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Developing an Integrated IoT Cloud Based Predictive Conservation Model for Asset Management in Industry 4.0

Journal of Social Computing 2023年第2期4卷 139-149页

作者： Karnam Shanmugam Kachhti Satyam Thimma Reddy Sreenivasula Reddy the Department of Computer Applications Annamacharya Institute of Technology and SciencesKarakambadiTirupathi 517520India the Department of Computer Science and Engineering Annamacharya Institute of Technology and SciencesKarakambadiTirupathi 517520India

With the advent of Industry 4.0(I4.0),predictive maintenance(PdM)methods have been widely adopted by businesses to deal with the condition of their *** the help of I4.0,digital transformation,information techniques,computerised control,and communication networks,large amounts of data on operational and process conditions can be collected from multiple pieces of equipment and used to make an automated fault detection and diagnosis,all with the goal of reducing unscheduled maintenance,improving component utilisation,and lengthening the lifespan of the *** this paper,we use smart approaches to create a PdM planning *** five key steps of the created approach are as follows:(1)cleaning the data,(2)normalising the data,(3)selecting the best features,(4)making a decision about the prediction network,and(5)producing a *** the outset,PdM-related data undergo data cleaning and normalisation to get everything in order and within some kind of *** next step is to execute optimal feature selection in order to eliminate unnecessary *** research presents the golden search optimization(GSO)algorithm,a powerful population-based optimization technique for efficient feature *** first phase of GSO is to produce a set of possible solutions or objects at *** objects will then interact with one another using a straightforward mathematical model to find the best feasible *** to the wide range over which the prediction values fall,machine learning and deep learning confront challenges in providing reliable *** is why we recommend a multilayer hybrid convolution neural network(MLH-CNN).While conceptually similar to VGGNet,this approach uses fewer parameters while maintaining or improving classification correctness by adjusting the amount of network modules and *** projected perfect is evaluated on two datasets to show that it can accurately predict the future state of components for upkeep prepara

关键词： Industry 4.0 predictive maintenance golden search optimization multilayer hybrid convolution neural network data cleaning

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Mitigating Denial Based Service Attacks in Heterogeneous Sensor Networks: Strategies and Solutions 11

Mitigating Denial Based Service Attacks in Heterogeneous Sen...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Desai, Jaynesh H Bagle, Neha S Rami, Khyati Verma, Shanti Bhagwan Mahavir University Department of Computer Applications Gujarat Surat India Sal Institute of Technology & Engineering Research Department of Computer Science Gujarat India L J University Department of Computer Applications Gujarat Ahmedabad India

ISBN: (纸本)9798350350357

With the development of heterogeneous sensory networks in various applications of domains for ensuring the security against denial based service (DoS) attacks will become a paramount. In this research study, a proposal of a novel system named SecureSenseGuard(SSG) for effectively detecting DoS attacks in heterogeneous sensor networks. SecureSenseGuard(SSG) leveraging the Decision Tree (DT) algorithm augmented with Recursive type Features Elimination (RFE) for achievinga robust and efficient attack detection. The proposed system may operate by first Collection of data from diverged sensors within the network. The data, representing various network attributes, are then preprocessed and fed into the DT algorithm for classifications process. RFE will be employed as a feature selection techniques to enhance the performance of DT algorithm by iterative process for removing less relevant features, thus improving in detection accuracy and reducing computational overhead. The proposed system SecureSenseGuard(SSG) will offer several advantages over existing approaches. By employing DT, it will provide interpretable and transparent decision rules for facilitating understanding and trust in the detection process. Moreover, the integration of RFE will enhance the system's scalability and adaptability for dynamic network environments. To evaluate the effectiveness of SecureSenseGuard(SSG) an extensive experiment may be conducted by using various ranges of datasets and synthetic attack scenarios. The Results demonstrate that SecureSenseGuard(SSG) achieved high detection rates. while maintaining a low false alarm rate, outperforming traditional approaches. Overall, SecureSenseGuard(SSG) may present a promising solution in mitigating DoS attacks in heterogeneous sensory networks by offering a balance between detection accuracy, interpretability, and computational efficiency. © 2024 IEEE.

关键词： Heterogeneous networks

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Hate Speech Analysis using Supervised Machine Learning Techniques

Hate Speech Analysis using Supervised Machine Learning Techn...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Pyingkodi, M. Thenmozhi, K. Chitra, K. Karthikeyan, M. Pradeep, M. Suriya, Prakash T D Viswanathan, T.J. Kongu Engineering College Department of Computer Applications Erode India Kristu Jayanti College Department of Computer Science Bengaluru India Computer Applications Kongu Engineering College Erode India

ISBN: (纸本)9798350348217

Hate speech is a serious issue that can have harmful consequences for individuals and groups. In an effort to combat this issue, automated methods for identifying and categorising hate speech in text and other media have been developed using machine learning and deep learning techniques. These methods involve training machine learning algorithms on large datasets of labelled text, audio or visual data, and then using these algorithms to identify and classify new, unseen examples of hate speech. Machine learning approaches can be effective at detecting and classifying hate speech, but they also have limitations and can be prone to errors, particularly if the training data is biased or if the algorithms are not carefully designed and evaluated. It is important to thoughtfully examine the ethical and social consequences of using machine learning to identify hate speech and to ensure that these methods are transparent, fair, and accountable. © 2023 IEEE.

关键词： Support vector machines

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Natural Language Processing (NLP) based Phonetic Insights for Improving Voice Recognition and Synthesis 4

Natural Language Processing (NLP) based Phonetic Insights fo...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Priyadharshini, N. Subbulakshmi, N. Kalasalingam Academy of Research and Education Department of Computer Applications Krishnankoil India Kalasalingam Academy of Research and Education Department of Computer Computer Science and Engineering Krishnankoil India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540364

Voice analysis in Natural Language Processing (NLP) involves the processing of spoken language to develop applications such as speech recognition, synthesis, and speaker identification. This survey explores the phonetic intricacies of Tamil and other Dravidian languages, examining their impact on NLP. The extensive phonetic inventory of the Tamil Language is characterized by its complex vowel system, retroflex consonants, and the distinction between aspirated and unaspirated stops, which presents unique challenges and opportunities for computational linguistics. Phonetics helps to process the human language both accurately and naturally and it works as a bridge between the spoken and written communication forms. This paper emphasizes the necessity of developing specialized phonetic modeling techniques and robust algorithms. Additionally, it highlights the importance of creating comprehensive phonetic datasets and leveraging advancements in machine learning and deep learning to improve NLP tasks such as speech recognition and synthesis. A brief comparison on algorithms and applications associated with voice-assisted technologies. By advocating for interdisciplinary collaboration among linguists, computer scientists, and language technologists, this survey aims to inspire further research and innovation in processing Tamil and other Dravidian languages, ultimately contributing to more inclusive and efficient NLP systems for these linguistically rich languages. © 2024 IEEE.

关键词： Deep learning

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Optimizing Retinal Fundus Images for the Automated Diabetic Retinopathy Diagnosis 2

Optimizing Retinal Fundus Images for the Automated Diabetic ...

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2nd Jordanian International Biomedical engineering Conference, JIBEC 2024

作者： Awad, Samer Gharaibeh, Yazan Obeid, Abeer Awwad, Sari The Hashemite University Department of Biomedical Engineering Zarqa Jordan The Hashemite University Department of Computer Science and Applications Zarqa Jordan

ISBN: (纸本)9798331539542

Diabetic retinopathy (DR) is a common eye condition arising from the complications associated with diabetes. The diagnosis of DR is traditionally carried out by experienced clinicians who screen fundus images to identify numerous small features. This process is challenging, time-consuming, and susceptible to errors. Various studies have successfully utilized different machine learning techniques for the automated diagnosis of DR. The performance of DR diagnosis can be influenced by the preprocessing of the images. In this study, the impact of unifying image resolution and field of view on the performance of machine learning-based automated DR diagnosis was evaluated. This approach ensured that the sizes of the various internal parts of the retina were effectively standardized across different images. Over 32,000 images from two different datasets obtained from *** were utilized. Two distinct classification experiments were conducted on the two datasets using an Inception convolutional neural network, both with and without the proposed method. The proposed method resulted in accuracies of 78.8% and 96.8% for dataset 1 and dataset 2, respectively, compared to accuracies of 78.1% and 95.9% without this method. © 2024 IEEE.

关键词： Adversarial machine learning

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Detection of Acute Lymphoblastic Leukemia Using a Novel Bone Marrow Image Segmentation

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Tsinghua science and Technology 2025年第2期30卷 610-623页

作者： M.Anline Rejula Ben M Jebin Ravi Selvakumar S.Amutha Eberlein George Department of Computer Applications-PG Scott Christian College(Autonomous)Nagercoil 629003India Department of Computer Science Malankara Catholic CollegeMariagiri 629153India School of Computer Science and Engineering Vellore Institute of TechnologyChennai 600127India Department of Pediatrics Government Medical College and HospitalGovernment of Tamil NaduNamakkal 637001India

In our study,we present a novel method for automating the segmentation and classification of bone marrow images to distinguish between normal and Acute Lymphoblastic Leukaemia(ALL).Built upon existing segmentation techniques,our approach enhances the dual threshold segmentation process,optimizing the isolation of nucleus and cytoplasm *** is achieved by adapting threshold values based on image characteristics,resulting in superior segmentation outcomes compared to previous *** address challenges,such as noise and incomplete white blood cells,we employ mathematical morphology and median filtering *** methods effectively denoise the images and remove incomplete cells,leading to cleaner and more precise ***,we propose a unique feature extraction method using a hybrid discrete wavelet transform,capturing both spatial and frequency *** allows for the extraction of highly discriminative features from segmented images,enhancing the reliability of *** classification purposes,we utilize an improved Adaptive Neuro-Fuzzy Inference System(ANFIS)that leverages the extracted *** enhanced classification algorithm surpasses traditional methods,ensuring accurate identification of acute lymphoblastic *** innovation lies in the comprehensive integration of segmentation techniques,advanced denoising methods,novel feature extraction,and improved *** extensive evaluation on bone marrow samples from the Acute Lymphoblastic Leukemia Image DataBase(ALL-IDB)for Image Processing database using MATLAB 10.0,our method demonstrates outstanding classification *** segmentation accuracy for various cell types,including Band cells(96%),Metamyelocyte(99%),Myeloblast(96%),***(97%),***(97%),and Neutrophil cells(98%),further underscores the potential of our approach as a high-quality tool for ALL diagnosis.

关键词： Adaptive Neuro-Fuzzy Inference System(ANFIS) blood cells segmentation leukemia classification

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Impact of nutrients in food quality and safety by machine learning classifier using internet of things

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International Journal of Information Technology (Singapore) 2024年第5期16卷 2803-2812页

作者： Balamurugan, S. Gurumoorthi, E. Devi, P.P. Maruthamuthu, R. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & amp Department of IT CMR College of Engineering and Technology Telangana Hyderabad India Department of Computer Applications Madanapalle Institute of Technology and Science AP Madanapalle India

The traditional Food Supply Chain (FSC) is challenged by a number of issues, including uncertainty, security, expense, complexity, and quality worries. A precise supply chain is required to address these problems. Managing the supply chain to distribute high-quality goods is a difficult task in the modern food sector. The efficient Tree Augmented Naive Bayes linked with FSC utilizing Internet of Things (IoT) is proposed in this paper to enable tracking, tracing, and managing the full food supply chain process, including supplier, exporter, and consumers. This paper's goals are to evaluate the food safety and create the best possible chronological data for the analysis of potential future assumptions. It also intends to connect the producer and the buyer by delivering high-quality food goods while using IoT technology to transport food from the producers to the customers. IoT-based food supply chain traceability is used to successfully transact data with ambiguous, unreliable, and inadequate data. In order to provide secure backdrop and food safety for FSC process utilizing IoT technology, the suggested effective Tree Augmented Naive Bayes algorithm would be able to overcome the challenges of conventional supply chain. © Bharati Vidyapeeth's Institute of computer applications and Management 2024.

关键词： Bayes classifier algorithm Food safety and IoT FSC Traceability Tree augmented Naive Bayes

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CNN-based Plant Leaf Disease Detection: A Key Solution for Enhancing Agricultural Productivity 3

CNN-based Plant Leaf Disease Detection: A Key Solution for E...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Nayak, Deepika Nair, Rekha R Tinababu Kishore, S. Alliance University Department of Computer Science and Engineering Alliance School of Advanced Computing Bengaluru India Department of Computer Applications Karnataka Mangaluru India

ISBN: (纸本)9798350354171

Agriculture plays a pivotal role in our lives and holds significant importance in our economy. Proper management of agricultural practices is essential for maximizing profits in agricultural production. However, many farmers lack expertise in identifying and managing plant leaf diseases, leading to reduced crop yields. Since agricultural productivity directly impacts the profitability of farming, efficient disease detection and management are crucial. To address this issue, Convolutional Neural Networks (CNN) emerge as a viable solution for leaf disease detection and classification. The primary objective of this research is to develop a robust CNN-based system capable of detecting and classifying leaf diseases in various crops such as apple, grape, corn, potato, tomato, and more. In this research, the CNN algorithm was utilised, and the accuracy was 97.58%. The system's application will enable farmers to monitor large fields of crops, facilitating early detection and treatment of diseases. The significance of plant leaf disease detection spans across multiple sectors, including Biological Research and Agriculture Institutes. Detecting diseases promptly can help implement timely medical treatments, thereby mitigating the negative impacts on crop health and productivity. Moreover, such a system can assist in monitoring crop health on a larger scale, benefiting the agricultural sector as a whole. © 2024 IEEE.

关键词： Agribusiness

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Classification of Skin Cancer Using Deep Learning 8th

Classification of Skin Cancer Using Deep Learning

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8th International Conference on Emerging Research in Computing, Information, Communication and applications, ERCICA 2023

作者： Nagarjun, A. Manju, N. Aradhya, V. N. Manjunath Shruthi, N. Malapriya, S. Department of Information Science and Engineering JSS Science and Technology University Mysuru India Department Computer Applications JSS Science and Technology University Mysuru India

ISBN: (纸本)9789819976324

Skin cancer is one of the most rapidly spreading illnesses in the world and because of the limited resources available, early detection of skin cancer is crucial accurate diagnosis of skin cancer identification for preventive approach in general. One of these models, Convolutional Neural Networks (CNN), has surpassed all others in object detection and classification tests. The dataset is screened from MNIST: HAM10000 which consists of seven different types of skin lesions with the sample size of 10,015. Transfer learning technique is used to train the model to obtain outstanding results. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolutional neural networks

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Artificial intelligence in physiological characteristics recognition for internet of things authentication

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Digital Communications and Networks 2024年第3期10卷 740-755页

作者： Zhimin Zhang Huansheng Ning Fadi Farha Jianguo Ding Kim-Kwang Raymond Choo School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing100083China Faculty of Informatics Engineering Aleppo UniversityAleppoSyria Department of Computer Science Blekinge Institute of Technology37179KarlskronaSweden Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Effective user authentication is key to ensuring equipment security,data privacy,and personalized services in Internet of Things(IoT)***,conventional mode-based authentication methods(e.g.,passwords and smart cards)may be vulnerable to a broad range of attacks(e.g.,eavesdropping and side-channel attacks).Hence,there have been attempts to design biometric-based authentication solutions,which rely on physiological and behavioral *** characteristics need continuous monitoring and specific environmental settings,which can be challenging to implement in ***,we can also leverage Artificial Intelligence(AI)in the extraction and classification of physiological characteristics from IoT devices processing to facilitate ***,we review the literature on the use of AI in physiological characteristics recognition pub-lished after *** use the three-layer architecture of the IoT(i.e.,sensing layer,feature layer,and algorithm layer)to guide the discussion of existing approaches and their *** also identify a number of future research opportunities,which will hopefully guide the design of next generation solutions.

关键词： Physiological characteristics recognition Artificial intelligence Internet of things Biological-driven authentication

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