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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Rahman, Mohammed Ziaur Mishra, Awakash Ojha, Ananta Goswami, Shubhashish School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Computer Science & Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India

ISBN: (纸本)9789819780426

Using a realistic dataset based on the United Kingdom clinical exercise studies Datalink, various architectures are evolved and evaluated to decide the simplest for predicting the risk of cardiac arrest. The architectures examined include several modified lengthy short-term memory networks, multi-input/multi-output architectures, Convolutional Neural Networks (CNNs), and Residual Neural Networks. Moreover, the consequences of the architectures on the predictions are compared while the statistics are supplemented with external capabilities. Furthermore, the results recommend increasing the facts with external functions, enhancing prediction accuracy for several architectures. Via this observation, conclusions may be drawn about which architectures are satisfactory-desirable for predicting cardiac arrest chance, as well as the consequences of external capabilities on the effectiveness of the architectures. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Analyzing Clinical Data for Improved Diagnosis Using AI/ML Algorithms in Smart Health Care 5th

Analyzing Clinical Data for Improved Diagnosis Using AI/ML A...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Lakshmisha, S.K. Alam, Intekhab Bhuvana, J. Bhatt, Rahul School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India

ISBN: (纸本)9789819780426

This study analyzes the capability for using AI/ML algorithms to improve diagnostic accuracy in smart health care. AI/ML strategies have revolutionized clinical analysis by way of offering state-of-the-art and dependable techniques for studying clinical facts. Via using superior techniques such as machine learning, deep gaining knowledge of, reinforcement studying, temporal pattern recognition, and superior decision tree algorithms, AI/ML algorithms provide plenty extra accurate predictions of diseases and conditions from affected person records. Further, AI/ML algorithms can discover relationships between different clinical elements that aren't visible to medical doctors and clinicians. Through the use of AI/ML algorithms, doctors will be able to make greater correct diagnoses and provide higher remedies to patients. The consequences of this take a look at imply that the AI/ML algorithms tested own the ability to enhance prognosis in clever healthcare in phrases of accuracy, precision, do not forget, and cost-effectiveness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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Spatial Filtering for Improved Target Identification from Hyper Spectral Images 5th

Spatial Filtering for Improved Target Identification from Hy...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Agarwal, Trapty Raghavendra, R. Goyal, Rohit Neekhra, Bhavesh Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789819780426

Hyper Spectral Imaging (HSI) is a powerful approach used in far flung sensing packages that captures spatially contiguous set of spectrum statistics from a scene. HSI information incorporates beneficial facts about the goals within the scene;however, due to noise and various distortions, it can be tough to discover those objectives. Spatial filtering is a method used to reduce undesirable noise in images if you want to make goal identity easier and extra accurate. On this paper, we endorse a brand new technique evolved for applying spatial filters on HSI pics that utilizes a spatially adaptive sigma clear out to successfully lessen noise and improve target identity accuracy. The advanced method correctly carries spatial variability within the filter length and clear out parameters to obtains progressed goal discrimination overall performance. The proposed technique is evaluated the use of both simulated and actual HSI records sets and effects display progressed target accuracy when as compared to traditional spatial filters. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Wiener filtering

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ChatGPT as a Software Development Bot: A Project-Based Study 19

ChatGPT as a Software Development Bot: A Project-Based Study

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19th International Conference on Evaluation of Novel Approaches to Software Engineering, ENASE 2024

作者： Waseem, Muhammad Das, Teerath Ahmad, Aakash Liang, Peng Fahmideh, Mahdi Mikkonen, Tommi Faculty of Information Technology University of Jyväskylä Jyväskylä Finland School of Computing and Communications Lancaster University Leipzig Leipzig Germany School of Computer Science Wuhan University Wuhan China

ISBN: (纸本)9789897586965

Artificial Intelligence has demonstrated its significance in software engineering through notable improvements in productivity, accuracy, collaboration, and learning outcomes. This study examines the impact of generative AI tools, specifically ChatGPT, on the software development experiences of undergraduate students. Over a three-month project with seven students, ChatGPT was used as a support tool. The research focused on assessing ChatGPT’s effectiveness, benefits, limitations, and its influence on learning. Results showed that ChatGPT significantly addresses skill gaps in software development education, enhancing efficiency, accuracy, and collaboration. It also improved participants’ fundamental understanding and soft skills. The study highlights the importance of incorporating AI tools like ChatGPT in education to bridge skill gaps and increase productivity, but stresses the need for a balanced approach to technology use. Future research should focus on optimizing ChatGPT’s application in various development contexts to maximize learning and address specific challenges. © 2024 by SCITEPRESS – science and technology Publications, Lda.

关键词： Software design

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Optimizing Wireless Sensor Network Connectivity Using Math Modeling Techniques 5th

Optimizing Wireless Sensor Network Connectivity Using Math M...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Gobi, N. Bhatnagar, Gunjan Bhavana, A. Mishra, Awakash Karnataka Bangalore India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Manduwala Dehradun Navgaon India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

Wi-Fi sensor networks (WSNs) are extensively deployed in various programs and have an increasing significance within the net of things (Iota). However, their connectivity remains a chief challenge, especially in large-scale networks, as it's far crucial for reliable communication and green statistics aggregation. To address this project, this paper presents a unique method to optimize the connectivity of WSNs through utilizing mathematical modeling techniques. Mainly, nonlinear optimization methods are hired to acquire a surest deployment sample for a given community configuration that maximizes the connectivity metric. The proposed method is proven to provide better connectivity outcomes than conventional solutions and is analyzed each theoretically and empirically with extensive simulations. Simulation outcomes additionally show that the method is robust to various node deployment techniques. Furthermore, it is evaluated using exceptional styles of environments and a huge variety of community configurations. The offered outcomes demonstrate the versatility and scalability of the proposed optimization method in optimizing the connectivity of WSNs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sensor nodes

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Investigating the Use of Multi-Sourced Input Data for Time Series Algorithms Applied to Hyper Spectral Imagery 5th

Investigating the Use of Multi-Sourced Input Data for Time S...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Raj, Shanthi Pichandi Ananda Mishra, Awakash D, Preethi Sivaraman, Himani School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Computer Science & Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Manduwala Dehradun Navgaon India

ISBN: (纸本)9789819780426

This paper examines the efficacy of merging multi-sourced enter records into time series algorithms for analyzing hyperspectral imagery. Combining entered information from different resources (e.g., from Radar, Landsat, and LiDAR) with usually used time collection algorithms (e.g., SVM, Random Forests, and Gradient Boosting) is investigated in this study. First, Landsat, LiDAR, and Radar data are purified, and the derived pixels are stacked and rasterized to generate input datasets as collections of vectors. Secondly, the entered datasets are transformed into temporal capabilities with the same old Deviation technique (SDM) and minimum Spanning Tree (MST) to visualize the records from a temporal angle. The temporal features enter the various time series algorithms for hyper hyperspectral type. The paper concludes by showing the capacity improvements because of merging more than one asset within the analyzed algorithms and further discussing feasible explorative programs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： analyzed explorative hyperspectral

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Application of AI/ML Algorithms for Enhancing Precision in Medical Diagnostic Processes 5th

Application of AI/ML Algorithms for Enhancing Precision in M...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Savita Raghavendra, R. Bhatt, Rahul Yamanappa Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789819780426

The utility of artificial intelligence (AI) and gadget getting to know (ML) algorithms to medical diagnostic processes has been developing in significance in recent years. AI/ML models were evolved to help clinical professionals in imparting correct and green diagnostic approaches. Those models are capable of providing precise and correct diagnoses at the same time as reducing the complexity of conventional decision making. AI/ML algorithms are getting used to discover styles in medical statistics that could in any other case be hard for human beings to come across or to offer contextual information to refine and enhance the accuracy of diagnoses. The benefits of using AI/ML algorithms for enhancing precision in medical diagnostic tactics consist of rapid and accurate prognosis, extended performance, and better clinical consequences. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Diagnosis

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Investigating the Use of Hierarchical Recurrent Neural Networks for Medical Image Segmentation 5th

Investigating the Use of Hierarchical Recurrent Neural Netwo...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Suneetha, K. Dobriyal, Rohit Elangovan, Vetrimani Savita Karnataka Bangalore India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

This paper investigates the usage of hierarchical recurrent neural networks (HRNNs) for clinical photo segmentation. HRNNs are a neural network structure combining more than one layer of recurrent cells and layers of convolution cells. The intention of this paper changed to evaluate the effectiveness of HRNNs for medical picture segmentation and how well it can be applied for this reason. The paper evaluates a present HRNN implementation for medical picture segmentation and shows its accuracy primarily based on the dice coefficient (DC). The authors additionally proposed a changed HRNN architecture to improve the segmentation performance: the architecture integrated residual connections, multi-scale inputs, multi-resolution outputs, and an utterly convolution structure. The proposed DT-SL loss was potent in improving the segmentation accuracy. Typically, the outcomes reveal that HRNNs are promising devices for clinical photograph segmentation tasks and can effectively facilitate faster and extra correct segmentation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Recurrent neural networks

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A full privacy-preserving distributed batch-based certificate-less aggregate signature authentication scheme for healthcare wearable wireless medical sensor networks (HWMSNs)

A full privacy-preserving distributed batch-based certificat...

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作者： Rabie, Osama Bassam J. Selvarajan, Shitharth Hasanin, Tawfiq Mohammed, Gouse Baig Alshareef, Abddulrhman M. Uddin, Mueen Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Cybersecurity Center King Abdulaziz University Jeddah Saudi Arabia Department of Computer Science Kebri Dehar University Kebri Dehar Ethiopia School of Built Environment Engineering and Computing Leeds Beckett University LeedsLS1 3HE United Kingdom Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India College of Computing and Information Technology University of Doha for Science and Technology Doha24449 Qatar

The dynamic connectivity and functionality of sensors has revolutionized remote monitoring applications thanks to the combination of IoT and wireless sensor networks (WSNs). Wearable wireless medical sensor nodes allow continuous monitoring by amassing physiological data, which is very useful in healthcare applications. These text data are then sent to doctors via IoT devices so they can make an accurate diagnosis as soon as possible. However, the transmission of medical text data is extremely vulnerable to security and privacy assaults due to the open nature of the underlying communication medium. Therefore, a certificate-less aggregation-based signature system has been proposed as a solution to the issue by using elliptic curve public key cryptography (ECC) which allows for a highly effective technique. The cost of computing has been reduced by 93% due to the incorporation of aggregation technology. The communication cost is 400 bits which is a significant reduction when compared with its counterparts. The results of the security analysis show that the scheme is robust against forging, tampering, and man-in-the-middle attacks. The primary innovation is that the time required for signature verification can be reduced by using point addition and aggregation. In addition, it does away with the reliance on a centralized medical server in order to do verification. By taking a distributed approach, it is able to fully preserve user privacy, proving its superiority. © 2023, The Author(s).

关键词： Sensor nodes

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Fuzz-classification(p,l)-Angel:An enhanced hybrid artificial intelligence based fuzzy logic for multiple sensitive attributes against privacy breaches

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Digital Communications and Networks 2023年第5期9卷 1131-1140页

作者： Tehsin Kanwal Hasina Attaullah Adeel Anjum Abid Khan Gwanggil Jeon Department of Computer Science COMSATS University IslamabadPakistan Department of Information Technology Quaid-e-Azam University IslamabadPakistan College of Science and Engineering School of Computing and MathsUniversity of DerbyDE221GBUK Department of Embedded Systems Engineering Incheon National UniversitySouth Korea

The inability of traditional privacy-preserving models to protect multiple datasets based on sensitive attributes has prompted researchers to propose models such as SLOMS,SLAMSA,(p,k)-Angelization,and(p,l)-Angelization,but these were found to be insufficient in terms of robust privacy and performance.(p,l)-Angelization was successful against different privacy disclosures,but it was not *** the best of our knowledge,no robust privacy model based on fuzzy logic has been proposed to protect the privacy of sensitive attributes with multiple *** this paper,we suggest an improved version of(p,l)-Angelization based on a hybrid AI approach and privacy-preserving approach like ***-classification(p,l)-Angel uses artificial intelligence based fuzzy logic for classification,a high-dimensional segmentation technique for segmenting quasi-identifiers and multiple sensitive *** demonstrate the feasibility of the proposed solution by modelling and analyzing privacy violations using High-Level Petri *** results of the experiment demonstrate that the proposed approach produces better results in terms of efficiency and utility.

关键词： Generalization Fuzzy-logic MSA Privacy disclosures Membership function (p,l)-Angelization QT HLPN

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