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

作者： Chandra, Subhash Rengarajan, A. Sahoo, Girija Shankar Sharma, Sunil Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science & Application Vivekananda Global University Jaipur India

ISBN: (纸本)9789819780426

This paper presents a unique method, which investigates the neuronal damage and plasticity, the use of changes in diffusion tensor imaging (DTI). Thru combining the information from DTI, collectively with cognitive and demographic records, this method identifies and assesses the adjustments inside the brain's white count number integrity. The findings suggest that the proposed approach is capable of signify the structural modifications in the white be counted, whilst additionally offering significant data about the diploma of neuronal harm and plasticity. This research consequently gives a significant device for in addition investigation into the diagnosis of certain neurological illnesses, especially in stroke and traumatic brain injury, wherein identifying modifications within the mind is critical for proper direction of remedy. In addition, the challenges related to the DTI statistics analysis is discussed, collectively with capability applications and future extensions. This paper presents a complete survey of ways Diffusion Tensor Imaging (DTI) may be applied to become aware of neural harm and plasticity. It explains how DTI can discover microstructural modifications in frightened tissue, making it a promising device for Neuro-imaging research. The paper compares the maximum popular indices used in analysis and model fitting the use of DTI to measure modifications, including fractional anisotropy and imply diffusivity. Subsequently, the paper examines destiny guidelines and potential packages of DTI within the clinical setting. In the end, the paper shows that DTI gives a valuable supply of perception into the neural machine, allowing researchers to discover, quantify, and better apprehend structural modifications to neurons. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Diffusion tensor imaging

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Applying Transfer Learning to Medical Image Classification Tasks 5th

Applying Transfer Learning to Medical Image Classification T...

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

作者： Gupta, Manali Haripriya, V. Singh, Akhilendra Pratap Yadav, Surendra Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science & Application Vivekananda Global University Jaipur India

ISBN: (纸本)9789819780426

Transfer studying is a machine learning approach that permits a model to leverage know-how from a previous challenge to boost studying in the following tasks. It's been used increasingly in medical photo classification obligations, allowing area-particular knowledge to be leveraged from current fashions. This method can enhance the overall performance of clinical photograph-type structures and reduce the amount of labeled information required to analyze a brand-new venture. In this paper, we assess the present-day kingdom of switches, getting to know the scientific photo category and the results for clinical picture evaluation. We discuss present switch studying techniques and their influences on the accuracy of medical photo category responsibilities and the challenges related to constructing generalizable fashions. Eventually, we take a look at existing datasets and implementations available for the clinical photo category and factor into promising destiny studies guidelines. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Understanding the Role of Data-Driven Communications Software Design in Networks 5th

Understanding the Role of Data-Driven Communications Softwar...

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

作者： Singh, Vaishali Inzimam Dadhich, Shruti R, Murugan Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science and Application Vivekananda Global University Jaipur India Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bengaluru India

ISBN: (纸本)9789819780426

Facts-pushed verbal exchange software program layout is a critical element in the design of statistics networks. It encompasses the identification, control, and manipulate of network traffics, which includes the prioritization of communications and associated statistics, in addition to the regulation of connection distributions and assets over the complete community. Furthermore, information-pushed communique software program design encompasses algorithms designed to mitigate potential verbal exchange bottlenecks, via controlling the drift of information via the network, thereby improving its reliability and serviceability. Thru this technique of design, it's far possible to create a dynamically optimized community that is capable of speedy and successfully routing facts to its supposed vacation spot. Through making use of these standards, designers and engineers are able to create high-appearing networks that fulfill the demands located upon them. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Software design

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Examining the Advantages of Dynamic Communications Software Design in Networks 5th

Examining the Advantages of Dynamic Communications Software ...

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

作者： Raghavendra, R. Das, Neeraj Inzimam Kurmi, Raviraj Singh Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science and Application Vivekananda Global University Jaipur India Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India

ISBN: (纸本)9789819780426

This paper examines the blessings of dynamic communications software program design in networks. By leveraging advances in generation, dynamic communications provide an ever-evolving method of connecting, communicating, and sharing facts inside networks without relying on static, often outdated, hardware. The principle blessings related to this community layout are scalability, flexibility, and excellent reliability. As networks extend, dynamic communications software can auto-regulate accordingly to ensure the overall capability of the general gadget. Moreover, users can count on operational performance upgrades and extended capability to address protection and privacy worries. Moreover, using dynamic software allows for more green aid usage, which facilitates lessening expenses related to the control and operation of the network. Moreover, dynamic communications have also helped reduce downtime and sell extra resilience in the face of surprising changes or unanticipated threats. Standard, this paper provides an in-depth knowledge of the advantages of dynamic communications software layout in networks. Dynamic Communications software program design and its various benefits in networking have been gaining substantial attention in recent years. Dynamic Communications software program layout additionally allows protocols to be modified on an as-needed foundation. That is useful for networks that might be constantly changing and want to adapt, which will take care of the converting environment. New protocols may be introduced or eliminated as wished, and the gadget will adjust to make the most efficient performance. In addition, using Dynamic Communications software design allows for a more straightforward integration of different systems. It is useful for networks that require a couple of systems to work together in order for them to function well. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Software design

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Leveraging Machine Learning to Optimize Real-Time Data Analysis Performance 5th

Leveraging Machine Learning to Optimize Real-Time Data Analy...

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

作者： Singh, Vaishali Kaushik, Harshita Verma, Archana Raghavendra, R. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science and Application Vivekananda Global University Jaipur India Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India

ISBN: (纸本)9789819780426

Leveraging system mastering (ML) algorithms has become an increasingly exciting technique for optimizing actual-time facts analysis performance. In the ever-growing age of massive facts, ML permits us to effectively make the experience of this information extra quickly and correctly. Traditionally, real-time statistics analysis requires complex guide evaluation or is constrained by the insufficient skills of conventional rule-based total strategies. ML can automate this method and identify patterns in the records that may not be apparent at once, even optimizing performance significantly. In addition, ML algorithms, including supervised and unsupervised methods, can be applied to label facts because it comes in, making it simpler to identify styles inside the statistics before a manual evaluation. This technique is called predictive analytics, and it dramatically reduces the time spent manually exploring the statistics. Usually, leveraging system learning to optimize the performance of actual-time records evaluation can improve the process of creating a sense of these facts. Through that specialization in predictive analytics and utilizing supervised and unsupervised techniques, we will quickly and appropriately analyze the statistics and make powerful selections. It can be pretty helpful in a selection of industries and conditions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Predictive analytics

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BP-LSTM-ATTENTION: A Regional Precipitation Multisource Data Processing Fusion Model 24

BP-LSTM-ATTENTION: A Regional Precipitation Multisource Data...

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8th International Conference on Electronic Information technology and computer Engineering, EITCE 2024

作者： Zhang, Xiaodan Wang, Hongyu You, Shaojie Zhao, Tong Li, Zi School of Computer Technology and Applications Qinghai University Qinghai Xining China Qinghai Provincial Laboratory for Intelligent Computing and Application Qinghai Xining China Qinghai Provincial Institute of Meteorological Sciences Qinghai Xining China

ISBN: (纸本)9798400710094

Qinghai Province shoulders the significant mission of maintaining ecological security, protecting the three-river source region, and safeguarding the ‘Chinese Water Tower’. To improve the accuracy of precipitation data is a great importance for studying regional ecological changes. The paper takes Qinghai Province as the research area and develops a multi-source precipitation data fusion model: BP-LSTM-ATTENTION, which is based on BP network, LSTM network and attention mechanism. The model uses satellite radar precipitation data and auxiliary data such as vegetation normalized difference index as feature data, and ground observation station data as label values, and through training and evaluation, improving the accuracy of data fusion. Then, a regional long-term and high spatial resolution precipitation fusion dataset with a spatial resolution of 1Km, a temporal resolution of 3 hours, and a time series from 2000 year to 2020 year, was generated by the data fusion model. Taking the fusion processing of precipitation data from 2016 to 2020 as an example, the fusion model successfully reduced the average RB of precipitation fusion data in the study area to 14.6%, the average RMSE to 0.1566, the average MAE to 43.706, and the average CC to 0.906. The experiment shows that the precipitation data fusion model studied in this paper has high data fusion processing accuracy and good precipitation data prediction performance. In terms of the model’s ability to capture precipitation events, the precipitation data fused by the model has an average POD of 0.818, an average FAR of 0.318, and an average MISS of 0.182 from 2016 to 2020, demonstrating its strong capability in monitoring precipitation events and good performance in reducing false alarms and missed reports. The BP-LSTM-ATTENTION model proposed in this study solves the problems of spatiotemporal correlation and time series in precipitation data of Qinghai Province, and has produced high-resolution, high spatiotemporal

关键词： Water towers

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Balancing data imbalance in biomedical datasets using a stacked augmentation approach with STDA, DAGAN, and pufferfish optimization to reveal AI's transformative impact

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International Journal of Information technology (Singapore) 2025年第1期17卷 455-480页

作者： Veedhi, Bhaskar Kumar Das, Kaberi Mishra, Debahuti Mishra, Sashikala Behera, Mandakini Priyadarshani Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to Be) University Odisha Bhubaneswar India Department of Computer Application Siksha ‘O’ Anusandhan (Deemed to Be) University Odisha Bhubaneswar India Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed to Be) University Maharashtra Pune India School of Computer Engineering KIIT (Deemed to Be) University Odisha Bhubaneswar India

This study systematically assesses the effectiveness of data augmentation techniques, focusing particularly on the stacked approach, across a diverse range of biomedical datasets. Advanced data augmentation methods, including style transfer data augmentation (STDA) and data augmentation generative adversarial network (DAGAN), offer promising solutions in this context. STDA leverages style transfer techniques from computer vision to create varied and realistic synthetic samples while preserving semantic content, thereby enhancing dataset variability and improving model generalization. Conversely, DAGAN utilizes generative adversarial networks (GANs) to learn the underlying data distribution, resulting in the generation of high-quality synthetic samples. Subsequently, STDA and DAGAN techniques are applied to the selected data, followed by convolution and pooling operations on the augmented samples. The pooled samples are then stacked, and the Cosine similarity is calculated to measure the resemblance to the original dataset. Parameters of STDA and DAGAN are fine-tuned to maximize the Cosine similarity. Additionally, a metaheuristic optimization algorithm called Pufferfish optimization algorithm (POA) is employed to optimize decision variables—learning rate, number of hidden units (common for both STDA and DAGAN), number of attention heads for STDA, and latent space dimensionality of DAGAN. The classification performance of the proposed POA-stacked STDA-DAGAN data augmentation methodology, along with variants using genetic algorithm (GA) and particle swarm optimization (PSO), is compared with original datasets and SMOTE balanced datasets using various classifiers and the observed accuracies, sensitivity, specificity and F-score are ranging from 91.83% to 99.11%, 91.38% to100%, 92.50% to 100% and 92.95% to 98.32% respective for all ten biomedical datasets used in this research. Additionally, the class distribution graphs, learning curves, statistical validation, and exe

关键词： Biomedical data Data augmentation Data augmentation generative adversarial network (DAGAN) Pufferfish optimization algorithm (POA) Style transfer data augmentation (STDA)

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A Unified Fog Computing Architecture for Next-Generation Smart City Traffic and Crime Management

A Unified Fog Computing Architecture for Next-Generation Sma...

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Artificial Intelligence and Data Engineering (AIDE), International Conference on

作者： Vikas Rai J Ashwini K M Department Of Master Of Computer Application NMAM Institute of Technology Nitte (Deemed to be University) India

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

The rapid growth of urbanization has led to a surge in demand for smart city systems that can efficiently manage resources and ensure safety. This paper delves into the design and simulation of a Smart City System that leverages Fog Computing. Fog Computing involves distributing computation and storage resources across devices located at the network edge. The system integrates a diverse range of sensors, including surveillance cameras, VPR (Vehicle Plate Recognition) cameras, and audio sensors, strategically deployed across different city areas. These sensors monitor traffic, detect anomalies, and promptly provide real-time alerts. The Fog architecture enhances system responsiveness by processing data closer to its source, thereby reducing latency and bandwidth consumption. To model and simulate the interaction between various Fog devices, cloud resources, and edge-based applications, we employ CloudSim, a cloud simulation tool. By meticulously mapping different application modules to devices based on their capabilities, we simulate the execution of smart city applications, such as video capture, traffic detection, and audio alarm triggering. The results show the possibility of employing Fog Computing for building an efficient, scalable, and responsive system for a smart city. Fog computing has the potential to significantly impact large urban areas by processing data at the edge, thereby reducing latency and bandwidth consumption.

关键词： Smart cities Image edge detection Surveillance computer architecture Bandwidth Cameras Sensor systems Surges Intelligent sensors Edge computing

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Structural Connectivity Analysis in Multiple Sclerosis Using Diffusion Tensor Imaging 5th

Structural Connectivity Analysis in Multiple Sclerosis Using...

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

作者： Yadav, Dhyan Chandra Doda, Davendra Kumar Joshi, Apoorva Rengarajan, A. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Electrical 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

Structural connectivity evaluation (SCA) is a powerful tool for characterizing mind circuitry and assessing nearby structural adjustments in the mind. Diffusion Tensor Imaging (DTI) is a complicated magnetic resonance imaging technique used to determine the structural integrity of white be-counted tissue and to map the fiber pathways connecting excellent mind areas. This method allows for non-invasive exploration of structural connectivity for the motive of bdd5b54adb3c84011c7516ef3ab47e54, the underlying dynamics of mind networks. This paper uses SCA to sing the changes in mind networks due to more than one Sclerosis (MS) by measuring the integrity of white be-counted number pathways. The consequences of the evaluation display that SCA is useful in detecting close-by changes inside the corpus callosum structure that correlate with the development and severity of MS. Furthermore, it is well-known that MS induces adjustments inside the connectivity among wonderful mind regions that appear to be location unique. The findings display that SCA using DTI is a precious device for statistics of the impact of MS on neural networks at a few stages inside the thoughts. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Tensors

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Analyzing the Effects of Coding and Compression on Network Performance 5th

Analyzing the Effects of Coding and Compression on Network P...

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

作者： Agarwal, Ankita Kumar, Deepak Singh, Alka Preethi, D. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Mathematics Vivekananda Global University Jaipur India Department of Master of Computer Application Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bengaluru India

ISBN: (纸本)9789819780426

This paper objectives to research the consequences of coding and compression on network performance. Network coding is the technique of encoding information before it is despatched over a network and has become an outstanding method to enhance community overall performance. Compression then again is used to reduce the size of the data before transmission, ensuing in quicker transmission time. This observe evaluates the effect those techniques have on network overall performance by measuring the bandwidth utilization, throughput, latency, and packet losses. A test becomes conducted to have a look at how the strategies have an effect on one of kind mixtures of information speeds, packet sizes, and facts loading. Results display that both strategies had a fine impact at the community overall performance, with network coding and compression having an extra effect on networks with higher facts hundreds. Moreover, the effects show that community coding had a greater impact on networks with larger packets, while compression had a more effect on networks with smaller packets. This studies gives insight into how community coding and compression can be used to enhance network overall performance. Coding and compression are important components of network performance that have been studied and tested to have a full-size influence on network pace, scalability, and reliability. Coding is used to reduce the amount of data that is sent over a community, by way of reducing the quantity of records frames that need to be sent and growing the performance of the records transfer technique. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network coding

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