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5th International Conference on Data Science, Machine Learning and applications, ICDSMLA 2023

作者： Agarwal, Ankita Seth, Jyoti Sharma, Ambrish Kumar Karthikeyan, M.P. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science and Engineering Vivekananda Global University Jaipur India Master of Computer Application Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India

ISBN: (纸本)9789819780426

Area version goals bridge the space between supply and target domain names by remodeling statistics from the source domain to the target area without providing categorized goal facts. Medical image domain version is a critical trouble in scientific imaging obligations, consisting of most cancers prognosis, because it lets in for the switch of understanding throughout particular medical imaging modalities, obligations, and affected person cohorts. It can help address record distribution shifts and make undertaking medical photo classes more dependable and accurate. In this examination, we look into several area model techniques and benchmark their performance in the venture of the medical photo category. We examine supervised and unsupervised methods, as well as deep studying-based methods. The results of our experiments offer insights into the performance of numerous area model techniques for clinical picture class that could assist in deciding the pleasant method to use in any given medical imaging task. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Medical imaging

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Hybrid Heuristic for Solving the Euclidean Travelling Salesman Problem

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SN computer Science 2024年第8期5卷 1-19页

作者： Singh, Dharm Raj Singh, Manoj Kumar Chaurasia, Sachchida Nand Verma, Pradeepika Department of Computer Application Jagatpur P. G. College Uttar Pradesh Varanasi 221302 India Department of Computer Science Banaras Hindu University Uttar Pradesh Varanasi 221005 India Technology Innovation Hub Indian Institute of Technology Bihar Patna 801106 India

This study introduces a hybrid methodology that integrates the ant colony optimization (ACO) with genetic algorithm (GA) techniques. ACO is employed first to create an initial population and to derive a sub-optimal solution for the TSP using a newly designed inver-over (IO) operator. The Proposed IO operator is utilized to improve the solution derived from the ACO. This refined solution is then employed in the GA, where a genetic operator is applied alongside other randomly selected members from the initial population during the second phase. GA is used with the proposed crossover operator and the 2-opt heuristic in this phase to achieve optimal solution refinement towards a global optimum. Our evaluation of the algorithm’s efficacy uses benchmark datasets from TSPLIB. The proposed approach gives superior solution quality, both the average and the best solution metrics, demonstrating enhanced performance with a lower percentage of best error and percentage of average error. Experimental results indicate that the hybrid approach outperforms the efficiency of other state-of-the-art techniques. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： 2-Opt optimal exchange Ant colony optimization IO operator Travelling salesman problem

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Machine Learning and Data Mining for Predictive Modeling of Stroke Risk and Post-Stroke Care Management 3

Machine Learning and Data Mining for Predictive Modeling of ...

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

作者： Gobi, N. Singh, B.P. Yadav, Surendra Department of Computer Science and Information Technology Bangalore India Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350343274

The research investigates the efficacy of a comprehensive machine learning and data mining approach in predictive modeling of stroke risk and post-stroke care management. A holistic methodology is proposed, integrating diverse datasets and advanced algorithms, including Logistic Regression, Random Forest, and Long Short-Term Memory (LSTM) networks. Data preprocessing, feature extraction, and personalized care plan generation are key components. Performance evaluation using accuracy, sensitivity, specificity, precision, F1 Score, AUC, and MAE demonstrates the superior predictive capabilities and efficiency of the proposed method compared to traditional approaches. The study highlights the potential for revolutionizing stroke care, emphasizing personalized risk assessment and post-stroke management for improved patient outcomes and healthcare delivery. © 2023 IEEE.

关键词： Data mining

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Real-Time Data Streams: The Future of Predictive Maintenance in Manufacturing 3

Real-Time Data Streams: The Future of Predictive Maintenance...

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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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Assessment of Blood Donor Information Using Kernel Density Maps: A Case Study at Gangtok District, Sikkim and Modeling of a Web application. 3

Assessment of Blood Donor Information Using Kernel Density M...

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3rd IEEE International Conference on Computing, Communication, and Intelligent Systems, ICCCIS 2022

作者： Nirola, Madhab Gupta, Mousumi Sharma, Arpan Sikkim Manipal Institute of Technology Department of Computer Application Sikkim Majitar India

ISBN: (纸本)9781665462006

Acquiring blood donor information is a very time-consuming process and may put the patient's health at risk. Finding blood donor data is essential for receiving the correct type of blood as quickly as possible. This study aims to develop a web application to alleviate the hassles faced by critical patients. We acquire sample data from CRH hospital in Gangtok, Sikkim. We assess data from blood donors using ArcGIS platforms. Further, we generate a region-wise heat map. We include 391 blood donor records of which 165 are A+ Ve, 94 are B+ Ve, 43 are AB+ Ve, 3 are B- Ve, and 86 were O+ Ve. Through the modeled application users can locate blood donor information near them and communicate with the blood donor by direct phone call using the assessed heatmaps. Users can also register themselves by providing information such as their personal information, blood type, etc., which will be displayed on the heatmap for future use. Through the modeled application blood banks in that area can Figure out which locations have a higher volume of a specific blood type that they require, and then set up a camp or encourage volunteers to donate blood to help. © 2022 IEEE.

关键词： Blood

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Malaria and Pneumonia Disease Prediction Using Deep Learning 2

Malaria and Pneumonia Disease Prediction Using Deep Learning

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Rai, Vijendra Singhal, Manoj Dwivedy, Bhoopendra Jain, Pallavi Ritika Abes Institute of Technology Department of Information Technology Ghaziabad India Gl Bajaj Institute of Technology and Management Department of Information Technology Greater Noida India School of Computer Science and Engineering Galgotia's University Greater Noida India School of Computer Application and Technology Galgotia's University Greater Noida India

ISBN: (纸本)9798350371055

Early identification, intervention, and efficient treatment planning all rely heavily on disease prediction. This study examines the methods for disease prediction based on deep learning, concentrating on pneumonia and malaria in particular. Pneumonia and malaria are two common, sometimes fatal infections, especially in underdeveloped areas. Traditional diagnostic techniques for these disorders can be time-consuming and frequently need for specialized knowledge. Therefore, it is necessary to investigate automated and reliable illness prediction methods. This study's goal is to assess how well deep learning algorithms can foresee malaria and pneumonia from medical imaging data. The study makes use of a broad dataset that includes chest X-ray pictures for the identification of pneumonia and blood smear images with malaria infection. To improve the dataset's quality and variety, preprocessing procedures including picture normalization and augmentation are used. The results of this research demonstrate the potential of deep learning algorithms for malaria and pneumonia prediction, which advances the field of medical diagnostics. These automated prediction algorithms could help medical personnel diagnose patients quickly, allowing for quick treatment to start and better patient outcomes. © 2024 IEEE.

关键词： Backpropagation CNN Deep Learning Inspection V3 Model Jupiter Python Transfer Learning

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Mathematical Modeling using Rough Set and Random Forest Model to Predict Wind Speed 9

Mathematical Modeling using Rough Set and Random Forest Mode...

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9th International Conference on Computing for Sustainable Global Development, INDIACom 2022

作者： Das, Ratnakar Mishra, Jibitesh Mishra, Sujogya Pattnaik, P.K. Das, Subhalaxmi Odisha University of Technology and Research Dept. of Computer Science and Application Bhubaneswar India Utkal University Dept. of Computer Science and Application Bhubaneswar India

ISBN: (纸本)9789380544441

Prediction of meteorological phenomena using conventional techniques is always a challenge for meteorologists. We developed a novel method for predicting meteorological phenomena by using the Random Forest Model (RFM) and Rough Set Theory (RST). We have used RST to find the most significant meteorological phenomena, and then by using RFM, we have derived a set of predictive results. Using RST on vague meteorological data, we find that the significant meteorological phenomenon is wind speed. After using the RFM based on the outcome, we have predicted the wind speed. We used the Chi-square test to validate our claim. For our analysis, we collected various meteorological data from different parts of our Odisha state in India. © 2022 Bharati Vidyapeeth, New Delhi.

关键词： Rough set theory

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Performance Issues and Research Challenges in Designing a Secure IoT with Blockchain 3

Performance Issues and Research Challenges in Designing a Se...

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3rd International Conference on Issues and Challenges in Intelligent Computing Techniques, ICICT 2022

作者： Hemani Singh, Dayashankar Dwivedi, Rajendra Kumar Madan Mohan Malaviya University of Technology Department of Information Technology and Computer Application Gorakhpur India

ISBN: (纸本)9781665482684

Now a day, Internet of Things (IoT) are being used in several areas such as vehicular systems, smart city, healthcare, and supply chain management. Sensor data is stored in the cloud in an IoT system. Therefore, there is always a security concern with this data. There are several techniques for maintaining security, but most of them work in a centralized manner. However, blockchain is an emerging technology that works in a distributed manner and is based on peer-to-peer computing. Security can be enhanced using blockchain in any system. Blockchain technology offers decentralised security and privacy. Sensors used in loT systems are resource constrained. Therefore, implementing blockchain in any loT system creates several research challenges. This paper provides a discussion on such performance issues and research challenges while integrating blockchain with loT. The paper also gives some insights to overcome such issues. © 2022 IEEE.

关键词： Smart contract

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Partial Attention-Based Direction-Aware Vehicle Re-identification 18th

Partial Attention-Based Direction-Aware Vehicle Re-identific...

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18th International Conference on computer Science and Education, ICCSE 2023

作者： Zhou, Yujie Yuan, Caihong Su, Chenshuang Zou, Mingdong Zhu, Xiaoke Liang, Wenjuan School of Computer and Information Engineering Henan University Henan Engineering Research Center of Intelligent Technology and Application Henan Province Spatial Information Processing Engineering Technology Research Center Kaifeng China

ISBN: (纸本)9789819707294

With the rapid development of urban transportation, vehicle re-identification has become a focal point in traffic management and vehicle tracking problems. In order to address the problem of small inter-class similarity among vehicles, previous studies utilize vehicle parsing models to extract local features. Therefore, we introduce the Squeeze-and-Excitation attention mechanism to extract important discriminative information from these local features. Furthermore, we propose a local co-occurrence attention mechanism to represent the proportion of common parts feature matching. To address the issue of large intra-class differences caused by vehicle direction change, we propose a lightweight and effective direction weighted fusion strategy. Experiments on two large datasets show that the proposed algorithm performs competitively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Urban transportation

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Vision Transformer in Oral Cancer Detection

Vision Transformer in Oral Cancer Detection

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Agarwal, Pinky Gupta, Naman Bharadwaj, Yashita Yadav, Anju Mathur, Pratistha Manipal University Jaipur Computer Application Jaipur India Jaipur India Manipal University Jaipur Information Technology Jaipur India

ISBN: (纸本)9798350300857

Oral cancer is a malignancy that can manifest in multiple areas of the mouth, including the tongue, lips, oral mucosa, gums, floor of the mouth, and other soft tissues. Despite the development of advanced diagnostic and therapeutic methods, the incidence and prevalence of oral cancer continue to be a cause for concern. This condition can have devastating effects on patients, including significant physical and emotional distress. Detecting oral cancer at an early stage can improve a patient's chances of survival and reduce the need for aggressive treatment. In recent years, machine learning techniques have demonstrated promising results in the detection of oral cancer. This research aims to explore the role of vision transformers in oral cancer classification as benign and malignant. The results obtained from the vision transformer are compared with Machine learning techniques like SVM, extreme Gradient Boosting (XGBoost), and Random Forest. Overall, the results show that ViT outperformed SVM, Random Forest, and XGBoost in oral cancer detection and stage classification with an accuracy of 96.7%. The accuracy, precision, recall, and F1-score of ViT are higher than other methods. The findings of the research show that all four machine learning methods examined can produce high precision rates in detecting oral cancer, with the Vision Transformer technique achieving superior results as vision transformers are designed to capture global context information in images. The proposed system has great potential to advance the precision and efficiency of oral cancer diagnosis, which can lead to more prompt and effective treatment. © 2023 IEEE.

关键词： Diseases

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