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Recognition and evaluation of cutaneous condition through assorted artificial intelligence reliant algorithms

International Journal of Information technology (Singapore) 2025年 1-13页

作者： Mishra, Manmohan Yadav, Ajay Kumar Mazumdar, Bireshwar Dass Gupta, Prashant K. Panwar, Arvind Bharadwaj, Shivam Department of Computer Application United Institute of Management Prayagraj India School of Computer Science & Engineering Technology Bennett University Plot Nos 8-11 TechZone II Uttar Pradesh Greater Noida 201310 India Galgotias University Plot No. 2 Yamuna Expy opposite Buddha International Circuit Sector 17A Uttar Pradesh Prayagraj 203201 India

Our skin is the hefty organ that envelops and shields body. It prevents us from numerous fatal and non fatal diseases. It is observed that due to bacteria or other causes of infection, skin faces certain minor or life threatening diseases. The most prioritized step toward restoring health is early illness signs identification. Identifying Cutaneous Condition from clinical images is one of the foremost challenges in medical image investigation. In the presented study we will enlighten the various Artificial Intelligence techniques falling under the categories of supervised machine learning including Probabilistic classifier (Naïve Bayes), Statistical algorithm (Logistic Regression), Ensemble learning (Random Decision Trees), Data analysis technique (Convolutional Neural Network) and Kernel approach (Support Vector Machine) to identify and classify the cutaneous condition appropriately so that corrective measures of skin treatment can be endow with. The proposed approach entails collecting images as input, preprocessing, segmenting, feature extraction and lastly applying the classification algorithms to derive the Cutaneous Condition categories. Additional trials are conducted using the different approaches as indicated and it was discovered from the suggested tests that the Convolutional Neural Network strategy yields the best results overall. The proposed model is trained, tested, and evaluated using the International Skin Imaging Collaboration (ISIC) 2019 challenge dataset and Human Against Machine with 10,000 training images (HAM10000) for the detection of manifold Cutaneous Condition. © Bharati Vidyapeeth's Institute of computer applications and Management 2025.

关键词： Artificial intelligence Confusion matrix Convolutional neural networks Cutaneous condition Image processing Machine learning

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CiGeN 1.0: A Generative AI Enabled Medical Image Augmentation Pipeline for Deep Network Models

CiGeN 1.0: A Generative AI Enabled Medical Image Augmentatio...

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2025 International Conference on Ambient Intelligence in Health Care, ICAIHC 2025

作者： Dash, Archana Nayak, Debasish Swapnesh Kumar Swarnkar, Tripti Siksha 'O' Anusandhan Deemed to be University Department of Computer Science and Engineering Bhubaneswar751030 India Centurion University of Technology and Management Department of Computer Science and Engineering Bhubaneswar752050 India National Institute of Technology Department of Computer Application Raipur492010 India

ISBN: (纸本)9798331530259

Deep Learning methods have gained a significant momentum in medical image classification tasks, including many ranges of datasets. However, these datasets often suffer from class imbalance, where the minority class (e.g., diseased cases) is underrepresented. This imbalance leads to biased predictions, affecting the model's performance. Generative Adversarial Networks (GANs) offer a potential solution by generating synthetic images for the minority class, helping balance the dataset. Methodology: In this study we further evaluated our Deep Learning Model CoVaD-GAN with separate set of datasets to test the dynamics behind it. We applied a Deep Convolutional GAN (DCGAN) to generate synthetic images for underrepresented classes in the chest X-ray (CXR) and Break His histopathological dataset. Augmented synthetic images were made a part of training set, and the model's performance was evaluated before and after augmentation using VGG-16, ResNet-50 with necessary metric measures as accuracy, sensitivity, and specificity were compared pre- and post-GAN augmentation. We also evaluated the quality of GAN-generated images using Fréchet Inception Distance (FID) and manual expert reviews. The results demonstrate significant improvements in model accuracy, sensitivity, and F1-scores after applying GAN-based augmentation. The use of GANs for data augmentation enhances the ability of deep learning models to generalize, particularly for minority classes. GAN-generated images were of high quality and contributed positively to model performance. The Proposed model (CiGeN 1.0) dynamically generalizes the other medical dataset introduced to it and derived a fair resulting diagnosis. © 2025 IEEE.

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Revolutionizing Brain Tumor Diagnosis with Convolutional Neural Networks

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Procedia computer Science 2025年 259卷 562-571页

作者： Aryan Singh Mohd Shuaib Manoj Diwakar Aditya Joshi Prabhishek Singh Neha Pandey Department of Computer Application Graphic Era Deemed to be University Dehradun 248001 India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun 248001 India School of Computer Science Engineering and Technology Bennett University Greater Noida 201310 India Department of Biotechnology Graphic Era Deemed to be University Dehradun 248001 India

Historically, the diagnosis of brain tumors from magnetic resonance imaging (MRI) images has been a careful and extended endeavor that relies heavily on the expertise of radiologists and neurologists. In addition to being time-consuming, the complex nature of brain image analysis makes it vulnerable to cognitive biases and human error, which can affect the precision and effectiveness of diagnosis. Moreover, manual MRI scan interpretation might result in discrepancies between practitioners, which makes diagnosis even more difficult. These difficulties highlight the need for innovative approaches to improve the accuracy and speed of brain tumor identification as the need for quicker and more accurate medical evaluations keeps rising. To meet these difficulties, an innovative method based on image-driven Convolutional Neural Networks (CNNs) has been developed. Meningiomas, pituitary, gliomas, and absence tumors are the four major brain cancer groups that this research focuses on. With the basis of the EfficientNetB3 framework which had been pre-trained on the large-scale ImageNet dataset, the model uses a transfer learning method using a brain tumor dataset of 7023 images by adding extra layers that are exclusively meant to enhance classification and improved performance. The proposed method secures a notable accuracy of 99.84% and a precision of 99.33%. This advanced approach not only improves diagnostic precision but also significantly reduces the time required for neurologists to examine scans, enabling them to focus more on patient care and the development of preventive strategies. The efficiency of the proposed model underlines that it can become a breakthrough in clinical diagnostics and stimulate the progress of medical disciplines’ development. For a comprehensive overview of the model architecture and implementation, please visit the GitHub repository https://***/Aryansin1234/*** . Additionally, a live demonstration of

关键词： Brain Tumor classification Deep Learning Convolutional Neural Network Transfer Learning Magnetic Resonance Imaging

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A Study on Short-Term Nowcasting Precipitation Forecasting Methods Based on Radar Echo Maps

A Study on Short-Term Nowcasting Precipitation Forecasting M...

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IEEE Information technology and Mechatronics Engineering Conference (ITOEC)

作者： Jie Liu Xingchi Wang Guojing Zhang Department of Computer Technology and Applications Qinghai University Xining China Qinghai Provincial Laboratory for Intelligent Computing and Application Qinghai University Xining China

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

Short-To-medium-term precipitation nowcasting aims to predict changes in mesoscale and microscale weather systems, particularly severe convective systems, within 0 to 2 hours. Compared to large-scale weather systems, mesoscale and microscale systems are characterized by their abruptness, complex mechanisms, and short life cycles, making short-term precipitation forecasting a highly challenging field. This study focuses on the Yinchuan region as a case study, processing radar echo data through dimensionality reduction, noise reduction, frame interpolation, and data augmentation to construct datasets suitable for training and testing. The IAM4VPx5 model is employed for forecasting and is compared against traditional spatiotemporal prediction models, including SimVP, ConvLSTM, and ConvGRU. Experimental results demonstrate that the IAM4VPx5 model outperforms SimVP, ConvLSTM, and ConvGRU across multiple metrics, such as Critical Success Index (CSI), Heidke Skill Score (HSS), and Probability of Detection (POD). Additionally, IAM4VPx5 exhibits superior performance in Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). Overall, the IAM4VPx5 model demonstrates exceptional performance compared to SimVP, ConvLSTM, and ConvGRU in various aspects. These results validate the effectiveness and advantages of the IAM4VPx5 model in processing radar echo data, offering an efficient solution for weather radar forecasting.

关键词： Training Meteorological radar Rain Accuracy Predictive models Radar imaging Data augmentation Data models Spatiotemporal phenomena Forecasting

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Super-Resolution of Diffusion-Weighted Images via TDI-Conditioned Diffusion Model 15th

Super-Resolution of Diffusion-Weighted Images via TDI-Cond...

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15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2024

作者： Ma, Jiquan Teng, Yujun Chen, Geng Jiang, Haotian Zhang, Kai Liu, Feihong Rekik, Islem Shen, Dinggang School of Computer Science and Technology Heilongjiang University Harbin China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China School of Biomedical Engineering and State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech University Shanghai China School of Information Science and Technology Northwest University Xi’an China BASIRA Lab Imperial-X and Department of Computing Imperial College London London United Kingdom

ISBN: (纸本)9783031869198

Diffusion-Weighted Imaging (DWI) is a significant technique for studying white matter. However, it suffers from low-resolution obstacles in clinical settings. Post-acquisition Super-Resolution (SR) can enhance the resolution of DWIs and has gained increasing research interest in recent years. An advanced generative model, the Diffusion Model (DM), exhibits particularly promising performance in image SR. However, effective conditions are required to bootstrap the DM for DWI SR. To this end, we proposed the first DM-based DWI SR model with two effective conditions based on low-solution DWIs and Track Density Imaging (TDI) maps, which possess rich high-resolution prior knowledge Additionally, we consider another condition based on features from low-resolution DWIs. These two conditions are integrated into our model, which comprises three components: DWI Resolution Enhancer (DRE), DWI Feature Extractor (DFE), and TDI Feature Extractor (TFE). DRE combines low-resolution DWI features from DFE with TDI features from TFE to progressively generate high-resolution DWIs. We performed extensive experiments on DWIs of normal subjects from human connectome projects and patients with Parkinson’s disease. The results demonstrate that our model outperforms existing DWI SR models, both qualitatively and quantitatively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

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Innovative detection of IoT cyber threats using a GBiTCN-Temformer and MKOA framework

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Journal of Network and computer applications 2025年 240卷

作者： Rai, Vipin Mishra, Pradeep Kumar Joshi, Shivani Kumar, Rajiv Dwivedi, Avinash Amrita School of Computer Science & Engineering Galgotias University Greater Noida India Department of Computer Science and Application School of Engineering and Technology Sharda University India Department of Computer Science and Engineering GL Bajaj Institute of Technology and Management India Department of Applied Computational Science and Engineering NVIDIA AI Learning and Research Center GL Bajaj Institute of Technology and Management India Department of Computer Science and Engineering Galgotias University UP Greater Noida India Center for Excellence on Cyber Security and Cryptology Computer Science & Engineering School of Engineering & Technology Sharda University Greater Noida India

In the digital era, the Internet of Things (IoT) platform is progressively utilized in several applications due to its versatility, adaptability, and accessibility. Despite that, the lack of security protocols, improper device updates, and unauthorized access exposes the IoT environment to diverse cyber threats, which affect network security and user confidentiality. To enhance cybersecurity, various deep learning (DL) approaches have been developed, yet these techniques have struggled to detect various attacks affecting the IoT platform, often yielding imprecise results and high false positive rates. This research proposes a novel framework that leverages the Modified Kepler optimization algorithm (MKOA) based feature selection model and the Gated Bidirectional Temporal Convolutional Network (GBiTCN) based Temporal transformer (Temformer) detection model to improve the cybersecurity and integrity of the IoT devices. The MKOA technique is introduced to select optimal features from the normalized data, thereby analyzing the characteristics of attacks and reducing data dimensionality. In addition, this study introduces a hybrid detection model, which combines the Gated BiTCN and Temformer approaches. The Gated BiTCN captures the bidirectional semantic features of network traffic, while the Temformer extracts correlations among different sequences for precise cyber-attack detection. Further, the effectiveness of this proposed technique is measured by utilizing IoT attack datasets with distinct parameters and it attains a higher accuracy of 98.76 % during attack detection tasks. These results demonstrate that the proposed technique provides exceptional performance in cyber threat detection. Experimental validation shows that the model significantly contributes to cyber-attack detection and enhances the cybersecurity of IoT networks effectively. © 2025

关键词： Cyber attacks

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Optimization and multi-functional predictive performance of advanced superhydrophobic cementitious materials for water-resistant infrastructure

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Asian Journal of Civil Engineering 2025年第5期26卷 2023-2036页

作者： Upadhye, Sachin Bagde, Prafulla Sange, Sanjay R. Rokade, Achal Chandak, Mayuri A. Patil, Tejas R. Kakade, Niteen T. Shelke, Nilesh Department of Computer Science and Application School of Computer Science and Engineering Ramdeobaba University Maharashtra Nagpur 440013 India Department of Mathematics Shri Ramdeobaba College of Engineering and Management Ramdeobaba University Maharashtra Nagpur 440013 India Shah And Anchor Kutchhi Engineering College Mumbai University Maharashtra Mumbai 400088 India Department of Civil Engineering Priyadarshini College of Engineering Maharashtra Nagpur 440019 India Department of Civil Engineering Priyadarshini Bhagwati College of Engineering Maharashtra Nagpur 440024 India Department of Mechanical Engineering Tulsiramji Gaikwad Patil College of Engineering and Technology Maharashtra Nagpur 441108 India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed) University Maharashtra Pune 440008 India

The development of water-resistant infrastructure faces critical challenges due to water ingress, which compromises durability and structural integrity levels. Traditional approaches to hydrophobic concrete often fail to address long-term performance under dynamic environmental conditions, with limitations in durability, scalability, and multi-functionality. To overcome these shortcomings, this study introduces advanced superhydrophobic cementitious materials integrating innovative methodologies that simultaneously enhance hydrophobicity, durability, and functionality. Functionalized nanoparticles with long-chain alkyl silanes offer dual benefits of superior hydrophobicity (contact angle > 150°) and enhanced mechanical strength (> 50 MPa), addressing the challenge of balancing water repellency with structural properties. Gradient porosity via 3D printing enables hierarchical hydrophobic textures, reducing capillary absorption by 95% and extending durability in wet-dry cycles. Stimuli-responsive surface modifications embed microcapsules with hydrophobic agents that ensure self-healing, achieving up to 80% recovery in water repellency post-damage. Machine learning optimizes mix formulations, reducing experimental iterations by 70% while ensuring predictive accuracy (> 95%) for targeted performance. Multi-functional composites incorporating piezoelectric nanoparticles harvest ~ 10 µW/cm² energy while maintaining hydrophobicity, demonstrating potential for smart infrastructure applications. Freeze–thaw resistance is enhanced through cryo-protective agents, achieving > 300 cycles without degradation. The proposed methodologies validate their efficacy through a real-world application testing framework, ensuring scalability with minimal loss of water repellency (> 90%) and compressive strength retention (> 85%). This work not only offers groundbreaking solutions for water-resistant infrastructure but also paves the way for sustainable, resilient, and smart const

关键词： Gradient porosity Hydrophobic nanoparticles Self-healing cement Smart infrastructure Superhydrophobic concrete

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Utilization of hesitant and intuitionistic fuzzy sets (HFS-IFS) in computational intelligence for decision modeling

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International Journal of Information technology (Singapore) 2025年 1-8页

作者： Sharma, Suvarna Dangi, Dharmendra Dixit, Dheeraj Kumar Gupta, Rashmi Kumar, Jeetendra Bhagat, Amit Department of Computer Science and Applications Dr. Vishwanath Karad MITWorld Peace University Pune India Information Technology Indian Institute of Information Technology (IIIT) C/O- Maulana Azad National Institute of Technology (MANIT) Bhopal India Computer Science and Engineering Madhav Institute of Technology & Science (MITS) Madhya Pradesh Gwalior India Computer Science and Application Atal Bihari Vajpayee University Chhattisgarh Bilaspur India Computer Applications Maulana Azad National Institute of Technology Bhopal India

This paper focuses on the utilization of hesitant and intuitionistic fuzzy sets (HFS & IFS) in a computational intelligent approach, mainly for decision modelling under complex vague surroundings. Indeed, classical fuzzy sets can be powerful in some applications, but they may not describe the broad range of epistemic imprecision and non-stationary uncertainty involved in making decisions at any given time. HFS & IFS overcome it by providing the ability to quantify degrees of hesitation and membership uncertainty. HFS allows a decision maker to express the multiple reference degrees associated with an option and also show uncertainty about how to assign that precise level of degree or real value. On the other hand, IFS allows for more possibilities of representing ambiguity. In this research, we incorporate the proposed model into a computational intelligent approach for improving decision-making, particularly in the context of multi-criteria decision analysis (MCDA), resource allocation and task completion parameters. The results show that the proposed HFS-IFS method presents improved performance in terms of accuracy and uncertainty treatment across a series of case studies. © Bharati Vidyapeeth's Institute of computer applications and Management 2025.

关键词： Computational Intelligence Decision modeling Hesitant fuzzy set (HFS) Intuitionistic fuzzy set (IFS) MCDA

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Intelligent Malware Detection Using Sentence Transformer and Hybrid Deep Learning Architecture

Intelligent Malware Detection Using Sentence Transformer and...

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International Conference on Signal Processing and Communication (ICSC)

作者： Sanjeev Kumar Yudhishthira Sapru Sugandha Sapru B. Janet Cyber Security Technology Division Centre for Development of Advanced Computing Mohali India Indian Computer Emergency Response Team CERT-In Delhi India Department of Mathematics PGGC Panjab University Chandigarh India Department of Computer Application National Institute of Technology Tiruchirappalli India

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

The complex evasion techniques adopted by the malware creator make signature-based malware detection approaches unable to detect unknown malware attacks. This study proposes a new malware detection and classification approach with significantly less dependence on manual feature engineering. The bidirectional encoder representations from transformers (BERT) are used to model the opcode sequences of the malware file as the semantic behavior of malware. After that, the vectorized feature vector is fed to the hybrid one-dimensional convolution neural network (1D-CNN) and the gated recurrent unit (GRU) model for feature reduction and normalization before training a Softmax classifier. A hybrid learning architecture is designed with a custom 1D-CNN and a GRU model to take the strengths of both types of networks. The proposed scheme is evaluated using the Microsoft BIG dataset, consisting of 1625 assembly files belonging to 9 distinct malware families. The proposed method produced 95.77% accuracy even with the smaller dataset, which we believe will boost performance by adding a more diverse dataset. It uses the next-generation BERT language model, which gives a promising future area for malware defenders. This research presents malware detection without much reliance on manual feature engineering or signature-based detection, making it suitable for detecting unknown malware attacks.

关键词： Training Semantics Manuals Bidirectional control Transformers Feature extraction Malware Encoding Vectors Next generation networking

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Enhanced Electrocardiogram Data Compression and Classification Using 2D Convolutional Neural Networks

Enhanced Electrocardiogram Data Compression and Classificati...

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Emerging Systems and Intelligent Computing (ESIC), International Conference on

作者： Amrendra Singh Yadav Preeti Mehta Deepti Mishra Rabindra Kumar Barik Department of Computer Science and Engineering AVM-Indian Institute of Information Technology and Management Gwalior India Department of Computer Science and Engineering National Institutes of Technology Delhi Delhi India School of Economics and Commerce KIIT Deemed to be University Bhubaneshwar India School of Computer Application KIIT Deemed to be University Bhubaneshwar India

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522117

The task of electrocardiogram (ECG) classification is a combination of data compression and classification of signals based on the patient’s health. This paper introduces a novel technique for compressing electrocardiogram (ECG) images and utilizing these compressed images for accurate classification. We transform ECG signals into images and compress them to reduce their size while preserving classification accuracy significantly. Our approach employs a 2-dimensional Convolutional Neural Network (2-D CNN) model and achieves an impressive accuracy of 97% even after compressing the images by up to 76%. The proposed technique was tested on the widely-used MIT-BIH Arrhythmia Dataset, demonstrating its robustness and efficacy. Maintaining high classification accuracy despite significant image compression is crucial for efficient storage and transmission in resource-constrained environments. This method holds substantial potential for real-time monitoring and diagnosis in telemedicine applications, emphasizing the importance of our contribution to the field.

关键词： Accuracy Image coding Biological system modeling Data compression Transforms Electrocardiography Robustness Convolutional neural networks Optimization Batch normalization

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