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Security of Examination Question Paper Through Blockchain – SecureQ

Security of Examination Question Paper Through Blockchain –...

引用

2024 IEEE International Conference on Innovation and Novelty in engineering and technology, INNOVA 2024

作者： Solomon Raju, G. Sowmya, K.N. Chennamma, H.R. Dept. of Computer Applications JSS Science and Technology University Mysuru India Dept. of Information Science and Engineering JSS Academy of Technical Education Bengaluru India

ISBN: (纸本)9798331505134

Ensuring the security of confidential information is a daunting task for any concerned authority. Proving the authenticity and integrity of exam question papers is a prime responsibility of examination authorities. Storing and sharing question papers on open networks makes them vulnerable to unauthorized access and alterations. The proposed approach redefines question paper management using the Interplanetary File System (IPFS) and blockchain technology, providing a secure, decentralized, and tamper-resistant platform. This solution addresses long-standing challenges and ensures security, accessibility, and data integrity, establishing new standards for examination management. A user-friendly text editor in the framework allows paper setters to create, modify, and format questions effortlessly while preserving content integrity. The integration of this editor streamlines question paper preparation and enhances both efficiency and security. By leveraging a decentralized framework, the proposed solution ensures that the critical tasks of any concerned authority are handled securely and efficiently, overcoming limitations of conventional methods. © 2024 IEEE.

关键词： Data integrity

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A DNA-based Authentication System for Securing Cloud Data Storage and Transactions 18

A DNA-based Authentication System for Securing Cloud Data St...

引用

18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Kumar, Tarun Kumar, Prabhat Namasudra, Suyel National Institute of Technology Dept. of Computer Sc. & Engineering Patna India National Institute of Technology Dept. of Computer Sc. & Engineering Agartala India

ISBN: (纸本)9789380544519

Cloud computing has grown rapidly, but data breaches and unauthorized account access remain a persistent threat. Commonly used cryptographic authentication methods rely on mathematically complex but computationally insecure protocols. This paper suggests using new developments in deoxyribonucleic acid (DNA) computing to make the cloud safer by using biological authentication protocols that can be used by a lot of people. A DNA-based cryptographic key system is developed for leveraging synthetic DNA's dense information capacity. This approach interfaces biochemically secure DNA signatures with existing cloud architectures to provide multi-factor authentication (MFA), encrypting access to sensitive user data and transactions. The developed DNA cryptography protocols offer resilience against common attacks like password leaks, while the massive parallelism inherent in biochemical reactions allows efficient processing of authentication requests even under immense cloud workloads. This paper proposes the system's security using established threat frameworks, analyzing attack vectors related to storage, access control, data leakage, etc. This interdisciplinary approach highlights how DNA computing's advantages in encryption, steganography, and blockchain can address real-world cybersecurity needs. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Biometrics Cloud Data Security DNA Cryptography Multi-factor Authentication Synthetic Biology

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Exploring Deep Learning Techniques for Automated Cataract Detection in Digital Camera Imagery

Exploring Deep Learning Techniques for Automated Cataract De...

引用

2024 International Conference on Advances in Modern Age Technologies for Health and engineering Science, AMATHE 2024

作者： Kamepalli, Sujatha Veeranjaneyulu, N. Bodapati, Jyostna Devi Vignan's Foundation For Science Technology And Research Dept. Department Of Information Technology And Computer Application Guntur India Vignan's Foundation For Science Technology And Research Dept. Department Of Advnced Computer Science And Engineering Guntur India

ISBN: (纸本)9798350371567

Cataracts, a prevalent eye condition impacting vision, demand accurate and prompt identification for effective risk reduction and blindness prevention. Traditional methods of cataract diagnosis rely heavily on manual examination by ophthalmologists, which can be time-consuming and subject to human error. With the advent of digital imaging technology, there is an opportunity to enhance the efficiency of cataract detection. The deep neural network models VGG19 and ResNet-50 are recommended in this paper for automatic cataract identification on digital camera images. To optimize network performance, adjustments to loss and activation functions were made, incorporating minimal kernels, reduced training parameters, and layers. This resulted in a ResNet-50 model with significantly decreased computational cost and average operating time compared to the cutting-edge models. The proposed ResNet 50 model achieved an average accuracy of 93.41%, outperforming the VGG 19 model, which had an accuracy of 91.13%. This research contributes to the evolution of cataract detection methodologies, emphasizing the potential of deep learning models, particularly ResNet-50, in improving diagnostic accuracy and operational efficiency for enhanced eye health outcomes. © 2024 IEEE.

关键词： Digital cameras

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Breast Cancer Classification Using ANN and ML Techniques 7

Breast Cancer Classification Using ANN and ML Techniques

引用

2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Bhuta, Nishi Raut, Roshani Pimpri Chinchwad College of Engineering Dept. of Computer Engineering Pune India Pimpri Chinchwad College of Engineering Dept. of Information Technology Pune India

ISBN: (纸本)9798350304268

Millions of individuals throughout the world are impacted by the significant health issue known as breast cancer. To improve patient outcomes, breast cancer must be identified early and correctly diagnosed. In this study, we used TensorFlow to create an artificial neural network (ANN) model to categorise breast cancer. To compare with our ANN model, we also employed seven alternative ML classifiers as basis models, including Line SNM, LR, KNN, NB, DT, RF, and Gradient Booster. For each model, we determined the precision, accuracy, recall, and F1 score. To improve performance, we ensembled all the classifiers. Each classifier's accuracy was analysed and contrasted, and the classification performance was demonstrated via a confusion matrix. Our results show that the ANN have given an accuracy of 97% and the ML model achieved the highest accuracy of 98.8% using LR, followed by SVM with 97.7%, KNN with 96.7%, RF with 95.6%, DT with 95.4%, NB with 95.3% and Gradient Booster with 94.2% The ensemble model gave the overall accuracy to 97.7%. These findings indicate that our proposed ANN model and ensemble approach are effective but LR gave the highest accuracy in classifying breast cancer and can serve as a useful tool for clinicians and researchers in this field. © 2023 IEEE.

关键词： Artificial neural network (ANN) Breast cancer Ensemble Learning ML

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Factors on the UTAUT2 to Identify the Influence on the Usage Behavior of AI-Based Air Quality 3

Factors on the UTAUT2 to Identify the Influence on the Usage...

引用

3rd International Conference on Creative Communication and Innovative technology, ICCIT 2024

作者： Aini, Qurotul Hartomo, Kristoko Dwi Dwi Purnomo, Hindriyanto Wahab, Abdul Rahardja, Untung Satya Wacana Christian University Dept. of Computer Science and Technology Salatiga Indonesia Satya Wacana Christian University Dept. of Information Technology Salatiga Indonesia International Islamic University Malaysia Dept. of Information and Computer Technology Kuala Lumpur Malaysia University of Raharja Master of Information Technology Tangerang Indonesia

ISBN: (纸本)9798350367492

This study investigates the effect of AI-based air quality detection apps on user behavior in Indonesia, ranked 26th globally for air pollution. Utilizing an online survey with 287 participants, focusing on 156 active users, and employing Partial Least Squares Structural Equation Modeling (PLS-SEM), the research assesses user satisfaction in environmental monitoring using the Unified Theory of Acceptance and Use of technology 2 (UTAUT2). This model examines 9 key variables: Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), Price Value (PV), Hedonic Motivation (HM), Habit (HT), Behavioral Intention (BI), Usage Behavior (UB), Considered Risk (CR), and Considered Trust (CT). Key findings emphasize the crucial roles of PE and BI in determining user's usage intentions and behaviors. These insights are vital for enhancing air quality monitoring technologies for developers and policymakers. The study also identifies areas for future research, such as exploring external and demographic influences, to further understand user behavior and satisfaction with AI-based environmental tools. Overall, it highlights the importance of user-centric approaches in technological development for improving environmental health outcomes. © 2024 IEEE.

关键词： Environmental monitoring

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Frequency Response Deterioration due to Surface Ridge Abrasion of Tactile Sensors 13

Frequency Response Deterioration due to Surface Ridge Abrasi...

引用

13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Yanwari, Muhammad Irwan Okamoto, Shogo Polines Dept. Computer Engineering Technology Japan Tokyo Metropolitan University Dept. Computer Science Tokyo Japan

ISBN: (纸本)9798350355079

Epidermal ridges are crucial for tactile sensing in both human fingers and synthetic sensors. These ridges are made of compliant materials and susceptible to abrasion damage after iterative use. This study focuses on examining the impact of sensor ridge damage on the acquired signal, which encompasses a broad range of frequency components. We compared the frequency responses of a tactile sensor scanning sandpaper with both intact and damaged sensors. The degradation of signal levels was evident across the 0-1,000 Hz range, with particularly prominent suppression of the peak frequency component. This study demonstrates the frequency-dependent influence of ridge abrasion. © 2024 IEEE.

关键词： Friction

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Forest Fire Prevention and Detection System 5

Forest Fire Prevention and Detection System

引用

5th IEEE International Conference on Power, Electronics and computer Applications, ICPECA 2025

作者： Jayakody, Lasitha Edirisinghe, Chamathka Senasinghe, Tharush Dissanayake, Shaminda Gamage, Narmada Pandithage, Dinithi Wijayaraja, Jaliya L. Sri Lanka Institute of Information Dept. of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798331533694

Forest fires pose significant threats to both the environment and human life, necessitating the development of advanced detection and prevention systems. In this study, we propose an integrated IoT (Internet of Things)-based system for early detection and management of forest fires. The system incorporates a range of sensors including heat, gas, and thermal imaging sensors, along with IoT technology for real-Time monitoring and communication. Utilizing image processing techniques and (ML) Machine Learning algorithms, the system can accurately detect fires, predict their spreading patterns, and analyze environmental conditions. Key features include the deployment of disposable heat sensors for temperature monitoring, IoT sensors for gas detection and weather monitoring, and a centralized processing node for data analysis. Alerts are sent to nearby authorities and residents, while automated firefighting devices are activated to suppress fires until backup arrives. Experimental results demonstrate the effectiveness of the proposed system in early detection and prevention of forest fires. © 2025 IEEE.

关键词： Temperature sensors

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A Study on Apriori based Movie Recommendation 2

A Study on Apriori based Movie Recommendation

引用

2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Behera, Tapas Mahapatra, Sarita Mohanty, Kritik Ranjan Farhan, Mohammad Tripathy, Sachidananda Priyadarshini, Sushree Bibhuprada B. Siksha 'o' Anusandhan Dept. of Computer Science & Information Technology Bhubaneswar India

ISBN: (纸本)9798350370188

With so many movies available nowadays, it might be challenging for people to choose one that suit their preferences and tastes. We have created a system for suggesting movies to users based on their viewing interests and history. A dataset of user movie viewing history and preferences is first gathered and preprocessed by the movie recommendation system followed by employing an Apriori technique to find the common item sets from frequently watched movies. The recommendations are created using these item sets as a foundation. A real-world movie dataset is applied in extended trials for movie recommendation system while considering evaluation metrics like recall, precision, and accuracy. The experimental findings show that the suggested approach is capable of giving users precise and individualized movie suggestions by utilizing the Apriori algorithm. © 2024 IEEE.

关键词： Recommender systems

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Autonomous Social Amenity, Pothole Detection and Reporting 4

Autonomous Social Amenity, Pothole Detection and Reporting

引用

4th International Multidisciplinary information technology and engineering Conference, IMITEC 2024

作者： Mosela, David Olaifa, Moses Owolawi, Pius Malele, Vusi Tshwane University of Technology Dept. Computer Systems Engineering Tshwane South Africa Tshwane University of Technology Dept. of Computer Systems Engineering Tshwane South Africa

ISBN: (纸本)9798350387988

Challenges faced by traffic management in many developing nations across the globe is a result of the poor quality of road infrastructure. Inadequate and defective road infrastructure are major contributors to this effect. A key element that poses significant risk to road safety and that leads to increased road management cost is the increasing number of potholes and inefficient detection and reporting approach. This study provides an in-dept. exploration of the latest developments in pothole detection systems and examines various methodologies and technologies used in these systems, emphasizing the importance of real-time monitoring for proactive road maintenance. In addition, we proposed a system that addresses the persistent challenge of road infrastructure degradation by adopting cutting-edge technologies, including computer vision, machine learning, and sensor networks (such as ultrasonic and GPIO systems) that are used to identify and assess potholes on road surfaces. To achieve the aim of the study, the system adopts the use of RasberryPi 4 Model B for pothole detection and YOLOv4 for image processing. © 2024 IEEE.

关键词： Highway administration

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A Review of Disease Detection in Sugarcane Using Machine Learning and Deep Learning Techniques 15

A Review of Disease Detection in Sugarcane Using Machine Lea...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Pavan, Undavalli Chandra Pateriya, R.K. Tomar, Deepak Singh Singh, Surendra Maulana Azad National Institute of Technology Dept. of computer science & engineering Bhopal India National Institute of Technology Dept. of computer science & engineering Uttarakhand India

ISBN: (纸本)9798350370249

Agriculture has been an active source of food and economic growth, but it faces significant challenges from diseases and climate change. In Indonesia, sugarcane production is severely impacted by viral infections such as Sugarcane mosaic virus (SCMV) and Sugarcane streak mosaic virus (SCSMV). These infectious diseases pose a threat to the world's sugarcane industry, resulting in significant crop losses in nations like Brazil and India, along with other viruses like the Fiji Disease Virus (FDV), and Sugarcane Yellow Leaf Virus (SCYLV). For such diseases to be managed and performance to be maintained, early detection is essential. Disease detection is accomplished by both conventional and cutting-edge approaches, such as molecular, serological, and imaging methods. However, the advancements in Machine learning, Deep learning and computational image processing algorithms have demonstrated significant potential in the early and precise diagnosis of disease. In this study, different methods used for machine learning (ML) and deep learning (DL) for plant disease diagnosis were reviewed and it highlights the need of high-quality datasets, various pre-processing techniques, robust extraction of features, and efficient classification techniques. It also discussed the issues with data collection, symptom variability, over-fitting, and imbalances in classes, along with potential avenues for further advancement in diagnosing diseases systems. © 2024 IEEE.

关键词： challenges deep learning detection hybrid models machine learning Plant diseases

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