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Artificial Neural Network Technique for DDOS Attack Prediction in Cyber-IOT System 3

Artificial Neural Network Technique for DDOS Attack Predicti...

引用

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

作者： Asad, Md Tiwari, Rakesh Kumar Technocrats Institute of Technology & Science Dept. of Computer Science Engineering Bhopal India

ISBN: (纸本)9798350342338

An intrusion-detection IoT framework is designed to analyze unauthorized access or security breaches in IoT networks. These systems typically use a combination of hardware and software to analyze network traffic, identify anomalies, and alert security personnel when a potential threat is detected. An intrusion-detection system (IDS) is a one type of security checking model that may be used to monitor a set of connections or devices in order to check for telltale indicators of possible infiltration. During the course of the last several years, there has been a general rise in the number as well as the intensity of cyber attacks. As a consequence of this, there is a need for study of security in cyber world and the prevention of assaults, such as the implementation of intrusion-detection. Businesses that rely on the internet absolutely have to make protecting their data online one of their top responsibilities. Within the scope of this study, this paper proposes a neural network technique as a means of anticipating assaults on IDS. Python Spyder is the software that make use of for this simulation. © 2023 IEEE.

关键词： Attack Cyber- IOT DDos IDS NN

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Enhancing Livestock Supply Chains with Blockchain Traceability from Source to Market: A Survey 5

Enhancing Livestock Supply Chains with Blockchain Traceabili...

引用

5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Dahariya, Tukendra Kumar Sharma, Suraj Ray, Niranjan Kumar Guru Ghasidas Vishwavidyalaya Dept. Computer Science & Engineering Chhattisgarh Bilaspur India Kalinga Institute of Industrial Technology School of Computer Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798331522100

This document provides an in-dept. analysis of how blockchain technology can greatly improve transparency, traceability, and accountability in fish and livestock supply chains, presenting the potential to transform this industry. Utilizing blockchain's decentralized framework, which guarantees a secure and unchangeable record of transactions, problems associated with inefficiencies, fraud, and data manipulation in conventional supply chains can be efficiently resolved. This survey analyzes the present condition of blockchain applications in fish and livestock management, emphasizing their advantages and drawbacks. Furthermore, it highlights significant technical and regulatory challenges that impede wider acceptance, while recognizing potential future research avenues that could enhance processes from the production stage to the retail stage. The results intend to assist stakeholders in developing supply chains that are more sustainable, efficient, and transparent, fostering trust and complying with regulatory requirements. © 2025 IEEE.

关键词： Records management

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SMOTE and Hyperparameter Optimization: A Dual Machine Learning Strategy for Enhancing Coupon Recommendation in Vehicular Contexts 3

SMOTE and Hyperparameter Optimization: A Dual Machine Learni...

引用

3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Ravikumar, R.N. Jain, Sanjay Sarkar, Manash Amity University Rajasthan Dept. of Computer Science and Engineering Jaipur India Atria Institute of Technology Dept. of Computer Science and Engineering Banglore India

ISBN: (纸本)9798350319125

This study centers on enhancing the accurateness of coupon recommendation algorithms within the framework of in-vehicle settings. The In-Vehicle Coupon Recommendation dataset from the UCI Repository was utilized in this study. A range of machine learning algorithms, such as Random Forest, Gradient Boosting, Extra Trees, LightGBM, XGBoost, AdaBoost, Stochastic Gradient Descent (SGD), and CatBoost, were employed for analysis. Among the several algorithms considered, CatBoost exhibited notable performance, with a baseline accuracy of 74%. In order to improve the efficacy of the model, we utilized the 'Synthetic Minority Over-sampling Technique' (SMOTE) to mitigate the issue of class imbalance. This was subsequently followed by a thorough hyperparameter adjustment of the CatBoost algorithm. The improved model demonstrated a significant upgrade, with an accuracy rate of 77%. Additionally, there were noteworthy improvements in precision, recall, and F1 score. The AUC, which represents the Area Under the Receiver Operating Characteristic Curve, saw a notable improvement to 0.82. This signifies a noteworthy enhancement in the capacity to differentiate between positive and negative cases. The results emphasize the efficacy of CatBoost in the context of recommending coupons within vehicles, while also drawing attention to the influence of preprocessing methods and hyperparameter adjustment in enhancing the model's overall performance. This study is a valuable contribution to the domain of personalized advertising in the context of vehicles, providing valuable insights for the advancement of coupon recommendation systems that are more precise and dependable. © 2023 IEEE.

关键词： Coupon Recommendation Hyperparameter Tuning Machine Learning Recommender System SMOTE

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Air Quality Evaluator using Arduino 6

Air Quality Evaluator using Arduino

引用

6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Pavan Ganesh Reedy, Y. Kadali Jagadees, H. Pravin, A. Dept. of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798350398496

Air pollution has a negative impact on our ability to do daily tasks and on our standard of living. Ecosystems and human well-being are under danger. Recent years have seen a notable increase in heavy industry, leaving it even more important to monitor air quality. When it comes to air quality, individuals must be aware of how much control they have. This study proposes a network of sensors to track changes in the air. The Arduino was employed as the platform's microcontroller. The purpose of the air pollution surveillance systems is to continuously track and record data on the state of the air around a certain location and upload that information to a central server for safekeeping and online access. Parts-per-million measures used to quantify pollution levels, and the results were evaluated in Microsoft Excel. The system's ability to monitor air quality as planned worked as intended. The results were displayed on the bespoke hardware's ui and were also stored in the cloud, where they could be accessed by anybody with a smartphone. © 2023 IEEE.

关键词： Air quality

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BSCIAM: A Blockchain based Secure Cloud Identity and Access Management Framework

BSCIAM: A Blockchain based Secure Cloud Identity and Access ...

引用

2024 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2024

作者： Das, Swatisipra Sahil, Mohammad Pandit, Naveen Kumar Priyadarshini, Rojalina Gochhayat, Sarada Prasad Fakir Mohan University Postgraduate Dept. of Computer Science Odisha India C.V. Raman Global University Dept. of Computer Science and Engineering Odisha India Indian Institute of Technology Jammu Dept. of Computer Science and Engineering J&K India

ISBN: (纸本)9798350348460

Cloud computing enables on-demand computation on remote servers and computers. Thanks to the adaptability and scalability of the infrastructure for data storage, processing, and management. To solve the security problems arising in cloud identity management techniques such as, dependency on a third-party token provider, leakage of user's details, single point of failure, etc., decentralized cloud identity management systems came into the picture. Blockchain is a decentralized database providing immutability and transparency to recorded transaction data. The current token-based decentralized cloud identity management systems have limitations, including the absence of data access management procedures and a lack of security features. This paper suggests a Blockchain based Secure Cloud Identity and Access Management (BSCIAM) model for secure identity and access management, utilizing encryption and key sharing techniques. The proposed model has been implemented using Ethereum smart contracts for token-based identity management. © 2024 IEEE.

关键词： access management blockchain cloud identity management

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Evaluating Ensemble Models on Imbalanced Data Sets: A Comparative Study across Varied Minority Class Ratios 4

Evaluating Ensemble Models on Imbalanced Data Sets: A Compar...

引用

4th International Conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Das, Surajit Nayak, Samaleswari P. Sahoo, Biswajit Nayak, Sarat Chandra Kiit Deemed to Be University School of Computer Engineering Bhubaneswar India Silicon Institute of Technology Dept. of Computer Science and Engineering Bhubaneswar India Gitam Deemed to Be University School of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350349856

The complexities of using ensemble models to rectify class imbalance in healthcare datasets are investigated in this study. Using well-known methods such as Random Forest, Gradient Boosting, AdaBoost, CatBoost, LightGBM, XGBoost, BRF and EE we perform an extensive analysis on data-sets that exhibit different levels of class imbalance. The study uses stratified k-fold cross-validation for robust evaluation and the Synthetic Minority Over-sampling Technique (SMOTE) for data augmentation. ROC curves are shown, and performance indicators like as accuracy, precision, recall, F1-score, and area under the curve (AUC) are examined. The results shed light on the complex effects of ensemble models on unbalanced healthcare data-sets and offer directions for further investigation. © 2024 IEEE.

关键词： Adaptive boosting

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Advancement in Image Plagiarism Detection using Feature Extraction and Deep Learning Approaches 5

Advancement in Image Plagiarism Detection using Feature Extr...

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5th International Conference on Image Processing and Capsule Networks, ICIPCN 2024

作者： Bhandari, Sonam Chaudhary, Sumit Malhotra, Sonal Joshi, Kapil Kirola, Madhu Rajkumar Uttaranchal Institute Of Technology Uttaranchal University Dept. of Computer Science and Engineering Dehradun India Graphic Era Hill University Graphic Era Deemed to be University Dept. of Computer Science and Engineering Dehradun India Dev Bhoomi Uttarakhand University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350367171

With the widespread use of digital media, amount of visual content on the internet and the incidence of image plagiarism has also increased. In various domains including academia, journalism and creative industries, unauthorized use or reproduction of images without proper attribution has become significant concern. Scaling, rotation, cropping, filtering due to these diverse range of transformations detecting image plagiarism is a challenging task now. This study investigates the use of deep learning approach to image plagiarism detection that combines methods for similarity measurement, clustering and feature extraction. A comprehensive solution is being proposed by combining Scale Invariant Feature Transform (SIFT) for extracting features, Bag of Visual Words (BoVW) model and Convolutional Neural Networks (CNNs) for image analysis. The effectiveness of the proposed approach through extensive experiments is demonstrated, outperforming the baseline methods and showcasing superior performance metrics. © 2024 IEEE.

关键词： Contrastive Learning

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Real-Time Gaze Tracking for Online Examination

Real-Time Gaze Tracking for Online Examination

引用

2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Bisht, Preyanshu Kumar, Suresh Netaji Subhas University of Technology Dept. of Computer Science and Engineering Delhi India

ISBN: (纸本)9798331508685

The exponential growth in online education has increased the demand for automated systems to ensure academic integrity during online examinations. A real-time proctoring system addresses this need by monitoring a student's eye gaze and head movements during an exam. This system leverages Dlib's pre-trained frontal face detector and 68-point facial landmark predictor to detect facial features and track the direction of eye movement and head position. By analyzing these metrics, the system can flag behaviors associated with potential cheating, such as looking away from the screen for extended periods. This paper discusses the design and implementation of the proctoring system, details the detection algorithms, and evaluates its effectiveness for real-time monitoring. © 2025 IEEE.

关键词： Online systems

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Safe Route Prediction Based on AQI and Machine Learning

Safe Route Prediction Based on AQI and Machine Learning

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2023 IEEE International Conference on Network, Multimedia and Information technology, NMITCON 2023

作者： Shreyank Kumar V, Sharath Shashwath Vishwaraj, T. Pramod, T.C. Siddaganga Institute of Technology Dept. of Computer Science and Engineering Tumakuru India

ISBN: (纸本)9798350300826

Pollution is the most indispensable and upsetting issues faced in today's day to day life in the world. Analyzing air quality is crucial for understanding and addressing the potential impacts on human health., ecosystems., and the economy. In this paper., safe route prediction system is designed for Tumakauru city commuters based on Air Quality Index (AQI) integration and by leveraging machine learning models and map functionality. Multiple machine learning models, such as XGBoost, LR, MLP classifier, RFC, and Lasso, are utilized for accurate AQI predictions. The system integrates Google Maps to input source and destination locations, retrieve possible routes, and mark danger zones based on predefined AQI thresholds. Safer routes are suggested by eliminating paths passing through identified danger zones. The system enhances user decision-making and promotes health and environmental sustainability. The results demonstrate the models' accuracy in predicting AQI values and categorizing air quality levels. The system's practical implications include minimizing health risks and reducing air pollution exposure. Overall, the proposed solution introduces a comprehensive solution for safe commuting based on AQI predictions. © 2023 IEEE.

关键词： Air quality

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W-Net Plus: Dnn for Spatial Saliency Prediction in Videos 1

W-Net Plus: Dnn for Spatial Saliency Prediction in Videos

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1st IEEE International Conference on Innovations in High Speed Communication and Signal Processing, IHCSP 2023

作者： Sharma, Gaurav Singh, Maheep Kumain, Sandeep Chand National Institute of Technology Dept. of Computer Science Engineering Srinagar India

ISBN: (纸本)9798350345957

Deep neural networks (DNNs) have been employed recently to identify saliency in images and videos. Different models have been suggested to predict spatiotemporal as well as temporal and spatial saliency. In this study, a model called W-net Plus is proposed for predicting spatial saliency in videos. The newly proposed model not only translates information from the encoder to the decoder but also from the decoder to the encoder, and it is inspired by U -net architecture. The proposed model has been evaluated by the author(s) on various data sets, and they have obtained noteworthy findings for the F - Max, structural similarity (S-measure), and Mean Absolute Error (MAE) measurement parameters, demonstrating its effectiveness and survivability. No stage of the training involves the utilization of transfer learning in the work. © 2023 IEEE.

关键词： Deep neural networks

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