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Improving Food Safety by IoT-based Climate Monitoring and Control Systems for Food Processing Plants

Remote Sensing in Earth Systems sciences 2025年第2期8卷 387-399页

作者： Kolikipogu, Ramakrishna Shivaputra Muniyandy, Elangovan Maroor, Jnaneshwar Pai Lakshmi, Gundala Venkata Rama Konduri, Bhagavan Naveenkumar, R. Department of Information Technology Chaitanya Bharathi Institute of Technology Hyderabad India Department of Electronics and Communication Engineering Dr. Ambedkar Institute of Technology Bengaluru India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Department of Business Administration Nitte Institute of Professional Education Nitte (Deemed to Be University) Karnataka Mangaluru India Department of Computer Science and Engineering CVR College of Engineering Hyderabad India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Computer Science and Engineering Chandigarh Colleges of Engineering Chandigarh Group of Colleges Jhanjeri Punjab Mohali 140307 India

Web different IoT apps offer lot more options for every player in the meat supply chain to monitor, control, and optimize their manufacturing method. Beginning within late 2017, industrial IoF2020 program will concentrate toward massive deployments of IoT via three use cases in the meat supply chain: pig farm management, poultry chain tracking, and for livestock visibility with openness. This initiative will include eleven collaborators spanning 5 nations. Installing and integrating IoT sensors for environmental and livestock management would alleviate producers’ difficulty getting knowledge that data needed regarding continuously tracking how well they are doing. Alert detection is additionally being created, integrating multiple sources of data in order to give farmers helpful input along with communicating news with other partners. By carrying out this, preventative or remedial measures can be done for infections, swine taint, bird mortality, feed waste, the natural world, et cetera. Additionally, EPCIS-based traceability from farm to fork will be allowed to ensure customers may get precise data about the source and caliber of their livestock. Each of these three use scenarios’ existing stage in work along with future plans are expected to be discussed. IoF2020 seeks to support the digital revolution in smart farming by tackling various educational and commercial problems while also promoting its use throughout Europe. It maintains mechanical necessary degree and security against physical along with toxic contaminants during industrial butchering of chicken, a lot measures have been taken. Buyers’ worries about food safety and health are the result of eating chicken increasingly. Our authorities have begun many kinds of initiatives aimed at improving safeguards for food. Our goal is to create an apparatus that alerts the factory farms affecting potential threats for product hygiene of food. With such setup, the detector can be mounted on top of the poultry slaugh

关键词： Food safety Internet of Things Meat processing Monitoring Supply chain

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Blockchain-based Patient Recommendation System for Smart Healthcare

Blockchain-based Patient Recommendation System for Smart Hea...

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2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Mehta, Raj Mehta, Mahek Kakkar, Riya Oza, Parita Agrawal, Smita Tanwar, Sudeep Dutta, Ashit Kumar Rodrigues, Joel J.P.C. Department of Computer Science and Engineering Institute of Technology Nirma University Gujarat 382481 India Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Saudi Arabia

ISBN: (纸本)9798350350548

In recent times data breaches in various sectors of industry have become a common threat. It has become very crucial to secure patient data in the health industry. The upcoming Healthcare 4.0 techniques can play an important role in this. We have implemented these techniques in our proposed model to protect personalised health information of the individual health profiles of the patient using blockchain. The proposed model is also a recommending system with the aim to offer relevant advice to the patients to keep a check on the various health parameters like blood pressure, body temperature, blood sugar etc. © 2024 IEEE.

关键词： Electronic health record

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HPML-CVD: Hyperparameter Tuned Machine Learning Model to Predict Cardiovascular Disease

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Biomedical Materials and Devices 2025年 1-15页

作者： Uddin, Nazim Hossain, Md. Sahadat Mahmud, Md. Zulfiker Ferdous, Rebina Talukder, Md. Alamin Department of Information and Communication Technology Chandpur Science and Technology University Chandpur Bangladesh Department of Computer Science and Engineering Jagannath University Dhaka Bangladesh Basic and Applied Research on Jute Project Bangladesh Jute Research Institute Dhaka 1207 Bangladesh Department of Computer Science and Engineering International University of Business Agriculture and Technology Dhaka Bangladesh

Cardiovascular disease (CVD) remains a significant global health concern, necessitating early detection and accurate prediction for effective intervention. Machine learning (ML) offers a data-driven approach to analyzing patient data, identifying complex patterns, and predicting CVD risk factors such as blood pressure, cholesterol, and genetic predispositions. This study employs the Heart Disease UCI dataset, comprising 303 cases and 14 attributes, to develop predictive ML models. A comprehensive feature engineering approach was applied, including Chi-Square feature selection, where the 10 most influential features were identified to enhance model performance. After feature selection, hyperparameter tuning was performed to optimize five classifiers: Random Forest, K-Nearest Neighbors, Decision Tree, Support Vector Machine, and Logistic Regression. The results indicate significant improvements in accuracy after feature engineering and fine-tuning, with the Random Forest classifier achieving the highest accuracy of 93.41%, outperforming previous work on the same dataset by 3.41%, followed by SVM at 91.21%. These findings underscore the potential of ML in early CVD prediction, emphasizing its role in proactive risk management and healthcare. The integration of ML-driven CVD risk assessment paves the way for personalized healthcare, benefiting both patients and medical professionals by facilitating early intervention, improving patient outcomes, and optimizing healthcare resource allocation. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Cardiovascular disease Cleveland dataset Heart disease prediction Logistic regression Machine learning

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Minimizing energy consumption in fixed node networks using a novel neutrosophic model: comparative analysis with standard and existing algorithms

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International Journal of Information Technology (Singapore) 2025年 1-15页

作者： Ghimire, Sangam Bhurtel, Nirjal Jha, Sudan Ahmad, Sultan Abdeljaber, Hikmat A. M. Nazeer, Jabeen Department of Computer Science and Engineering Kathmandu University 28 kilo Bagmati Dhulikhel 45210 Nepal Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-kharj 11942 Saudi Arabia School of Computer Science and Engineering Lovely Professional University Punjab Phagwara 144411 India Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan MEU Research Unit Middle East University Amman 11831 Lebanon

The growing development and utilization of networked systems has led to more concern regarding the energy efficiency of these systems. In this paper, we present a novel approach to minimizing energy consumption in fixed node network for which we propose a new Neutrosophic model. This model leverages the unique strengths of Neutrosophic logic to enhance the efficiency by minimizing the energy consumption. The simulation of results is performed using Matplotlib (Python) with implementation parameters such as number of nodes (n) and additive white Gaussian noise (AWGN) (η). Additionally, we show a comparative analysis between the proposed model and other prevalent approaches used in minimizing energy consumption in networked systems. The comparative results shows improvement in energy efficiency, showcasing the potential of the proposed model as an alternative for optimizing energy consumption in networked systems. This work highlights the effectiveness of the proposed model and opens new avenues for future advancements in energy-efficient networked system using Neutrosophic approach. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Comparative analysis Energy-consumption optimization Networked systems Neutrosophic model Sensitivity analysis

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Efficacious Routing Approaches in Vehicular Ad hoc Network: An Empirical Study 1

Efficacious Routing Approaches in Vehicular Ad hoc Network: ...

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1st International Conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023

作者： Janet Kumari, J Jesy Thangam, S. Raja, A. Saleem Amrita Vishwa Vidhyapeetham Amrita School of Computing Bengaluru Department of Computer Science and Engineering India University of Technology and Applied Science-Shinas Department of Computer Science and Engineering Oman

ISBN: (纸本)9798350342796

VANET, or Vehicular Ad hoc Network is a transient wireless structure made up of different kinds of vehicles serving as network hubs and the links between them acting as links. VANET is an improving mechanism that has received an amount of growth in the fields of research, academia, and business. A VANET is a mobile network that uses vehicles as terminals. VANETs, similar to spontaneous systems, possess all the main features of independent connectivity, including processing and preservation constraints. To improve safety while driving and other services, a network of VANETs can be established. The effectiveness of VANETs has been demonstrated in numerous real-world applications, such as traffic management and driver-assist systems. Information exchanged over VANETs can undoubtedly help users experience a better driving experience and prevent accidents. academia, and government. VANET is an acronym used to describe the Vehicular ad hoc network that forms over-moving vehicles on the road. Vehicle networks are rapidly emerging as a platform for creating and implementing new and classic applications. VANETs are distinguished by their high mobility, frequently changing topology, and ephemeral, one-time connections. Vehicle interruptions are common due to how dynamic and moving vehicles are at present. Clustering techniques for vehicles that arrange to increase network scalability and connection dependability as VANET introduces vehicles in groups. Subsequently, other clustering strategies and techniques also exist, and it is necessary for a wide range of situations, including information transfer, routing, and detection of accidents. The methods used for vehicle clustering techniques are examined in this study in terms of networking techniques. © 2023 IEEE.

关键词： ant colony optimization hybrid protocol proactive reactive VANET

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Energy-Efficient Cloud Computing Through Reinforcement Learning-Based Workload Scheduling

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International Journal of Advanced computer science and Applications 2025年第4期16卷 645-656页

作者： Malipatil, Ashwini R Paramasivam, M.E. Gulyamova, Dilfuza Saravanan, Aanandha Ramesh, Janjhyam Venkata Naga Muniyandy, Elangovan Ghodhbani, Refka Department of Computer Science and Engineering BNM Institute of Technology Bangalore India Department of ECE Sona College of Technology Salem India Computer Engineering Department University of Information Technologies in Tashkent Tashkent Uzbekistan Department of ECE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of CSE Graphic Era Hill University Dehradun248002 India Department of CSE Graphic Era Deemed to be University Uttarakhand Dehradun248002 India Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Center for Scientific Research and Entrepreneurship Northern Border University Arar73213 Saudi Arabia

The basis for current digital infrastructure is cloud computing, which allows for scalable, on-demand computational resource access. Data center power consumption, however, has skyrocketed because of demand increases, raising operating costs and their footprint. Traditional workload scheduling algorithms often assign performance and cost priority over energy efficiency. This paper proposes a workload scheduling method utilizing deep reinforcement learning (DRL) that adjusts dynamically according to present cloud situations to ensure optimal energy efficiency without compromising performance. The proposed method utilizes Deep Q-Networks (DQN) to perform feature engineering to identify key workload parameters such as execution time, CPU and memory consumption, and subsequently schedules tasks smartly based on these results. Based on evaluation output, the model brings down the latency to 15 ms and throughput up to 500 tasks/sec with 92% efficiency in load balancing, 95% resource usage, and 97% QoS. The proposed approach yields improved performance in terms of key parameters compared to conventional approaches such as Round Robin, FCFS, and heuristic methods. These findings show how reinforcement learning can significantly enhance the scalability, reliability, and sustainability of cloud environments. Future work will focus on enhancing fault tolerance, incorporating federated learning for decentralized optimization, and testing the model on real-world multi-cloud infrastructures. © (2025), (science and Information Organization). All Rights Reserved.

关键词： Deep reinforcement learning

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A Novel Scavenger Hunt Activity for Increasing Student Engagement in Cryptography Coursework

A Novel Scavenger Hunt Activity for Increasing Student Engag...

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2024 ASEE Annual Conference and Exposition

作者： Rathore, Heena Griffith, Henry Computer Science Department Texas State University United States Department of Math and Engineering San Antonio College United States

As the realm of cybersecurity grows increasingly critical, imparting the knowledge of computer system security particularly cryptography to students is *** paper presents an innovative approach to this endeavor through the integration of scavenger hunt, uniquely tailored to transcend the boundaries of traditional *** conventional methods which are predominantly introduced during high school or incorporate a single intricate puzzle for participants to solve, this paper emphasizes practical application over theory, improving the way students grasp complex concepts and retain *** this work, students collaborated in groups to engage in an "Capture the Flag" style scavenger hunt, conducted through the platform of Google *** the activity, they were actively encouraged to leverage a wide array of online tools, encompassing resources such as usage of large language models such as ChatGPT and various others, to collaboratively solve the *** the exercise, students encountered encrypted messages at various stages and to progress in the activity had to apply cryptographic principles to decipher these *** proposed practical application of cryptography involved tasks like decrypting codes, solving puzzles, or using ciphers to reveal clues led them closer to the final *** introducing scavenger hunt at the intersection of computer system security education, we open a gateway to experiential learning that is engaging, interactive, and *** approach was also compared with a research-centric group project that delved into establishing secure methods for cyber-physical *** study indicates that a majority of students (77.4%) viewed the Capture the Flag Scavenger Hunt as a highly valuable learning experience. © American Society for engineering Education, 2024.

关键词： Students

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Innovations in Agri-Tech: A Review of Artificial Intelligence Applications and Challenges in Modern Agriculture 2

Innovations in Agri-Tech: A Review of Artificial Intelligenc...

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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Agarwal, Rupanshi Bhardwaj, Isha Sharma, Ashish Kumar Sanghi, Akash Agarwal, Gaurav Invertis University Department of Computer Science and Engineering Bareilly India Kothiwal Institute of Technology and Professional Studeis Department of Applied Science and Humanities Moradabad India Invertis University Department of Computer Applications Bareilly India

ISBN: (纸本)9798331519056

Artificial intelligence (AI) is driving a massive shift in the agriculture industry, which is essential to both global food security and economic stability. This review article explores the use of AI technologies in agriculture, outlining their current and potential future applications while charting their evolution. We examine several AI applications such as computer vision, machine learning, robots and the (IoT) Internet of Things, highlighting their contributions to increasing efficiency, streamlining resource allocation, and strengthening decision-making. Machine learning techniques are utilized for predictive analytics in disease identification and crop yield estimation, while computer vision supports plant health monitoring and harvesting automation. Planting, weeding, and harvesting chores are revolutionized by robotics, and data collecting for precision agriculture is made easier by IoT devices in conjunction with sensors. Additionally, by offering indepth analyses of the soil and crops, drones and satellite imagery are essential to precision farming. The technological, financial, and social barriers to the broad adoption of AI are also covered in this study. These barriers include poor data quality, expensive implementation, and moral dilemmas with regard to labour displacement and environmental effects. In this paper, we propose that the future of global food security and sustainability depends on the strategic application of AI in agriculture. In order to fully realize the potential of artificial intelligence in agriculture Strategic implementation will determine the sustain ability and future of global food security. In this order, this study attempts to direct future research and policy development by offering a thorough overview of existing technology, applications, and difficulties. © 2024 IEEE.

关键词： Smart agriculture

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Secured Cyber Security Algorithm for Healthcare System Using Blockchain Technology

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Intelligent Automation & Soft Computing 2023年第2期35卷 1889-1906页

作者： D.Doreen Hephzibah Miriam Deepak Dahiya Nitin C.R.Rene Robin Computational Intelligence Research Foundation(CIRF) Chennai600023TamilnaduIndia College of Computer and Information Sciences Majmaah UniversityMajmaah11952Saudi Arabia Department of Electrical Engineering and Computer Science College of Engineering and Applied ScienceUniversity of CincinnatiCincinnati45221OHUnited States Department of Computer Science and Engineering Sri Sairam Engineering CollegeChennai600044TamilnaduIndia

Blockchain technology is critical in cyber *** most recent cryptographic strategies may be hacked as efforts are made to build massive elec-tronic *** of the ethical and legal implications of a patient’s medical data,cyber security is a critical and challenging problem in *** image secrecy is highly vulnerable to various types of *** a result,designing a cyber security model for healthcare applications necessitates extra caution in terms of data *** resolve this issue,this paper proposes a Lionized Golden Eagle based Homomorphic Elapid Security(LGE-HES)algorithm for the cybersecurity of blockchain in healthcare *** blockchain algorithm preserves the security of the medical image by performing hash *** execution of this research is carried out by MATLAB *** suggested fra-mework was tested utilizing Computed Tumor(CT)pictures and MRI image data-sets,and the simulation results revealed the proposed model’s profound *** the simulation,94.9%of malicious communications were recognized and identiﬁed effectively,according to the total outcomes *** suggested model’s performance is also compared to that of standard approaches in terms of Root Mean Square Error(RMSE),Peak Signal to Noise Ratio(PSNR),Mean Square Error(MSE),time complexity,and other factors.

关键词： Healthcare system cybersecurity security data sharing blockchain

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Discrete GWO Optimized Data Aggregation for Reducing Transmission Rate in IoT

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computer Systems science & engineering 2023年第3期44卷 1869-1880页

作者： S.Siamala Devi K.Venkatachalam Yunyoung Nam Mohamed Abouhawwash Department of Computer Science and Engineering Sri Krishna College of TechnologyCoimbatore641042India Department of Applied Cybernetics Faculty of ScienceUniversity of Hradec KrálovéHradec Králové50003Czech Republic Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

The conventional hospital environment is transformed into digital transformation that focuses on patient centric remote approach through advanced *** diagnosis of many diseases will improve the patient *** cost of health care systems is reduced due to the use of advanced technologies such as Internet of Things(IoT),Wireless Sensor Networks(WSN),Embedded systems,Deep learning approaches and Optimization and aggregation *** data generated through these technologies will demand the bandwidth,data rate,latency of the *** this proposed work,efficient discrete grey wolf optimization(DGWO)based data aggregation scheme using Elliptic curve Elgamal with Message Authentication code(ECEMAC)has been used to aggregate the parameters generated from the wearable sensor devices of the *** nodes that are far away from edge node will forward the data to its neighbor cluster head using *** scheme will reduce the number of transmissions over the *** aggregated data are preprocessed at edge node to remove the noise for better *** node will reduce the overhead of cloud *** aggregated data are forward to cloud server for central storage and *** proposed smart diagnosis will reduce the transmission cost through aggrega-tion scheme which will reduce the energy of the *** cost for proposed system for 300 nodes is 0.34μ*** energy cost of existing approaches such as secure privacy preserving data aggregation scheme(SPPDA),concealed data aggregation scheme for multiple application(CDAMA)and secure aggregation scheme(ASAS)are 1.3μJ,0.81μJ and 0.51μJ *** optimization approaches and encryption method will ensure the data privacy.

关键词： Discrete grey wolf optimization data aggregation cloud computing IoT WSN smart healthcare elliptic curve elgamal energy optimization

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