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Gaussian Differential Privacy Integrated Machine Learning Model for Industrial Internet of Things

SN computer Science 2023年第5期4卷 454页

作者： Lazar, Arokia Jesu Prabhu Soundararaj, Sivaprakash Sonthi, Vijaya Krishna Palanisamy, Vishnu Raja Subramaniyan, Vanithamani Sengan, Sudhakar Department of Computer Science and Engineering AI & ML CMR Institute of Technology Telangana Hyderabad 501401 India Department of Software Systems School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore 632014 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram 522502 India Department of Computer Science and Engineering Kongu Engineering College Tamil Nadu Perundurai 638060 India Master of Computer Applications M. Kumarasamy College of Engineering Tamil Nadu Karur 638060 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli 627152 India

Agriculture, energy, mining, healthcare, and transportation are a few of the top industries transformed by the industrial internet of things (IIoT). Industry 4.0 mainly relies on machine learning (ML) to use the vast interconnectedness and large amounts of IIoT data that IIoT primarily drives. ML approaches are trained on confidential data generated by the IIoT environment often exposes privacy to adversarial assaults. Blockchain-based ML is established in the proposed secured IIoT research work to safeguard and enhance privacy. This research proposes a Gaussian differential privacy-integrated machine learning model (GDPIMLM), created for a scalable and controlled IIoT system, fully connected (FC) layer, implemented blockchain benefits to create a privacy-preserving mechanism while considering other limitations and reasonable time. A probability test using two altered Gaussian distributions (GD) forms the basis for defining Gaussian differential privacy (GDP). Due to a central restriction theorem for differential privacy, GDP is the primary privacy term within the family of Federal differential privacy (f-DP) policies. Ethereum is used to implement experimental evaluations. Data collections show that the recommended method enhances digital data privacy with industry-leading security without reducing performance. The performance analysis of the proposed model of secured industrial internet of things (IIoT) is studied using the accuracy of comparison with existing methods and for diverse distributed entities (DE). The proposed secured IIoT attains a minimal time consumption (TC) of 790 Sec—the highest accuracy of 92%, outperforming the existing method. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Blockchain Industry Industry 4.0 Learning Privacy Security Smart objects

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Machine Learning For Smart School Selection And Admissions

Machine Learning For Smart School Selection And Admissions

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Gupta, Anish Tyagi, Lalit Kumar Kumar, Sandeep Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Science & Engineering Mohali India Aryan Institute of Technology Ghaziabad India

ISBN: (纸本)9798331508685

Selecting the appropriate school for their child is one of the decisions that parents are faced with in life, this process can be stressful and uncertain at times. Thus, machine learning is noted to be a possible solution that can be deployed to enhance and optimize the admissions and school selection process in the current periods. The aim of this research is to explore the framework and applicability of machine learning techniques in view of selective admissions, and smart school selection. In the early stages of our investigation, we suggest a simple to use, web based tool where with the aid of different learning machines, all the parents and guardians will be able to find the appropriate school for their child. To help the clients to select particular schools, the website collects and consolidates various information including, but not limited to the following: season activity involvement, academic performance, geographical preferences, and school ranking. This makes the process of selecting a school to attend easier and also helps parents to make right decisions for their children to attend the best school. The second section of this paper describes the procedures of implementing the machine learning techniques in detail, the overviews of the steps of feature selection, data preprocessing, and model evaluation. We also point out the challenges and ethical concerns of employing machine learning for school choice and the problem of an objective machine over a bias one. © 2025 IEEE.

关键词： Adversarial machine learning

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Machine learning based Development and Evaluation of a Hand Exoskeleton Rehabilitation Robot for Patients with Partial Paralysis 2

Machine learning based Development and Evaluation of a Hand ...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Singh, Durgesh Kumar Srivastava, Anand Prakash Singh, Shivanshu Paliwal, Sanjay Singh, Anupam Singh, Jagendra Hi-Tech Institute of Engineering & Technology Mechanical Engineering Department Ghaziabad India Nitra Technical Campus Department of Computer Science Ghaziabad India R D Engineering College Mechanical Engineering Department Ghaziabad India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9798350371055

This work focuses on the estimation of elbow joint angles from electromyography (EMG) signals and investigates the use of machine learning models in rehabilitation robotics. Four distinct machine learning algorithms were tested for their capacity to precisely translate EMG inputs into actuator commands: Artificial Neural Network (ANN), Naive Bayes (NB), Linear Regression (LR), and Decision Trees (DT). The models were trained and tested using a dataset generated by a wireless EMG sensor and motion capture system that recorded the dynamic relationship between elbow movements and related muscle activations. The results show the remarkable 98.7% accuracy of the ANN model, indicating that it can be a valuable tool for real-time rehabilitation robotics applications. Additionally, NB, LR, and DT displayed competitive results that demonstrated their unique capacities to manage a range of sensor inputs. The discussion highlights how important these models' interpretability, computational efficiency, and continuous adaptation are to their successful integration into real-world rehabilitation settings. This work contributes to the field of rehabilitation robotics by applying machine learning models for accurate actuation control based on EMG signals. The findings demonstrate how these models could enhance the accuracy and responsiveness of assistive technology, offering individuals with partial paralysis customized and adaptable rehabilitation regimens. © 2024 IEEE.

关键词： assistive devices electromyography joint angle prediction machine learning Rehabilitation robotics

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Maximizing Crop Yields and Water Efficiency through Tailored Irrigation System Design and Crop Selection

Maximizing Crop Yields and Water Efficiency through Tailored...

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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Musale, Prajakta Jadhav, Balasaheb Bahir, Suvarna A Thopate, Kaushalya Anarase, Dipali Jaiswal, Krishna Vishwakarma Institute of Technology Department of Engineering Sciences and Humanities Pune India Vishwakarma Institute of Technology Department of Artificial Intelligence and Data Science Pune India Trinity Academy of Engineering Department of Computer Engineering India Vishwakarma Institute of Technology Department of Computer Engineering Pune India Wipro Technology India

ISBN: (纸本)9798350308167

Today various water-pump operating systems are available in the market, which allows the user/farmer to operate his water-pump from a distant location using mobile app or on a phone call to the system which is integrated with the water-pump. While studying the existing system we came across a problem that, existing systems does not provide any solution for optimizing the usage of water and power. Optimizing the use of power and water in farming has become a real-time problem these days. To overcome this problem, we have proposed an IOT based system "Advanced Irrigation System". This system provides automation for operating the water pump in the farm by automatically turning ON and OFF the water-pump depending on the type of the crop and its moisture requirements. Also, proposed system provides farmer interface via a mobile application in his regional language, where he can check the moisture, humidity and temperature of his farm. Along with this proposed system has a Google Assistant integrated, using which famer can operate the water-pump remotely over a voice command. © 2023 IEEE.

关键词： Advanced Irrigation System Crop Selection Maximizing Crop Yields power Optimization Water Efficiency water-pump operating systems

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Sustainable Agriculture in Food Security Integrating Satellite Data Risk Assessment by Cyberattack Detection: AI Applications

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Remote Sensing in Earth Systems Sciences 2025年第2期8卷 435-443页

作者： Kumar, M. Prema Krishnammal, N. Gupta, Manu Begum, Mairaj Unisa Sultana, Sameera Degala, Divya Priya Dept. of ECE Shri Vishnu Engineering College for Women (A) Andhra Pradesh Bhimavaram 534202 India Department of Computer Science and Engineering Dr. N.G.P. Institute of Technology Tamilnadu Coimbatore India Department of Electronics and Communication Engineering Sreenidhi Institute of Science and Technology Telangana Hyderabad India Department of CSE-AIML MALLAREDDY UNIVERSITY Telangana Hyderabad India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Globally, chronic food insecurity persists and is made worse by shocks brought on by climate change, like floods and droughts. In order to guarantee prompt assistance delivery, humanitarian programming prioritizes predicting levels of food insecurity and identifying homes that are at risk. Using complicated and diverse data, machine learning (ML) models have been widely used to promote food security. This research proposes a novel technique in sustainable agriculture-based food security analysis and satellite data cyberattack detection using machine learning models. Here, the input is collected based on satellite data, and its cyber detection has been analyzed using a convolutional spatio adversarial encoder neural network. Then, from this satellite data, the food security analysis data has been collected and processed for noise removal and normalization. Processed data has been classified using the fuzzy Q-Boltzmann ResNet transfer neural network. Experimental analysis is carried out for the food security analysis dataset in terms of data integrity, detection accuracy, average precision, F-1 score, and recall. The proposed approach yielded 97% detection accuracy, data integrity of 98%, 96% average precision, 93% recall, and 94% F-1 score. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Cyberattack detection Food security analysis Machine learning models Satellite data Sustainable agriculture

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Empowering Tourists with Context-Aware Recommendations using GAN 2

Empowering Tourists with Context-Aware Recommendations using...

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2nd International Conference on Electronics and Renewable Systems, ICEARS 2023

作者： Stephy, E. Evangelin Rajeswari, M. Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350346640

A technology-based solution known as a tourism recommendation system makes suggestions to visitors based on their preferences, prior travels, and experiences. These systems gather data from a variety of sources, including web searches, user reviews, travel history, etc. When there is a lack of data about some users or items, current technologies suffer from sparsity, which can lead to inaccurate recommendations. Another challenge that tourism faces is the diversity issue, which occurs when similarities are valued over preferences. GAN and context-aware recommendation systems are two prominent techniques that are combined in our system. The system's objective is to give personalized recommendations to travelers based on multiple contextual elements. The system creates new recommendations based on the knowledge it has gained by using GANs to identify patterns and connections between various elements of a tourist's setting and their preferences. Synthetic data is generated to supplement the original dataset, which can aid in overcoming the cold-start and sparsity issues. It can also aid in the development of more scalable recommendation systems. © 2023 IEEE.

关键词： Generative adversarial networks

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Homomorphic Encryption based Federated Learning for Financial Data Security 4

Homomorphic Encryption based Federated Learning for Financia...

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4th International Conference on Computing and Communication Systems, I3CS 2023

作者： Dhiman, Shalini Nayak, Sumitra Mahato, Ganesh Kumar Ram, Anil Chakraborty, Swarnendu Kumar National Institute of Technology Department of Computer Science & Engineering Arunachal Pradesh India

ISBN: (纸本)9798350323771

Federated Learning is a distributed machine learning technique that enables on-device training without exchanging the sensitive data over the centralized server. In this paper, Federated Learning is used to train financial data models on-devices with the help of IoT applications in financial or business systems. This creates more advanced and secured financial business models. We have applied the mechanism of homomorphic encryption and cryptographic primitives, including masking and local model protection to prevent any kind of inferring in private financial or business data where multiple attackers usually find a way of model inversion or reconstruction attack. We used datasets of various financial sectors as the primary measure, rather than taking the size of datasets that is generally used in deep learning, to get the correct measurement of the rate of contribution in every session of the local model's training to the training of the global model. If the calculated online number of clients exceeds a predetermined threshold, then the federated learning process will be continued with the dropout-tolerant plan. The security study demonstrates that the suggested solution fulfils data privacy requirements. The costs of computation and communication are also examined theoretically. According to research observations, the proposed approach achieved promising outcomes while assuring privacy preservation when compared to existing schemes. © 2023 IEEE.

关键词： Sensitive data

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A Robust Object Recognition Using Modified YOLOv5 Neural Network 10

A Robust Object Recognition Using Modified YOLOv5 Neural Net...

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10th International Conference on Signal Processing and Integrated Networks, SPIN 2023

作者： Balmik, Archana Barik, Subhasish Nandy, Anup National Institute of Technology Rourkela Department of Computer Science & Engineering Rourkela India

ISBN: (纸本)9781665490993

Human beings can recognize and detect the object in any image or video with ease, but when it comes to machines dealing with the same task, it's quite challenging. In the past years, there has been significant progress in the area of computer vision. However, researchers are still working on enhancing the performance of object recognition algorithms in terms of accuracy and speed. This paper presents one of the greatest CNN representatives, You Only Look Once (YOLO), which tackles object detection in the simplest and most efficient way. This study primarily focuses on object recognition with modified YOLOv5. Here, we have implemented the model on a dataset comprising five objects. We have analyzed various preprocessing and feature extraction techniques to enhance the performance of the proposed model. The highest mean average precision (mAP) achieved by the modified YOLOv5 is 90.7%. The recognition performance of the modified YOLOv5 is compared with the YOLOv4 algorithm. It is also compared with various conventional object detection algorithms outside the YOLO family, such as Support vector machine (SVM), deep Convolutional neural network (CNN), and EfficientNet. The results proved to be promising as compared to the other state-of-the-art techniques. © 2023 IEEE.

关键词： Object recognition

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Machine Learning for Data Fusion:A Fuzzy AHP Approach for Open Issues

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computers, Materials & Continua 2023年第12期77卷 2899-2914页

作者： Vinay Kukreja Asha Abraham K.Kalaiselvi K.Deepa Thilak Shanmugasundaram Hariharan Shih-Yu Chen Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjab140401India Department of Networking and Communications SRM Institute of Science and TechnologyKattankulathurChengalpattuTamil Nadu603203India Department of Networking and Communications Faculty of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathurChengalpattuTamil Nadu603203India Department of Computer Science and Engineering Vardhaman College of EngineeringHyderabad501218India Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin64002Taiwan Intelligence Recognition Industry Service Research Center National Yunlin University of Science and TechnologyYunlin64002Taiwan

Data fusion generates fused data by combining multiple sources,resulting in information that is more consistent,accurate,and useful than any individual source and more reliable and consistent than the raw original data,which are often imperfect,inconsistent,complex,and *** data fusion methods like probabilistic fusion,set-based fusion,and evidential belief reasoning fusion methods are computationally complex and require accurate classification and proper handling of raw *** fusion is the process of integrating multiple data *** filtering means examining a dataset to exclude,rearrange,or apportion data according to the *** sensors generate a large amount of data,requiring the development of machine learning(ML)algorithms to overcome the challenges of traditional *** advancement in hardware acceleration and the abundance of data from various sensors have led to the development of machine learning(ML)algorithms,expected to address the limitations of traditional ***,many open issues still exist as machine learning algorithms are used for data *** the literature,nine issues have been identified irrespective of any *** decision-makers should pay attention to these issues as data fusion becomes more applicable and successful.A fuzzy analytical hierarchical process(FAHP)enables us to handle these *** helps to get the weights for each corresponding issue and rank issues based on these calculated *** most significant issue identified is the lack of deep learning models used for data fusion that improve accuracy and learning quality weighted *** least significant one is the cross-domain multimodal data fusion weighted 0.076 because the whole semantic knowledge for multimodal data cannot be captured.

关键词： Signal level fusion feature level fusion decision level fusion fuzzy hierarchical process machine learning

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WLARS: Workload-Aware Recharge Scheduling Mechanism for Improving Surveillance Quality in Wireless Rechargeable Sensor Networks

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IEEE Sensors Journal 2023年第11期23卷 12237-12250页

作者： Dande, Bhargavi Chang, Chih-Yung Wu, Shih-Jung Roy, Diptendu Sinha National Taiwan University Department of Electrical Engineering Taipei10617 Taiwan Tamkang University Department of Computer Science and Information Engineering New Taipei25137 Taiwan National Institute of Technology Meghalaya Department of Computer Science and Engineering Shillong793003 India

With radio frequency (RF), the mobile charger (MC) can wirelessly transmit energy to the sensor nodes in the network. The wireless energy transfer enhances the lifetime of sensor nodes in wireless rechargeable sensor networks (WRSNs). Most of the studies improved the efficiency of MC or perpetuated the lifetime of WRSNs. At the same time, none of the studies focused on considering the spatial and temporal surveillance qualities (STSQs) of each sensor recharged. Therefore, this article proposed an efficient energy recharge scheduling for MC, aiming to maximize the STSQ of the given network. Initially, sensor routing load (RL) is considered to partition the network to distribute the charging load of the MCs evenly. Second, the busy level of the MC is considered to send the recharging requests to the MC. Based on the busy level of MC, the sensors adjust the sensing rate frequency to manage their energy. Finally, cooperation between the neighboring MCs is proposed to reduce the waiting time for recharging requested sensors. The simulation results present that the proposed work yields the literature in terms of STSQ, traveling distance, average waiting time (AWT), and energy usage efficiency. © 2001-2012 IEEE.

关键词： computer architecture

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