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FinSecure: Utilizing IoT Sensors for Formaldehyde Detection and Fish Freshness Detection for Enhancing Safety in Fish Consumption Using Machine Learning and Deep Learning

FinSecure: Utilizing IoT Sensors for Formaldehyde Detection ...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Harish, S. Somesh, K. Suntharavadivelan Venkatesan, M. Prabhavathy, P. National Institute of Technology Puducherry Department of Computer Science and Engineering Karaikal India Vellore Institute of Technology Department of Computer Science and Engineering and Information Systems Vellore India

ISBN: (数字)9798331540685

ISBN: (纸本)9798331540685

The global food supply heavily relies on fisheries, highlighting the crucial importance of ensuring the safety of fish products. However, the widespread application of antibiotics and the existence of compounds such as formaldehyde present significant hazards to both human consumers and the underwater ecosystem. These risks are compounded by processes like bio-magnification, further emphasizing the need for vigilant monitoring and regulation. This project presents a comprehensive approach to fisheries management and food safety monitoring through the integration of advanced machine learning, deep learning techniques and IoT sensor technology. The overall objective is to develop a model that can determine whether the given sample Fish Species is safe or unsafe for human consumption, by considering parameters: Fish Freshness Detection, using a HCHO Sensor to determine the Formaldehyde levels in the given Fish and finally using these parameters to determine whether the fish can be consumed or not. For the Fish Freshness Detection, utilizing Transfer Learning (pre-trained MobileNetV2 model), the system achieves an impressive accuracy of 99.02%. Thus, integrating all these models, determine whether the supplied fish is safe for consumption or not. By integrating these functionalities, the system contributes to proactive fisheries and food safety monitoring and protection. The holistic approach of this project, combining accurate positions the developed model and IoT application as valuable assets for sustainable aquaculture practices and the preservation of aquatic ecosystems. The project's outcomes contribute to the advancement of several interdisciplinary domains such as machine learning, and fisheries management, laying the groundwork for continued innovation in this interdisciplinary domain. © 2024 IEEE.

关键词： Fish

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Blending Visitor Familiarity and Site Significance: RAG Enhanced Heritage Interpretation 13

Blending Visitor Familiarity and Site Significance: RAG Enha...

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Chao, August F.Y. National Penghu University of Science and Technology Department of Computer Science and Information Engineering Penghu Taiwan

ISBN: (纸本)9798350355079

Cultural heritage interpretation holds a critical role in enriching the comprehension and admiration of historical sites in small towns by visitors. Nevertheless, the lack of adequate human resources in remote areas may pose a challenge to the effective interpretation of heritage. In this research, a new methodology is suggested, which utilizes Retrieval-Augmented Generation (RAG) technology to deliver personalized cultural heritage narratives that are customized to suit the preferences of tourists. Through the consolidation of extensive language models with retrieval capabilities, the system can connect visitors' existing knowledge with the local attractions, objects, and activities, thereby nurturing a stronger bond with the abundant history of the location. The proposed system helps overcome the challenges of reaching diverse audiences and highlighting the cultural importance, while also reducing the need for specialized and trained staff members. A trial run employing the Llama3 model and resources linked to Nanliao village in Penghu Island showcases the system's proficiency in producing pertinent and culturally sensitive outputs. The research shows the potential benefits of combining advanced language analysis technology with explanations of cultural heritage. This could provide a scalable and efficient way to enhance the experiences of visitors to small towns. © 2024 IEEE.

关键词： Tourism

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Smart Hybrid Mini-Xception Model and Eyebrow Displacement-Based Stress Level and Emotion Detection System 12th

Smart Hybrid Mini-Xception Model and Eyebrow Displacement-Ba...

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Bhuvana, S. Anantha Prabha, P. Deva Priya, M. Christy Jeba Malar, A. Karthick, S. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Tamilnadu Coimbatore India Department of Computer Science and Engineering Karpagam College of Engineering Tamilnadu Coimbatore India Department of Information Technology Sri Krishna College of Technology Tamilnadu Coimbatore India Department of Computer Science and Engineering Delhi - NCR Campus SRM Institute of Science and Technology Uttar Pradesh Ghaziabad India

ISBN: (纸本)9789819789450

Stress is a state of mental or emotional strain due to adversative or challenging situations. A human may undergo bad life experiences or events, and it is a significant issue to be dealt in today's society. It comes as a result of family situations, health concerns, work environments and people's behavior among other things. It affects people of various ages, including children, teenagers, middle-aged people and senior citizens. It has serious consequences affecting human body and life. These problems can be addressed by detecting mental stress at an early stage. The main goal of the propounded system is to identify human stress using a pre-trained Mini-Xception model. Additional measures such as eye moments, mouth activity and head motion are employed for determining the level of human stress. FER2013 dataset is used and the system is tested for all kinds of emotions. Human stress level is experimented by capturing video using webcam. From the experimental outcomes, it is evident that the proposed system offers better performance in terms of Precision, Accuracy, Specificity and Sensitivity in contrast to traditional methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Eyebrow displacement FER2013 dataset Mini-Xception model Stress detection

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A Computational Model for Driver Risk Evaluation and Crash Prediction Using Contextual Data from On-board Telematics

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Recent Advances in computer science and Communications 2022年第4期15卷 620-635页

作者： Jain, Neetika Mittal, Sangeeta Department of Computer Science & Engineering and Information Technology Faculty of Engineering Jaypee Institute of Information Technology Noida India

Introduction: Vehicle crashes can be hazardous to public safety and may cause infrastructure damage. Risky driving significantly raises the possibility of the occurrence of a vehicle crash. As per statistics by the World Health Organization (WHO), approximately 1.35 million people are involved in road traffic crashes resulting in loss of life or physical disability. WHO attributes events like over-speeding, drunken driving, distracted driving, dilapidated road infrastructure and unsafe practices such as non-use of helmets and seatbelts to road traffic accidents. As these driving events negatively affect driving quality and enhance the risk of a vehicle crash, they are termed as negative driving attributes. Methods: A multi-level hierarchical fuzzy rules-based computational model has been designed to capture risky driving by a driver as a driving risk index. Data from the onboard telematics device and vehicle controller area network is used for capturing the required information in a naturalistic way during actual driving conditions. Fuzzy rules-based aggregation and inference mechanisms have been designed to alert about the possibility of a crash due to the onset of risky driving. Results: On-board telematics data of 3213 sub-trips of 19 drivers has been utilized to learn long term risky driving attributes. Furthermore, the current trip assessment of these drivers demonstrates the efficacy of the proposed model in correctly modeling the driving risk index of all of them, including 7 drivers who were involved in a crash after the monitored trip. Conclusion: In this work, risky driving behavior has been associated not just with rash driving but also other contextual data like driver’s long-term risk aptitude and environmental context such as type of roads, traffic volume and weather conditions. Trip-wise risky driving behavior of six out of seven drivers, who had met with a crash during that trip, was correctly predicted during evaluation. Similarly, for the other 12

关键词： Telematics

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Recognition of American sign language using modified deep residual CNN with modified canny edge segmentation

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Multimedia Tools and Applications 2025年 1-28页

作者： Hariharan, U. Devarajan, Devikanniga Kumar, P. Santhosh Rajkumar, K. Meena, M. Akilan, T. Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University Punjab Mohali140413 India Department of Computer Science and Engineering GITAM University Karnataka Bengaluru561203 India Department of Information Technology Faculty of Engineering and technology SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai600089 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India Department of Computer Science and Engineering Sri Sairam Engineering College Tamil Nadu Tambaram602109 India Research Coordinator Dept. of IQAC Amity University Kolkata West Bengal 700135 India

American Sign Language (ASL) recognition aims to recognize hand gestures, and it is a crucial solution to communicating between the deaf community and hearing people. However, existing sign language recognition algorithms still have some drawbacks, such as difficulty recognizing hand movements and low recognition accuracy for most sign language recognition. A Modified Convolutional Neural Network (MCNN) deep residual 101 classifier-based American Sign Language identification system has been developed to address this problem. There are three main parts presents in the method. The first part is pre-processing the images to remove the noise, enhance the contrast of the picture, adjust the contrast level and smoothen the picture using various filters. The second part is the segmentation, and it’s used to partition the image using a modified canny edge detection method by removing all weak edges present in the image. Finally, classification will be done using the Modified CNN deep residual 101 classifiers. By classifying the image, the American Sign Language is accurately identified. This process is conducted through images. The outcome shows that the suggested approach has a 0.97% accuracy and a 0.05% False Positive Rate. Other CNN architectures such as resNet 50 and resNet 18 reached 0.90% and 0.80% accuracy, respectively, which is lower than our proposed method. In addition, the resNet 101 classifier effectively recognizes the difficult hand gestures through the image data. It obtains high recognition accuracy for 36 signs is 0 to 9 numbers and a to z alphabets from American Sign Language. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Utilizing Social Trust Mechanisms to Minimize the Influence of False Ratings on Recommendation Systems 16

Utilizing Social Trust Mechanisms to Minimize the Influence ...

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16th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2024

作者： Wu, Sin-Hua Lin, Lien-Fa National Kaohsiung University of Science and Technology Department of Computer Science and Information Engineering Kaohsiung Taiwan

ISBN: (纸本)9798350377903

In today's recommendation systems, people's ratings of products are important criteria for analysis. For instance, a recommendation system based on content analysis may be utilized. However, this can lead to a decrease in recommendation accuracy due to the presence of a large number of false ratings. In this study, we propose a recommendation system that utilizes two different neural networks. The first model is based on users' ratings of items for rating prediction, while the second model incorporates users' social trust data to determine the trust level of item ratings. Finally, the results of the two models are integrated using an integrated learning approach to mitigate the effect of false ratings. © 2024 IEEE.

关键词： Prediction models

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Agriconnect: Revolutionizing Farm Machinery Booking 2

Agriconnect: Revolutionizing Farm Machinery Booking

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2nd IEEE International Conference on Advances in information technology, ICAIT 2024

作者： Subrahmanya Pradhan, Trilochan Shaikh, Sauda Shabbir Shetty, Ruchitha N. Shreekumar, T. Department of Information Science & Engineering Mangalore Institute of Technology and Engineering Moodabidri India Department of Computer Science & Engineering Mangalore Institute of Technology and Engineering Moodabidri India

ISBN: (纸本)9798350383867

Crop assessment plays an important role in ensuring food safety, and recent technological advances such as machine learning and deep learning have revolutionized assessment, and crop and culture management. Agriconnect is the largest network combining image-based crop analysis with rental equipment to optimize agriculture. Agriconnect uses the latest image processing technology to identify crops from field images, allowing farmers to make informed decisions about their crops. In addition, the platform supports easy-to-assemble equipment, allowing farmers to adapt machines to their specific needs, thus reducing waste and improving resource allocation. Agriconnect simplifies the farming process and increases overall profitability by providing a user-friendly interface for product identification and equipment booking. By combining forward-thinking technology with powerful management tools, Agriconnect not only improves the accuracy of farming but also improves efficiency. He envisions a future in which technology will play a key role in making farming easier and more efficient for farmers around the world. Agriconnect is a prime example of how technology is changing agriculture, enabling farmers to make informed decisions, improving resource use, and ultimately increasing production to implement permaculture practices. © 2024 IEEE.

关键词： Algae control

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Enhancing Efficiency in Livestock Monitoring and Management with IoT Solutions '

Enhancing Efficiency in Livestock Monitoring and Management ...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Sahoo, Girija Shankar Devrani, Rekha Aadil, Mir Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

This technical abstract makes a specialty of an Iota-based strategy to enhance the performance of livestock monitoring and management. The proposed answer involves the usage of wireless sensors and mobile gadgets to gather records regarding the environment and the health of the animals, which are then processed and transmitted to the cloud for similar analysis. The evaluation from the cloud can then be used to detect modifications in the livestock's health or surroundings, trigger indicators, and inform endorsed moves. This solution reduces the time requirements for facts collection and evaluation and consequently decreases the ability for diseases to occur or unfold many of the farm animals. Moreover, the solution can offer vital statistics for farmers and ranchers to make higher selections to enhance the productivity of their herds and streamline operational processes. © 2024 IEEE.

关键词： management modifications requirements surroundings technical abstract

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Enhancing User Experience and Integrating Passwordless FIDO-Based Authentication through Bluetooth Low Energy (BLE) 3

Enhancing User Experience and Integrating Passwordless FIDO-...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Rathi, Snehal Oswal, Harsh D'Souza, Ansfred Pratham, Shaantanu Khedkar, Vijayshri BRACTs Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Department of Computer Science & Engineering Pune India

ISBN: (纸本)9798350354348

Traditional password-based authentication methods confront several issues such as security weaknesses, user inconvenience, and susceptibility to various attacks, e.g. phishing. These issues are solved with newly created industry standards such as Web Authentication, CTAP (Client to Authenticator Protocol), FIDO (Fast Identity Online), and W3C guidelines that deliver reliable and interoperable authentication solutions. This research explores how the FIDO, Web Authentication, CTAP, and W3C standards can be blended to revolutionize authentication practices. The proposed method should increase the security and usability of the authentication process without requiring passwords. Our technology facilitates this by establishing secure connections over Bluetooth, offering seamless UX - between a mobile device (in your pocket) and single desktop while maintaining the strongest security postures through structured authentication experiences. © 2024 IEEE.

关键词： Authentication Protocol

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Heterogeneous Ensemble Feature Selection Model(HEFSM)for Big Data Analytics

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computer Systems science & engineering 2023年第5期45卷 2187-2205页

作者： M.Priyadharsini K.Karuppasamy Department of Information Technology Sri Ramakrishna Engineering CollegeCoimbatoreTamilnaduIndia Department of Computer Science&Engineering RVS College of Engineering and TechnologyCoimbatoreTamilnaduIndia

Big Data applications face different types of complexities in *** and purifying data by eliminating irrelevant or redundant data for big data applications becomes a complex operation while attempting to maintain discriminative features in processed *** existing scheme has many disadvantages including continuity in training,more samples and training time in feature selections and increased classification execution *** ensemble methods have made a mark in classification tasks as combine multiple results into a single *** comparing to a single model,this technique offers for improved *** based feature selections parallel multiple expert’s judgments on a single *** major goal of this research is to suggest HEFSM(Heterogeneous Ensemble Feature Selection Model),a hybrid approach that combines multiple *** major goal of this research is to suggest HEFSM(Heterogeneous Ensemble Feature Selection Model),a hybrid approach that combines multiple ***,individual outputs produced by methods producing subsets of features or rankings or voting are also combined in this ***(K-Nearest Neighbor)classifier is used to classify the big dataset obtained from the ensemble learning *** results found of the study have been good,proving the proposed model’s efficiency in classifications in terms of the performance metrics like precision,recall,F-measure and accuracy used.

关键词： PSO(Particle Swarm Optimization) GWO(GreyWolf Optimization) EHO(Elephant Herding Optimization) data mining big data analytics feature selection HEFSM classifier

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