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

作者： Jashandeep Singh Gursharan Singh School of Computer Science and Engineering Lovely Professional University Phagwara India Department of Computer Science and Engineering Lovely Professional University Phagwara India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Strawberry leaf scorch presents major risks to fruit yields through its harmful effects on plant health so early detection methods are vital for growers. This research analyzes how different Convolutional Neural Networks classify strawberry leaf diseases while fixing problems found in earlier studies about speed and diagnosis precision. The proposed research evaluated five CNN models namely AlexNet, DenseNet, ResNet, MobileNet, and EfficientNet through implementation on a total of 6146 images that showcased both healthy and scorch-affected strawberry leaves. The proposed research optimized feature extraction techniques and added model comparison to find the most effective configuration. EfficientNet achieved higher accuracy and precision than all test models when detecting diseases in samples. The strong disease identification abilities of DenseNet and MobileNet show promise for developing better ways to manage agricultural health problems. This study expands precision agriculture methods while helping farmers sustain their operations through modern machine learning tools.

关键词： Training Productivity Precision agriculture Accuracy User interfaces Feature extraction Real-time systems Convolutional neural networks Internet of Things Diseases

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Real-Time Elderly Fall Detection as Ambient Intelligence in Old Age Homes

Real-Time Elderly Fall Detection as Ambient Intelligence in ...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Laxmi Shabadi Chaitra M Monik L Pavan S G R K Deekshith Laxmi Narayan Department of Computer Science and Engineering SJB Institute of Technology Bangalore India Department of Computer Science and Engineering BNM Institute of Technology Bangalore India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

The rapid expansion of autonomous technologies, the rise of computer vision, and edge computing present exciting opportunities in healthcare monitoring systems. Fall prevention is especially important for the elderly because falls from this age group often result in fatalities and serious injuries. Fall detection devices that can quickly recognize falls and alert emergency services have become more and more popular as a result. The primary goal of the project is to increase elderly home safety by implementing an ambient intelligence-based automated emergency recognition system. We present a unique method for fall posture detection utilizing an intelligence surveillance camera and a class of efficient models called MobileNets for mobile and embedded vision applications. A real-time embedded solution persuaded from “edge AI” is implemented using the state-of-the-art Object detection: single shot detector (SSD) Mobile Net V2 and Internet of things embedded GPU platform NVIDIA’s Jetson Nano.

关键词： Prevention and mitigation Surveillance Object detection Medical services Real-time systems Ambient intelligence Internet of Things Older adults Fall detection Injuries

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Machine Learning Approaches for Evaluation of Chronic Kidney Disease 6th

Machine Learning Approaches for Evaluation of Chronic Kidney...

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6th International Conference on Advanced Informatics for Computing Research, ICAICR 2023

作者： Sohal, Harwinder Singh Singla, Jimmy Kaur, Gurpreet Kaur, Harpreet Department of Computer Science and Engineering CT University Punjab Ludhiana India Department of Computer Science and Engineering Chitkara University Punjab Rajpura India

ISBN: (纸本)9783031840616

In today’s era medical information and disease detection as well as health care depends greatly on machine learning. Modern machine learning algorithm’s make an advantage in detecting the big health hazards like Brain Tumors, Cancer, Covid 19, Diabetes, Kidney Diseases etc. Kidneys are the most vital organs in human body, if any issue occurs in kidney it can have harmful effect on body. Kidney sickness comes in two different forms: Acute Kidney Disease and Chronic (long-term) Kidney Disease and this must be emphasized. Acute kidney disease is the most prevalent kind of kidney disease. Kidney function gradually declines over years, which is the hallmark of kidney failure. Chronic Kidney Disease is a dangerous condition that can be extremely fatal if not identified and treated at its early stages. Thus, the primary objective of the present work is to use machine learning techniques to accurately forecast Chronic Kidney Disease. For this, clinical dataset of 400 patients with 25 attributes related to this disease is obtained from the well-known UCI ML repository. Since the dataset is large and potentially expensive modeling costs, information gain is also utilized in the feature selection process. However, dataset has numerous of missing variables. To obtain a clean dataset, pre-processing is carried out. After pre-processing, three different machine learning approaches Decision Tree, Logistic Regression and Random Forest are applied on dataset. Then result of each three approaches are compared using a range of performance indicators. According to the experimental findings Decision Tree, Logistic Regression and Random Forest achieved an accuracy of 97.5%,97.5% and 98.75% respectively in disease prediction. Regarding the ability to forecast Chronic Kidney Disease, Random Forest is thought to be superior than others. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Logistic regression

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SA-ResNet:An Intrusion Detection Method Based on Spatial Attention Mechanism and Residual Neural Network Fusion

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computers, Materials & Continua 2025年第5期83卷 3335-3350页

作者： Zengyu Cai Yuming Dai Jianwei Zhang Yuan Feng School of Computer Science and Technology Zhengzhou University of Light IndustryZhengzhou450066China College of Software Engineering Zhengzhou University of Light IndustryZhengzhou450066China Faculty of Information Engineering Xuchang Vocational Technical CollegeXuchang461000China School of Electronic Information Zhengzhou University of Light IndustryZhengzhou450066China

The rapid development and widespread adoption of Internet technology have significantly increased Internet traffic,highlighting the growing importance of network *** Detection Systems(IDS)are essential for safeguarding network *** address the low accuracy of existing intrusion detection models in identifying network attacks,this paper proposes an intrusion detection method based on the fusion of Spatial Attention mechanism and Residual Neural Network(SA-ResNet).Utilizing residual connections can effectively capture local features in the data;by introducing a spatial attention mechanism,the global dependency relationships of intrusion features can be extracted,enhancing the intrusion recognition model’s focus on the global features of intrusions,and effectively improving the accuracy of intrusion *** proposed model in this paper was experimentally verified on theNSL-KDD *** experimental results showthat the intrusion recognition accuracy of the intrusion detection method based on SA-ResNet has reached 99.86%,and its overall accuracy is 0.41% higher than that of traditional Convolutional Neural Network(CNN)models.

关键词： Intrusion detection deep learning residual neural network spatial attention mechanism

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Bone Fracture Classification in X-ray Images: A Deep Learning Approach Leveraging Transfer Learning

Bone Fracture Classification in X-ray Images: A Deep Learnin...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Md. Sabbir Ahammed Md. Musfiqur Rahman Mridha Md. Taufiq Khan Md. Jamil Chaudhary Md. Fatin Nibbrash Nakib Sakib Imtiaz Md. Ashikuzzaman Ashik Dept. of Computer Science & Engineering Varendra University Rajshahi Bangladesh Dept. of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Bone fracture is a very common medical issue faced by medical practitioners, and fracture detection is quite crucial for treatment. Till now, the most popular diagnosing method for bone fracture is X-ray imaging. However, in many cases, an X-ray image of a fractured bone is prone to human error. Advancements in artificial intelligence (AI) have demonstrated the potential to achieve high accuracy in tasks like X-ray image classification of fractured bone. In this work, we have incorporated a deep learning-based approach to facilitate the diagnosis of bone fracture. We have utilized different pre-trained state-of-the-art (SOTA) convolutional neural networks (CNN), namely, DenseNet, VGG, ResNet, Xception, and EfficientNet as feature extractors, to extract higher-level feature space representations of X-ray images. We have incorporated multiple classifiers, namely Logistic Regression, Random Forest, Linear Regression, XGBoost, and a custom feed-forward network (FFN) to discriminate among the higher-level representations extracted by the aforementioned pre-trained feature extractors. Among the several feature extractor and classifier combinations we have experimented with, DenseNet169 combined with the custom FFN produced the best results, reporting an overall accuracy of 99.48%, Receiver Operating Characteristic Area Under the Curve (ROC-AUC) score of 99.99%, precision of 99.48%, recall of 99.48%, and F1 score of 99.48%. The results demonstrate that combining an appropriate pretrained SOTA CNN model and a classifier can achieve high classification accuracy in the bone-fracture X-ray image classification task. Such a method previews a promising avenue in helping medical practitioners to achieve better diagnostic performance with patient benefit.

关键词： Deep learning Accuracy Transfer learning Feature extraction Bones Convolutional neural networks X-ray imaging Medical diagnostic imaging Random forests Image classification

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An enhanced LSTM-CRF based approach for Knowledge Integration based Sentiment Analysis

An enhanced LSTM-CRF based approach for Knowledge Integratio...

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International Symposium on Advanced Computing and Communication (ISACC)

作者： Allam Balaram M. SilpaRaj K. Likhitha Sree V. Pramodh Kumar D. Vinay Kumar K. Venu Kumar Reddy Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Department of Computer Science and Engineering (Cyber Security) CVR College of Engineering Hyderabad India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Sentiment analysis is a key element for expert reviews but is at a loss in terms of detection of sarcasm and irony, which distort the actual sentiments. This paper proposes an approach that will fuse syntactic and contextual knowledge to respond to these requirements. Steps for preprocessing include tokenization, stop word removal, lemmatization, followed by feature extraction using BERT embedding. In addressing high-dimensional output, the Relief algorithm is then used to extract the relevant features. The classification model utilizes a combination of Deep Learning for sequence LSTM networks and Maximum entropy methods, i.e., Conditional Random Fields (CRF), and enables sequential dependency learning to give better accuracy. This application of data pertaining to time and context can effectively address subtle nuances such as sarcasm and irony and classify the text as positive, negative, or neutral. It brings several advancements with respect to traditional models, thus improving the understanding of emotion in text.

关键词： Sentiment analysis Accuracy Reviews Viterbi algorithm Syntactics Feature extraction Conditional random fields Tokenization Classification algorithms Long short term memory

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Securing ML Models on Websites: Face Recognition and Spoof Detection via IPFS Blockchain

Securing ML Models on Websites: Face Recognition and Spoof D...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Ramakanth Kumar P Pranav V Jambur Nidhi Vinayak Kulkarni Nikita S Raj Kapini Niranjan R N Noel S Mathew Piyush Kheria Computer Science & Engineering R. V. College of Engineering Bengaluru India Computer Science & Engineering Data Science R. V. College of Engineering Bengaluru India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

This paper presents a secure framework for deploying ML models client-side, addressing challenges like unauthorized access, reverse engineering, and spoofing. The proposed solution combines encryption, obfuscation, and integrity verification, using JIT encryption to protect models by decrypting only as needed, minimizing exposure. Cryptographic signatures and MediaPipe ensure secure transmission and model integrity, while Hoffmann compression reduces the model size to 5-7 MB, optimizing load times without sacrificing functionality. Blockchain-based IPFS secures data storage, utilizing content-addressable storage to prevent reverse engineering and ensure data integrity. Efficient caching strategies, like HTTP caching and browser requests, enhance accessibility and reduce latency. Liveness detection verifies the user’s face by capturing 100-200 frames in 2 seconds for spoofing protection. Dynamic key encryption adapts with each transaction, countering replay attacks and reinforcing model security. The framework leverages Kubernetes for scalable, reliable deployment in large-scale applications. Edge AI enables sensitive biometric data processing on-device, maintaining high accuracy and low latency, which is ideal for real-time web and mobile uses. This comprehensive, scalable approach combines encryption, blockchain, and AI to secure client-side ML models, addressing evolving security needs in modern applications.

关键词： Biological system modeling Heuristic algorithms Face recognition Computational modeling Reverse engineering Edge AI Encryption Blockchains Protection Load modeling

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Radio Map Estimation via Latent-Domain Plug-and-Play Denoisers

Radio Map Estimation via Latent-Domain Plug-and-Play Denoise...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Xu, Le Cheng, Lei Chen, Junting Pu, Wenqiang Fu, Xiao School of Electrical Engineering and Computer Science Oregon State University Corvallis United States College of Information Science and Electronic Engineering Zhejiang University Hangzhou China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China

ISBN: (纸本)9798350368741

Radio map estimation (RME) aims to construct a map of radio strength across multiple domains (e.g., space and frequency) from limited measurements. Data-driven deep neural model-based RME showed promising performance, yet requiring excessive training resources. This work puts forth an RME approach that can effectively incorporate learned information without training over radio map data. Our idea is to employ the plug-and-play (PnP) denoising scheme from computational imaging. The PnP framework allows incorporating denoisers trained over natural images to handle other types of data, e.g., ocean sound fields and medical images, due to the similarity of their denoising processes. Conventional PnP methods mostly use the learned denoisers in the data domain. Instead, the proposed approach applies PnP in the latent domain through a tailored algorithm design and spatial-spectral factorization of radio maps. This way, the proposed RME method exhibits enhanced scalability and noise robustness. Simulations are used to illustrate the effectiveness of the proposed approach. © 2025 IEEE.

关键词： plug-and-play algorithm design radio map estimation Spectrum cartography

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Capstone Projects as Ventures to New Knowledge 20th

Capstone Projects as Ventures to New Knowledge

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20th International Conference on Foundations of computer science, FCS 2024, and 20th International Conference on Frontiers in Education, FECS 2024, held as part of the World Congress in computer science, computer engineering and Applied Computing, CSCE 2024

作者： Wang, Hong Langenderfer, Robert Allen Computer Science and Engineering Technology The University of Toledo Toledo United States Computer Science and Engineering Technology The University of Toledo Toledo United States

ISBN: (纸本)9783031859298

The capstone project serves as a culmination of a computer science student’s academic journey, encapsulating their learning across the curriculum and culminating in the creation of robust software systems relevant to real-world applications. Throughout the project’s development, students not only refine their technical abilities but also cultivate essential project management and teamwork skills. This paper presents an innovative pedagogical approach to teaching the capstone course, emphasizing the integration of new techniques and fostering a culture of lifelong learning. Through various illustrative examples, it demonstrates how students acquire proficiency in new database systems, programming languages, and artificial intelligence (AI) technologies through their engagement in capstone project development. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Project management

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DISTANCE BETWEEN SPECIES BY CONFUSION OPERATORS OF MULTI-CLASS CLASSIFIERS

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2025年第1期2025卷 171-182页

作者： Tarbă, Nicolae Irimescu, Ionela N. Pleavă, Ana M. Scarlat, Eugen N. Mihăilescu, Mona Doctoral School Computer Science and Engineering Department Faculty of Automatic Control and Computers National University of Science and Technology POLITEHNICA Bucharest Romania Applied Sciences Doctoral School National University of Science and Technology POLITEHNICA Bucharest Romania CAMPUS Research Center National University of Science and Technology POLITEHNICA Bucharest Romania Physics Dept National University of Science and Technology POLITEHNICA Bucharest Romania Physics Dept Research Center for Applied Sciences in Engineering National University of Science and Technology POLITEHNICA Bucharest Romania

We introduce a method to evaluate the similarities between classes of objects based on the confusion matrices coming from the multi-class machine learning (ML) predictors that operate in the vector space generated by the classes. Operation in the confusion domain allows us to use the distance as evaluation metric of the closeness of the relevant properties carried by the classes. We applied the method to semiquantitatively evaluate the effects of X-ray and carbon ion radiation on cultured SW1353 chondrosarcoma cells with respect to the non-irradiated reference. We prove that it is possible to draw useful information on the strength of cells nuclei changes. © 2025, Politechnica University of Bucharest. All rights reserved.

关键词： Radiation effects

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