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2023 International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2023

作者： Palaniappan, Ramya Siva, R. Surendran, Suseendran Srm Institute of Science and Technology Department of Computational Intelligence Tamil Nadu Chennai India Bannari Amman Institute of Technology Department of Computer Science Engineering Tamil Nadu Sathyamangalam India

ISBN: (纸本)9781665474511

Hypoglycemia / Hyperglycemia is a long-term metabolic disorder in which the Blood Glucose (BG) level of the patient is low or high than the standard range. High blood glucose level if untreated can cause heart disease, stroke, blindness, kidney failure and amputation of toes, feet or legs. The Internet of Medical Things(IoMT) is the amalgamation of the information from medical devices and medical software applications that connects healthcare IT systems seamlessly. Remote Health Monitoring System (RHMS) is the process of making use of the technology for providing medical services to the patients in non-clinical environment. In situations where the patients cannot reach the clinical environment, RHMS when incorporated will aid the healthcare professionals to deliver personalized treatment for the patients. In this study, IoMT enabled Continuous Glucose Monitoring (CGM) system enables continuous automated monitoring of blood glucose(BG) levels of the patients at regular intervals. This reduces the burden of measuring the BG levels and recording the results manually for further follow up. This continuous monitoring environment can send alerts to the patients and or to the clinicians when the BG levels are out of the normal range. This CGM system will be beneficial for the clinicians to alert Individuals about the upcoming health complications and recommend quality lifestyle for them to stay healthy. Yellow Springs Instrument's (YSI) BG readings are used to compare the proposed CGM system's performance in terms of Mean Absolute Relative Difference (MARD). Additionally, the challenges and threats associated with constructing the IoMT ecosystem were highlighted, as well as potential solutions. © 2023 IEEE.

关键词： Blood

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Optimizing Network Management and Virtualization Using Machine Learning Approach: Network Slice Prediction

Optimizing Network Management and Virtualization Using Machi...

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2024 International Conference on science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Akinyemi, Lateef Adesola Oshinuga, Olamide Peter Oladejo, Sunday Oladayo Ekwe, Stephen Obono Sumbwanyanbe, Mbuyu Mnkandla, Ernest Shoewu, Oluwagbemiga Omotayo University of South Africa Departments of Computer Science and Elect. Engineering CSET Johannesburg South Africa Lagos State University Department of Electronic and Computer Engineering Lagos Nigeria Stellenbosch University School for Data Science and Computational Thinking Stellenbosch South Africa Cape Peninsula University of Technology Department of Elect. Electronic and Computer Engineering Cape Town South Africa University of South Africa Department of Electrical Engineering CSET Johannesburg South Africa University of South Africa Department for Computer Science CSET Johannesburg South Africa

ISBN: (纸本)9798350358155

This study demonstrates the utilization of Machine Learning (ML) for network slice prediction, enabling the optimization of resources for diverse network slices. Traditional methods for network slice prediction often lack efficiency and result in inaccuracies. By leveraging ML algorithms such as Naive Bayes and Random Forest, an intelligent framework that automates network slice prediction is developed. This framework enhances network virtualization and management, facilitating resource allocation. The ML algorithms take real-time network conditions and usages, such as packet delay and smart city, as input and output for selecting the most suitable network slice. Data analysis is further conducted to reveal the connections between the input parameters and how these parameters influence the selection of the accurate network slice. Network slicing plays a crucial role as it enables the customization of services and facilitates efficient scaling to meet the specific needs of different applications and industries. The accuracy scores of the employed ML algorithms were generally perfect, except for the KNN and SVM classifiers, which achieved an accuracy of 94.30% and 92.16%, respectively, for the prediction of network slices based on incoming network connections and usages. © 2024 IEEE.

关键词： Data Analysis Machine Learning Network Management Network Slicing Network Virtualization

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Automated Early Diagnosis of Lung Tumor Based on Deep Learning Algorithms 1

Automated Early Diagnosis of Lung Tumor Based on Deep Learni...

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1st IEEE International Conference on Applied Intelligence and Sustainable Computing, ICAISC 2023

作者： Maheswari, M. Jothi, B. Devi, P. Gayathri Chitradevi, B. Pradeep, Kumar S SRM Institute of Science and Technology Department of Computational Intelligence Chennai India Panimalar Engineering College Department of ECE Chennai India Department of Computer Science Engineering Perambalur India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350323795

The main goals of this study are to identify the early stages of lung cancer and investigate the degree of accuracy of various machine learning algorithms. A thorough review of the literature revealed that certain classifiers have low accuracy while others have higher accuracy but are challenging to approach nearer to 100%. Low accuracy and high implementation costs as a result of inappropriate DICOM image handling Many various types of images are utilised in medical image processing, however CT scans are typically chosen since they have less noise. Deep learning has been shown to be the most effective technique for processing medical images, identifying and classifying lung nodules, extracting features, and predicting the stage of lung cancer. This system's initial stage employed image processing methods to separate lung sections. Using K Means, the segmentation is carried out. Segmented images are used to extract the features, which are then classified using a variety of machine learning algorithms. The accuracy, sensitivity, and specificity of the proposed techniques, as well as their classification time, are used to assess their performance. © 2023 IEEE.

关键词： computerized tomography

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Investigation on Preserving Privacy of Electronic Medical Record using Split Learning 5

Investigation on Preserving Privacy of Electronic Medical Re...

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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Kiruthika, M. Kumar, Abhishek Krishnasamy, Lalitha Sarveshwaran, Velliangiri Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Tamilnadu Kattankulathur603203 India Punjab National Bank New Delhi110075 India Department of Computer Science and Engineering School of Engineering and Technology Christ University Karnataka Bengaluru560074 India

Artificial Intelligence is deployed in multiple areas, including healthcare. Utmost research is done in AI enabled healthcare industry because of the demands like accurate result, data security, exact prediction, huge volume of data, etc. In conventional deep learning models, the training happens with the dataset that are stored in a single device. This requires a huge storage space and highly efficient machines to train the data. Usage of big data, demands for innovative models that can be deployed and used in confined storage. Split learning is one such collaborative distributed deep learning model that allows the data to be stored in a split fashion. Split learning supports desirable features like less storage, more privacy to raw data, ability to work with resource constraints, etc., making it suitable for storing electronic medical record of patients. This paper discusses the advantages of using split learning for healthcare, the possible configurations of split learning that supports data privacy in healthcare and finally discusses the open research challenges in implementing split learning for healthcare. © 2024 The Authors. Published by Elsevier B.V.

关键词： Digital storage

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Machine Learning: An Analytical Approach for Pattern Detection in Diabetes 1

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8th International Conference on Soft Computing: Theories and Applications, SoCTA 2023

作者： Chauhan, Ritu Goel, Anika Kaur, Harleen Alankar, Bhavya Center for Computational Biology and Bioinformatics Amity University Uttar Pradesh Noida India Department of Computer Science and Engineering Jamia Hamdard Delhi India

ISBN: (数字)9789811998584

ISBN: (纸本)9789811998577

In current times, the amount of data is increasing exponentially but generated data is not transformed into knowledge data which eventually serves no purpose. This issue can be resolved by applying machine learning approaches which prove to play a promising role in the field of prediction. Similarly, in healthcare industries, there is a supply of large amount of data for which it is necessary to extract information for prognosis, diagnosis, and medication development. In this paper, we have focused on diagnosis of diabetes which is a chronic disease with fastest global growth rate. The current study is carried out in two phases where in first phase data is pre-processed to remove the unwanted and missing values and in second phase the data is analyzed by model building using machine learning algorithm. Additionally, we have attempted machine learning techniques in our study such as decision tree classifier and logistic regression which can be used for diagnosis of diabetes, and we attain the accuracy of 75.9% for decision tree classifier and 74.6% for logistic regression. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Diagnosis

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GPU-Based Efficient Parallel Heuristic Algorithm for High-Utility Itemset Mining in Large Transaction Datasets (Extended Abstract) 40

GPU-Based Efficient Parallel Heuristic Algorithm for High-Ut...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Fang, Wei Jiang, Haipeng Lu, Hengyang Sun, Jun Wu, Xiaojun Lin, Jerry Chun-Wei Jiangnan University Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Department of Computer Science and Technology Wuxi214122 China Silesian University of Technology Department of Distributed Systems and IT Devices Poland

ISBN: (纸本)9798350317152

Heuristic algorithms have been developed to find approximate solutions for high-utility itemset mining (HUIM) problems that compensate for the performance bottlenecks of exact algorithms. However, heuristic algorithms still face the problem of long runtime and insufficient mining quality, especially for large transaction datasets with thousands to tens of thousands of items and up to millions of transactions. To solve these problems, a novel GPU-based efficient parallel heuristic algorithm for HUIM (PHA-HUIM) is proposed in this paper. The iterative process of PHA-HUIM consists of three main steps: the search strategy, fitness evaluation, and ring topology communication. The search strategy and ring topology communication are designed to run in constant time on GPU. The parallelism of fitness evolution helps to substantially accelerate the algorithm. To improve the mining quality, a multi-start strategy with an unbalanced allocation strategy is employed in the search process. Ring topology communication is adopted to maintain population diversity. A load balancing strategy is introduced to reduce the thread divergence to improve the parallel efficiency. The experimental results on nine large datasets show that PHA-HUIM outperforms state-of-the-art HUIM algorithms in terms of speedup performance, runtime, and mining quality. © 2024 IEEE.

关键词： Graphics processing unit

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Late and Early Blight Diseases Identification of Potatoes with a Light Weight Hybrid Transfer Learning Model

Late and Early Blight Diseases Identification of Potatoes wi...

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2023 IEEE World AI IoT Congress, AIIoT 2023

作者： Siddique, Abu Zobayer Bin Das, Shoibal Tabassum, Poonam Tasir, All Moon Roy, Shovon Rahman, Md. Saifur Mridha, M.F. Islam, Ashraful Bangladesh University of Business and Technology Department of Computer Science and Engineering Dhaka Bangladesh American International University-Bangladesh Department of Computer Science Dhaka Bangladesh Independent University Bangladesh Center for Computational and Data Sciences Dhaka Bangladesh

ISBN: (纸本)9798350337617

Potatoes are one of the world's most important commodities, and leaf maladies such as early and late blight can substantially reduce their yield and quality. Hence, both farmers and researchers must prioritize quick and precise illness diagnosis. In our research, we propose a strategy based on transfer learning for classifying toxic and diseased potato leaf tissue. We specifically used our dataset of potato leaf photos to fine-tune the Mobile-Net model, which was a pre-trained convolutional neural network. To enhance the model's functionality, we also added a few more layers. Our study found that, in comparison to other state-of-the-art methods, our methodology outperformed them all by achieving a multi-class classification accuracy of 99%. Our method can be used to detect and monitor potato leaf maladies in real-world situations, which could eventually contribute to enhancing potato productivity and food security. © 2023 IEEE.

关键词： Deep learning

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Mutually-paced Knowledge Distillation for Cross-lingual Temporal Knowledge Graph Reasoning 23

Mutually-paced Knowledge Distillation for Cross-lingual Temp...

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32nd ACM World Wide Web Conference, WWW 2023

作者： Wang, Ruijie Li, Zheng Yang, Jingfeng Cao, Tianyu Zhang, Chao Yin, Bing Abdelzaher, Tarek Department of Computer Science University of Illinois Champaign Urbana United States *** Inc United States School of Computational Science and Engineering Georgia Institute of Technology United States

ISBN: (纸本)9781450394161

This paper investigates cross-lingual temporal knowledge graph reasoning problem, which aims to facilitate reasoning on Temporal Knowledge Graphs (TKGs) in low-resource languages by transfering knowledge from TKGs in high-resource ones. The cross-lingual distillation ability across TKGs becomes increasingly crucial, in light of the unsatisfying performance of existing reasoning methods on those severely incomplete TKGs, especially in low-resource languages. However, it poses tremendous challenges in two aspects. First, the cross-lingual alignments, which serve as bridges for knowledge transfer, are usually too scarce to transfer sufficient knowledge between two TKGs. Second, temporal knowledge discrepancy of the aligned entities, especially when alignments are unreliable, can mislead the knowledge distillation process. We correspondingly propose a mutually-paced knowledge distillation model MP-KD, where a teacher network trained on a source TKG can guide the training of a student network on target TKGs with an alignment module. Concretely, to deal with the scarcity issue, MP-KD generates pseudo alignments between TKGs based on the temporal information extracted by our representation module. To maximize the efficacy of knowledge transfer and control the noise caused by the temporal knowledge discrepancy, we enhance MP-KD with a temporal cross-lingual attention mechanism to dynamically estimate the alignment strength. The two procedures are mutually paced along with model training. Extensive experiments on twelve cross-lingual TKG transfer tasks in the EventKG benchmark demonstrate the effectiveness of the proposed MP-KD method. © 2023 ACM.

关键词： Distillation

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Efficient Net-based Expert System for Personalized Facial Skincare Recommendations 7

Efficient Net-based Expert System for Personalized Facial Sk...

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Akshya, J. Mehra, Vinit Sundarrajan, M. Sri, Pattapu Teja Choudhry, Mani Deepak SRM Institute of Science and Technology Department of Computational Intelligence Chennai India SRM Institute of Science and Technology Department of Computer Science and Engineering Chennai India KGiSL Institute of Technology Department of Information Technology Coimbatore India

ISBN: (纸本)9798350397253

In recent years, recommender systems have gained a lot of traction in both the commercial world and the academic research community. The term 'recommendation system for facial skin care' refers to an automated system that gives consumers advice on which facial skin care products to use based on their specific skin type, skin issues, and preferences. Product recommendation systems analyze user data using a variety of methods, including collaborative filtering, content-based filtering, and machine learning algorithms. Better skincare results may be achieved through the usage of a suggestion system in which consumers receive advice related to their own skin issues. The problem with current models is that they don't adequately address users' issues and concerns while being based on collaborative or content-based filtering. In order to create an effective face skincare model, the authors of this study suggest a hybrid approach that integrates several existing methods (including KNN, CNN, Transfer Learning of Efficient Net BO, and a content-based filtering recommendation engine). The user's skin tone, skin type, and acne severity are all inputs into the algorithm, which then outputs a recommendation for the most suitable product. The results of a comparison between the suggested model and other models prove its superiority. Using a hybrid model that takes into account a number of different methodologies and user inputs, the article aspires to deliver a more all-encompassing and individualized solution for facial skincare recommendations. The proposed model has the potential to enhance the precision and efficiency of current skincare recommendation systems. The implementation of EfficientNet B0 improves the model's accuracy, which now has a validation accuracy of 80% and a training accuracy of 87.10%. © 2023 IEEE.

关键词： Recommender systems

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Neural Cryptography with Fog Computing Network for Health Monitoring Using IoMT

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computer Systems science & engineering 2023年第1期44卷 945-959页

作者： G.Ravikumar K.Venkatachalam Mohammed A.AlZain Mehedi Masud Mohamed Abouhawwash Department of Computer Science and Engineering Coimbatore Institute of Engineering and TechnologyCoimbatore641109India Department of Applied Cybernetics Faculty of ScienceUniversity of Hradec Králové50003Hradec KrálovéCzech Republic Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Sleep apnea syndrome(SAS)is a breathing disorder while a person is *** traditional method for examining SAS is Polysomnography(PSG).The standard procedure of PSG requires complete overnight observation in a *** typically provides accurate results,but it is expensive and time ***,for people with Sleep apnea(SA),available beds and laboratories are ***,it may produce inaccurate ***,this paper proposes the Internet of Medical Things(IoMT)framework with a machine learning concept of fully connected neural network(FCNN)with k-near-est neighbor(k-NN)*** paper describes smart monitoring of a patient’s sleeping habit and diagnosis of SA using FCNN-KNN+average square error(ASE).For diagnosing SA,the Oxygen saturation(SpO2)sensor device is popularly used for monitoring the heart rate and blood oxygen *** diagnosis information is securely stored in the IoMT fog computing *** can care-fully monitor the SA patient remotely on the basis of sensor values,which are efficiently stored in the fog computing *** proposed technique takes less than 0.2 s with an accuracy of 95%,which is higher than existing models.

关键词： Sleep apnea polysomnography IOMT fog node security neural network KNN signature encryption sensor

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