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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Naveena, S.Sathiya Goyal, Prakhar Narmadha Verma, Devika A. Balaji, N.V. Rampal, Sourav Prince Shri Venkateshwara Padmavathy Engineering College Department of Business Administration Chennai127 India Quantum University Research Center Quantum University India School of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research for Business and Technology Punjab India

ISBN: (纸本)9798350370249

Aspect computing is quickly turning into a possible answer for augmenting and streamlining the overall performance of next era community services. Edge computing gives a disbursed computing architecture that allows computation and records storage competencies to are living closer to the edge network, rather than in, valuable records middle. This enables applications to enjoy faster reaction times and reduced latency, growing the rate of facts shipping. Side computing also presents greater scalability, simplifying the combination of IoT gadgets, permitting statistics streaming from more than one asset. As networks pass to 5G, area computing will offer the important flexibility and stability to deal with big amounts of statistics visitors securely and successfully. By leveraging the power of edge computing, corporations can create an agile and resilient service structure, imparting more advantageous safety and a variety of customizable abilities that permit them to capitalize on the success of the 5 G community. © 2024 IEEE.

关键词： Abilities Advantageous Capitalize Customizable Permit Streaming Success Variety

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Enhanced Detection and Classification of Chronic Kidney Disease Using a Recurrent Neural Network Model

Enhanced Detection and Classification of Chronic Kidney Dise...

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International Conference on Artificial Intelligence and Smart Environment, ICAISE 2024

作者： Saranya, K. Arularasan, A.N. Sowmya, O.V. Farhaoui, Yousef Dilli Babu, M. Gopalakrishnan, S. Department of Computer Science and Engineering Bannari Amman Institute of Technology Tamil Nadu Sathyamangalam India Department of Computer Science and Engineering B.S. Abdur Rahman Crescent Institute of Science and Technology Vandalur Tamil Nadu Chennai 600048 India Department of Computer Science and Engineering K.S.R.M. College of Engineering Andhra Pradesh Kadapa 516003 India T-IDMS Department of Computer Science Faculty of Sciences and Techniques Errachidia Moulay Ismail University of Meknes Meknes Morocco Department of Information Technology Panimalar Engineering College Tamil Nadu Chennai 600123 India Department of Information Technology Hindustan Institute of Technology and Science Kelambakkam Tamil Nadu Chennai 603103 India

ISBN: (纸本)9783031909207

CKD is a condition that highly requires early detection in providing interventions on time;it has a very significant influence on patient care outcomes. The current paper proposes a novel approach to CKD detection and classification by introducing an RNN model tailored to handle complexities within medical data. The models proposed were compared with a large amount of the traditional and advanced machine learning techniques, including Logistic Regression, SVM, Decision Tree, Random Forest, CNN, and DNN. The RNN model really did an incredible job, registering 98.5% of accuracy, precision, recall and F1, exceeding all existing methods. These results prove sufficient potential of the model in accurately and reliably identifying CKD cases, hence making it very effective for use in clinical diagnostics. Advanced deep learning algorithms come with great promise for various applications in medical science to improve the diagnosis and care of patients. Further research works based on complex machine learning models will be used to overcome other critical challenges of healthcare. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Chronic Kidney Disease (CKD) Classification Deep Learning Disease Detection Feature Selection

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Enhancing Crop Yield: A Deep Learning Approach to Detect Tomato Leaf Diseases

Enhancing Crop Yield: A Deep Learning Approach to Detect Tom...

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

作者： Shreedevi S Ronihal Sheba Selvam Kusuma P Information Science and Engineering BNM Institute of Technology Bangalore Artificial Intelligence and Machine Learning BNM Institute of Technology Bangalore Computer Science and Engineering BNM Institute of Technology Bangalore

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

Agriculture represents a vital pillar of the global economy, with crop production significantly influenced by a broad variety of Phyto pathological conditions, particularly plant leaf-diseases. The early and accurate detection of these diseases are paramount, as they have a direct impact on crop yield, which consequently influences the economic viability and market profitability of agricultural outputs. However, many farmers and agricultural practitioners often lack the specialized knowledge required for precise diagnosis of leaf diseases, resulting in suboptimal crop production and economic losses. Consequently, the development of robust and efficient disease detection methodologies is imperative for the sustainability and success of agricultural practices. This research investigates the application including sophisticated deep learning algorithms like VGG19 and CNN, to identify and classify diseases in tomato plant leaves. The developed model attains an accuracy of 99.17%, surpassing both VGG19 and CNN, with the goal of enhancing crop management strategies and optimizing overall agricultural productivity.

关键词： Deep learning Accuracy Profitability computer architecture Feature extraction Robustness Convolutional neural networks Sustainable development Crop yield Diseases

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Catalyzing Random Access at Physical Layer for Internet of Things:An Intelligence Enabled User Signature Code Acquisition Approach

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China Communications 2021年第10期18卷 181-192页

作者： Xiaojie Fang Xinyu Yin Xuejun Sha Jinghui Qiu Hongli Zhang School of Electronic and Information Engineering Harbin Institute of TechnologyHarbin 1500001China Science and Technology on Communication Networks Laboratory Shijiazhuang 050050China School of Computer Science and Technology Harbin Institute of TechnologyHarbin 150001China

Exploiting random access for the underlying connectivity provisioning has great potential to incorporate massive machine-type communication(MTC)devices in an Internet of Things(Io T)***,massive access attempts from versatile MTC devices may bring congestion to the IIo T network,thereby hindering service increasing of IIo T *** this paper,an intelligence enabled physical(PHY-)layer user signature code acquisition(USCA)algorithm is proposed to overcome the random access congestion problem with reduced signaling and control *** the proposed scheme,the detector aims at approximating the optimal observation on both active user detection and user data reception by iteratively learning and predicting the convergence of the user signature codes that are in *** crossentropy based low-complexity iterative updating rule is present to guarantee that the proposed USCA algorithm is computational feasible.A closed-form bit error rate(BER)performance analysis is carried out to show the efficiency of the proposed intelligence USCA *** results confirm that the proposed USCA algorithm provides an inherent tradeoff between performance and complexity and allows the detector achieves an approximate optimal performance with a reasonable computational complexity.

关键词： Internet of Things(IoT) artificial intelligence physical layer cross-entropy random access

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Recommendation System Based on Sparrow Search Optimization Algorithm

Recommendation System Based on Sparrow Search Optimization A...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Yamsani, Nagendar Sasikala, M. Radhika, A. Rajasekar, S.S. Samanvita, N. Sr University School of Computer Science and Artificial Intelligence Warangal India Sona College of Technology Department of Information Technology Salem India Velammal College of Engineering and Technology Eee Madurai India Bannari Amman Institute of Technology Department of Computer Technology Sathyamangalam India Nitte Meenakshi Institute of Technology Department of Electrical & Electronics Engineering Bengaluru India

ISBN: (纸本)9798350313413

The recommendation system is an effective tool used for filtering the huge number of informations and that is highly spread due to the exchanging habits of personalization trends, system users and appearing internet access. Although the current recommender systems are famous for proving the accurate recommendations and it can suffer from several limitations such as sparsity, scalability, cold-start, etc. Because of the various existing techniques, the choice of techniques is a difficult task while creating the applications based on recommender systems. Additionally, every strategy appears with its collection of advantages, limitations and features which increases much more questions, that must be identified. In this paper, the proposed Sparrow Search Optimization Algorithm for recommender system using four various datasets named as MovieLens 100 k, MovieLens 1 million, Jester and Epinion. The performance of the proposed algorithm is estimated by applying parameters like Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Precision and Recall with K-means clustering algorithms. The obtained result shows that this proposed methodology provides high recommendation systems. © 2023 IEEE.

关键词： Clustering Cold-start Collaborative filtering Recommender System Sparrow Search Algorithm

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Utilizing Blockchain and Deep Learning for Decentralized Discovery of Deceptive Practices in Healthcare Insurance 3

Utilizing Blockchain and Deep Learning for Decentralized Dis...

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3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Shaik, Nazeer Kar, Nihar Ranjan Thankachan, Blessy Pathak, Amit Kumar Singh, Jagendra Gupta, Shelly SRIT-Autonomous Department of Computer Science Engineering Anantapur India School of Pharmacy University of Technology and Management Gopalpur India Jecrc University Department of Computer Applications Jaipur India Raj Kumar Goel Institute of Technology Department of Information Technology Ghaziabad India School of Computer Science Engineering and Technology Bennett University Greater Noida India Ghaziabad India

ISBN: (纸本)9798350342338

To address the complex issues of detecting fraudulent practices in healthcare insurance, this research employs sophisticated machine learning, especially the Long Short-Term Memory (LSTM) model, to provide a complete framework for reliable fraud detection. The study meticulously examines performance metrics such as accuracy, precision, recall, and the F1 score by employing and evaluating the LSTM model across two distinct datasets-Dataset A (198810 samples) and Dataset B (434319 samples)-illuminating the model's capacity to detect fraudulent activities while minimizing misclassifications. The evaluation process, as shown by confusion matrices displayed as percentages, reveals the model's strengths and points up areas for improvement. This study makes an important contribution to the field of fraud detection by providing practical insights to strengthen healthcare insurance against misleading practices. This research proclaimers a paradigm shift by combining innovative methodologies, extensive dataset curation, and stringent evaluation, ushering in increased security, transparency, and efficacy in healthcare insurance fraud detection, ultimately fostering a future of resilient and precise fraud detection mechanisms. © 2023 IEEE.

关键词： IoT machine learning performance metrics security attacks

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Packaging 4.0: Artificial Intelligence and Machine Learning Applications in the Food Packaging Industry

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Current Food science and technology Reports 2025年第1期3卷 1-14页

作者： Kumar, Lokesh Dutt, Richa Gaikwad, Kirtiraj K. Department of Paper Technology Indian Institute of Technology Roorkee Roorkee Uttarakhand India Department of Computer and Information Science and Engineering University of Florida Gainesville USA

Artificial intelligence (AI) and machine learning (ML) techniques are widely used in biomedical research, engineering research, finance, economics, and food packaging technology. This review highlights various applications of AI and ML techniques in food packaging industries. Global packaging conglomerates are utilizing AI and ML-trained models to remain competitive in the packaging market and improve the experience of packaging resulting in enhanced consumer relationships. Manual packaging and supply chain optimization (SCOp) operations in packaging industries are time-consuming, inefficient, and non-standardized. Adapting AI, ML, and deep learning techniques for packaging and SCOps operations has significantly enhanced the overall efficiency of packaging industries. This review highlights the key takeaways, providing insight into AI, ML, and deep learning techniques adapted for packaging operations and SCOp. AI and ML pervade all packaging domains, and supply chain operations indicate transformative system changes. The discussion reflects AI, ML, and deep learning as potential tools to change traditional quality inspection, packaging, label design, fraud detection, and SCOp processes. AI and ML-based solutions introduced by Coca-Cola and Amazon have been thoroughly reviewed. Finally, this paper concluded with challenges and future recommendations for integrating advanced AI and ML-based tools into packaging and supply chain optimization operations.

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Detection and Diagnosis of Atopic Dermatitis Using Deep Learning Network 1

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Proceedings of the International Conference on Intelligent Systems and Network, ICISN 2023

作者： Nguyen, Anh-Minh Vu, Van-Hieu Trinh, Thanh-Binh High School for Foreign Languages Hanoi National University Hanoi Viet Nam Institute of Information Technology Vietnam Academy of Science and Technology Hanoi Viet Nam Faculty of Computer Science Phenikaa University Hanoi Viet Nam

ISBN: (数字)9789819947256

ISBN: (纸本)9789819947249

This paper proposes a method to diagnose atopic dermatitis based on deep learning network by analyzing image data of infected skin areas. We use deep learning network to analyze the layers and get the suitable layer for diseased and non-diseased images. These images are further feature extracted through HOG feature extraction and SVM classifier to classify disease and non-disease. The proposed method proves to be effective in precision and recall, that can be considered as an adjunct to traditional diagnostic methods, and the results obtained are equivalent to that of a diagnostician while limiting the heterogeneity between the predictors. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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Extraction of gravel characteristics and spatial inversion for ecological restoration monitoring in the Northern Tibetan Plateau

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Journal of Mountain science 2025年第2期22卷 556-574页

作者： KONG Bo YU Huan QIU Xia HU Wenkai HE Bing GUAN Xudong Institute of Mountain Hazards and Environment Chinese Academy of SciencesChengdu 610041China College of Geography and Planning Chengdu University of TechnologyChengdu 610059China Tibet Geological Environment Monitoring Center Lhasa 850000China School of Computer Science Chengdu University of Information TechnologyChengdu 610225China Sichuan Real Estate Registration Center(Sichuan Provincial Land and Cadastral Affairs Center) Chengdu 610072China

Previous studies have often focused on monitoring grassland growth as the primary target of remote sensing investigations on grassland ecological restoration in the northern Tibetan Plateau,overlooking the crucial role played by gravel in the ecological restoration of these *** study utilizes supervised classification and segmentation techniques based on machine learning to extract gravel morphology profiles from field-sampled plot images and calculate their characteristic *** a multivariate linear approach combined with Principal Component Analysis(PCA),a model for inferring gravel characteristic parameters is *** features,particle size characteristics,and spatial distribution patterns of gravel are *** reveal that gravel predominantly exhibit sub-rounded shapes,with 80%classified as fine *** coefficients of determination(R2)between gravel particle size and coverage,perimeter,and area are 0.444,0.724,and 0.557,respectively,indicating linear *** cumulative contribution rate of the top five remote sensing factors is 95.44%,with the first geological factor contributing 77.64%,collectively reflecting the primary information of the 20 factors *** shows that areas with larger gravel particle sizes correspond to increased perimeter and *** in the Nagqu Prefecture of northern Tibet have a particle size range of 4-8 mm,primarily comprising fine gravel which accounts for 94.61%.These findings provide a scientific basis for extracting gravel characteristic parameters and understanding their spatial distribution variations in the northern Tibetan Plateau.

关键词： Gravel characteristics parameters Northern Tibetan Plateau Gravel outline extraction Remote sensing inversion Grassland degradation

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Potato Plant Leaf Disease Detection and Recognition Using R-CNN Model 3

Potato Plant Leaf Disease Detection and Recognition Using R-...

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3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Balakrishnan, Minu Raja, M. Kumar, P.Vinoth Subramaniam, Suvitha Latha, R. Sri Eshwar College of Engineering Department of Information Technology Coimbatore India Cmr Institute of Technology Department of Computer Science and Engineering Bengaluru India Chennai Institute of Technology Depatment of Mechatronics Chennai India Kpr Institute of Engineering and Technology Department of English Coimbatore India Chennai India

ISBN: (纸本)9798350322842

A smart farming system that makes use of the appropriate infrastructure is an example of a new technology that aids in the improvement of the quality and quantity of the country's agricultural goods, such as potatoes. Diseases cannot be prevented since developing potato plants requires consideration of factors such as the environment, soil, and quantity of sunlight. Deep learning has resulted in recent advances in computer vision. These advancements have enabled Potato to utilize a camera to diagnose disease. This study developed an effective new method for detecting infections in potato plants. Yukon Gold, a kind of potato, served as the trial subject. Specifically, Alternaria, Blackleg, and Target Spot were targeted for detection using this method. Using 5,615 images of diseased and healthy Potato plant leaves taken in controlled environments, deep CNN is trained to identify between the presence and absence of three illnesses. To identify the Potato illnesses affecting the tracked plants, the system used a CNN. Anomaly detection using the F-RCNN-trained model achieved a confidence score of 80%, while illness identification using the Transfer Learning model achieved an accuracy of 97.85%. The effectiveness of the automated image-capturing system for diagnosing diseases in Potato plant leaves was determined to be 93.33%. © 2023 IEEE.

关键词： Convolutional neural networks

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