-to facts, including neurologic assessments, Glasgow Coma Scale rating, and other parameters. The effects confirmed that patients who had a better degree of injury discovered from three-D MR scans had worse scientific effects in comparison to sufferers who had milder harm. This correlation became great for acute and subacute TBI patients. The authors endorse that 3-D MR scans can be used to predict the clinical results of TBI patients and can have the potential to inform treatment selections.

关键词： Correlation Three-dimensional displays Sensitivity Magnetic resonance imaging Brain injuries Biomedical imaging

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YOLO: Redefining Real-Time Object Detection Accuracy and Efficiency

YOLO: Redefining Real-Time Object Detection Accuracy and Eff...

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Applied Intelligence and Computing (AIC), IEEE World Conference on

作者： Shivam Gopikishan Verma Sudhanshu Maurya Himanshu Pant Ajay Kumar Yadav Sneha Thomas Varghese Rachit Garg Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India School of Computing Graphic Era Hill University Uttarakhand India Department of Computer Application United Institute of Management Prayagraj Uttar Pradesh India School of Computer Science and Engineering Lovely Professional University Phagwara India

ISBN: (数字)9798350384598

ISBN: (纸本)9798350384604

Image classification, also known as object detection, is an advanced form of computer vision in which a network can predict objects in an image and point them out as bounding boxes. Classifying and recognizing objects in an image is just one aspect of object detection because it also involves localizing these objects and attracting bounding boxes around them. Since object detection involves image and video analysis, it has been of critical interest to researchers over the past few years. The previous object recognition method relies on manually created features and traditional and trainable algorithms. By using the YOLO (You Only Look Once) process, this research aims to perform real-time object detection. Regarding speed and performance, the YOLO algorithm is much more efficient than the existing model. Unlike existing algorithms that do not scan the entire image in single forward propagation, YOLO uses a neural network to predict binding boxes for the whole image. In comparison with existing algorithms, YOLO performs faster.

关键词： YOLO Training Accuracy Neural networks Streaming media Prediction algorithms Real-time systems Object recognition Standards Testing

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Improving Connectivity of IoT-Enabled Networks Through Big Data Analytics

Improving Connectivity of IoT-Enabled Networks Through Big D...

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International Conference on Computing and Networking Technology (ICCNT)

作者： K S Kiran Sumanta Sen Sunil Sharma School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India Department of Pharmacy ARKA JAIN University Jamshedpur Jharkhand India Department of Computer Science & Application Vivekananda Global University Jaipur India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The net of things (IoT) has quick grow to be the backbone of many corporations, taking into account real-time statistics collection and automatic tasks. However, the connectivity of these IoT-enabled networks can become without difficulty congested when huge amounts of data are generated in a brief time frame. To be able to hold a high stage of overall performance, big facts analytics can be used to research the massive volumes of generated statistics, provide insights and locate anomalies to improve community connectivity. Massive statistics analytics will permit higher records processing and evaluation competencies, become aware of regions of improvement and optimize network operations. Additionally, predictive analytics techniques can be used to forecast upcoming community congestion and take movement that could enhance network performance in real time. This, but, requires green facts collection, garage and control gear to ensure that the big records analytics answer is effective and efficient. This paper investigates how massive records analytics may be used to improve the connectivity of IoT-enabled networks through leveraging the energy of predictive analytics.

关键词： Performance evaluation Gears Scalability Big Data Real-time systems User experience Safety Reliability Internet of Things Predictive analytics

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Exploring Spectrum Sharing Algorithms for 6G Cellular Communication Networks

Exploring Spectrum Sharing Algorithms for 6G Cellular Commun...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Ashutosh Pattanaik Shweta Zahid Ahmed Department of Mechanical Engineering School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India Department of Pharmacy ARKA JAIN University Jamshedpur Jharkhand India Department of Computer Science & Application Vivekananda Global University Jaipur India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

6G cell verbal exchange networks might be the successor to 5G networks, and their success relies heavily upon optimization measures that are enabled through the exploration of spectrum-sharing algorithms (SSAs). SSAs are hard and fast techniques implemented to maximize machine assets, performance metrics, and operational performance. That is performed by way of lowering interference, permitting spectrum contextualization, and improving the sharing of underutilized and congested spectrum. For 6G verbal exchange networks, a heightened attention on advanced database storage, distributed computing, and interference avoidance techniques must be taken a good way to ensure the maximum green use of be had spectrum. This paper explores a spread of SSAs for 6G mobile communication networks, consisting of dynamic spectrum get admission to, non-orthogonal more than one get right of entry to, and direct tool-enabled opportunistic spectrum usage. We evaluate current research that has been implemented to the improvement of SSAs and offer insight into future demanding situations and programs inside the subject. Furthermore, we talk about the significance of massive records and systems getting to know in the creation of SSA algorithms, providing capability enhancements to the ever-changing 6G communique networks. Standard, the usage of SSAs in 6G verbal exchange networks can make certain the most effective and green usage of be had spectrum, at the same time as additionally imparting extraordinary stages of aid flexibility

关键词： 6G mobile communication Wireless communication Measurement Distributed databases Interference Oral communication Security Wireless fidelity Standards Optimization

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Efficient Deep learning-based Real-time Migration Strategies for Containers in SDN

Efficient Deep learning-based Real-time Migration Strategies...

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Next Generation Data-driven Networks (NGDN), International Conference on

作者： Shan Huang Yanan Liu Yanuo Li Feili He Xiaodong Duan School of Computer Science and Engineering Dalian Minzu University Key Laboratory of Big Data Application Technology of the State Ethnic Affairs Commission Dalian Ethnic Culture Digital Technology Key Laboratory Dalian China

ISBN: (数字)9798350388374

ISBN: (纸本)9798350388381

Container technology is widely used and improve the efficiency of container real-time migration has become an important research topic. Existing studies mainly focus on optimizing iterative dumping of containers without fully considering the impact of migration node selection on migration efficiency. Software-defined networking (SDN), achieves centralized control of the network by separating the control plane and the forwarding plane. Applying deep learning to SDN makes tasks such as traffic prediction and network optimization more intelligent. This paper proposes Prediction Model based on CLCB (CNN-LSTM-CBAM) uses real-time collected load data as input, extracts feature components of load data using one-dimensional convolutional neural networks, and then predicts using LSTM. To improve prediction accuracy, a lightweight CBAM attention module is introduced, and the prediction results are output through fully connected layers. The Network Traffic Prediction Model based on GCGRU-Conv (GCN-GRU-CNN) considers both spatial and temporal features of network traffic. GCN and GRU are used to extract spatial and temporal features of network traffic, respectively, and then these features are fused together using convolutional neural networks. To address the gradient problem caused by increasing network depth, a residual network structure is introduced. Experimental results show that the method proposed can effectively reduce the container migration time and has better prediction results.

关键词： Deep learning Costs Telecommunication traffic Containers Predictive models Feature extraction Real-time systems Convolutional neural networks Software defined networking Load modeling

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Evaluation of Contingency-Based Learning Strategies for Precision Medicine

Evaluation of Contingency-Based Learning Strategies for Prec...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Harshita Kaushik Naveen Kumar Rajendran Ashwini Kumar Department of Computer Science& Application Vivekananda Global University Jaipur India Department of Aerospace Engineering School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India Department of Mechnical Engineering ARKA JAIN University Jamshedpur Jharkhand India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Contingency-based learning strategies primarily based on synthetic intelligence (AI) are being hired within the area of precision remedy to enable greater powerful selection-making when treating patients. The overall performance of such learning strategies should be evaluated to make sure they are presenting accurate and dependable outcomes. This paper aims to assess contingency-based learning strategies for precision medication to help clinicians and researchers appropriately assess the efficacy of these learning techniques and decide the first-rate method for given software., a scientific evaluation of the current literature was undertaken to identify existing assessment strategies and analyze their strengths and weaknesses. The assessment criteria used in this analysis are accuracy, interpretability, complexity, scalability, and transferability, which had been selected as they affect the performance of AI-primarily based approaches in real-international settings. Furthermore, a discussion of current equipment and frameworks that could be used to assess AI-based techniques in the area of precision remedy was performed. From this assessment, it has been discovered that most of the present assessment techniques lack the necessary accuracy and transferability essential to appropriately check the efficacy of AI-based strategies for precision medicinal drugs. But, sure, evaluation metrics, together with explain ability, may be employed to degree the overall performance of AI-primarily based techniques in order to decide the nice approach for a given application.

关键词： Drugs Measurement Privacy Accuracy Scalability Precision medicine Contingency management Software Safety Medical diagnostic imaging

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Role of Contemporary Techniques in Agriculture Development: A Systematic Review 2

Role of Contemporary Techniques in Agriculture Development: ...

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2nd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2022

作者： Popli, Renu Singh, Daljeet Kumar, Rajeev Saini, Gurpreet Singh Garg, Kanwal Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Center for Space Research DRD Lovely Professional University Phagwara India School of Electronics and Electrical Engineering Lovely Professional University Phagwara India Kurukshetra University Department of Computer Science and Application Kurukshetra India

ISBN: (纸本)9781665437899

Gone are the days when agriculture was said to be a profession of uneducated people who used only basic mechanical tools to survive. Use of technology in agriculture has transformed it into a high-tech, smart and efficient business. The abrupt increase in population has pushed the efficiency of agriculture activities to achieve higher and better yield from given set of resources. In this paper, a comprehensive survey on the role of contemporary techniques used in agricultural development is presented. Various aspects of smart agriculture involving the use of Wireless Sensor Networks (WSNs) to gather information, big data analysis for processing the collected information, Internet of Things (IOT) framework for communication, image processing techniques and mobile phone application developed for agriculture are discussed. Landmark studies published in each of these related fields are also presented. © 2022 IEEE.

关键词： Wireless sensor networks

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