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Elevating Login Security with Hybrid Encryption Enhanced QR Authentication 9

Elevating Login Security with Hybrid Encryption Enhanced QR ...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Lahari, Busam Venkata Dhathri, Busireddy Nithin, G. Kavitha, C.R. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350377972

As the digital world continues to grow, the need for robust and secure authentication systems to protect sensitive information has never been more important. This paper introduces a developing approach to login security by combining QR Code technology with the highly regarded RSA Signature Scheme (Rivest-Shamir-Adleman) and Advanced Encryption Standard (AES). This combination aims to significantly improve login security. Traditional username-password based authentication is vulnerable to phishing attacks, password compromise, cyber exploits, intellectual property theft, brute force attacks. On the other hand, RSA has built a strong reputation, mainly because it uses a strong encryption scheme that makes it extremely difficult for the agent to get access to sensitive data. This two-factor authentication system provides security, while ensuring user convenience by merging the RSA and AES algorithms with QR codes. This paper aims to combine QR code-based login consistently with existing digital platforms, using the strengths of both RSA and AES encryption. This dual-layered security approach was created to attain significant advancements in protecting information on an international scale while ensuring ease of use for the end-user. In addition, the paper will address possible challenges such as key management and the development of effective authentication and access control mechanisms, making sure a complete and secure authentication solution. Also, 4 different combinations of algorithms are compared, out of which RSA and AES are the best combinations ever. © 2024 IEEE.

关键词： Authentication Protocol

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An Investigation of Federated Learning Strategies for Disease Diagnosis 15

An Investigation of Federated Learning Strategies for Diseas...

引用

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

作者： Das, Abhay Krishnadas, Aishwarya Krishnan, Vaishakh S. Farida, Avani Sarath, Greeshma Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350370249

Traditional healthcare systems utilize centralized approaches for building machine-learning models for disease diagnosis. It requires sharing raw data to the centralized server, which is practically difficult due to patient's privacy. This leads to significant vulnerabilities and challenges. As a result, switching to approaches for scalable and distributed AI at the network edge for privacy-preserving smart healthcare applications is essential. Deep learning models need to train on data, on any nodes, in a privacy-preserving manner, but not give up the distinction of patients. This paper aims to investigate the application of FL in providing health diagnosis while enhancing the patient's data privacy, by adopting the Flower platform. It employs the following four algorithms for federated learning that can facilitate collaborative model training with shared patient data: FedAvg, FedProx, FedAdagrad, and FedAdam. To evaluate FL's effectiveness, deep learning models trained on decentralized COVID-19 and Pneumonia datasets are used from different sources. FL methods were compared to a Centralized Convolutional Neural Network (CNN) to check the performance. The analysis shared in this paper shows that implementing FL contributes to establishing the subject's accurate diagnostics concerning the patient's data protection in the healthcare industry. © 2024 IEEE.

关键词： Adaptive Optimization Methods Federated Learning (FL) Flower (FL framework) Healthcare Diagnostics

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IoT-Cloud based Intelligent System for Crop Yield Prediction: A Review 15

IoT-Cloud based Intelligent System for Crop Yield Prediction...

引用

15th International Conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Shalini, G. Beena, B.M. Khanna, Manju Department of Computer Science & Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9798331300579

Crop yield prediction is a crucial and challenging task in agriculture due to the complex and non-linear parameters involved. It mainly depends on three fluctuating factors namely soil, weather and environment hence traditional farming methods and statistical methods are no longer feasible to predict the yield precisely therefore Smart farming is gaining more attention with the advent of cutting edge technologies like IoT, Cloud and AI frameworks. Crop yield prediction involves Crop selection, Disease Detection, Weed detection, pest/insect Recognition and Water requirement level to assess the crop quality. This paper intends to provide a literature review on existing techniques to predict the yield of crops using IoT, cloud and Artificial intelligence. The study of 26 selected papers based on exclusion criteria are reviewed chronologically from the year 2017 to 2023 then, outlines the use of different sensors, datasets used in the works considered, simulation platforms, state-of-the art machine learning and Deep learning algorithms applied as well as the performance metrics used to measure the errors. Finally, the research gaps and challenges faced using these cutting edge technologies are defined. © Grenze Scientific Society, 2024.

关键词： Smart agriculture

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Preventing Accident Through Blockchain and IoT 15

Preventing Accident Through Blockchain and IoT

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

作者： Sriram, Suthir Sonar, Pradip Sah Nivethitha, V. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350370249

In today's changing world, the rise in car accidents and deaths on the road is an outcome of increasing traffic and population growth. The World Health Organization (WHO) states that a concerning 1.35 million lives are lost each year due to road accidents with factors like alcohol use, reckless driving and speeding playing roles. To make matters worse a significant 55 percent of road fatalities remain unreported leading to delays in emergency responses and sadly more deaths. To solve this issue, an effective solution emerges through the merging of the Internet of Things (IoT) and blockchain technologies. The proposed system for warning about car collisions combines an Arduino controller with sensors such as accelerometers and vibration sensors. These sensors quickly identify dangers activating the emergency system that uses GPS to pinpoint locations and a GSM module to communicate with emergency services. Immediate alerts are then sent out to designated contacts for help. By incorporating technology into the mix, the efficiency of emergency alerts is further boosted by ensuring communication between vehicles and medical facilities. This integration promotes trust and transparency through encrypted data sharing and verification methods thereby enhancing the reliability of the system. This novel approach significantly reduces fatalities by creating a transparent environment, for emergency assistance. Moreover, the system's cost and easy availability make it suitable for use in all types of vehicles irrespective of their brand or design. Choosing this budget option has promise not just in lowering the number of deaths but also, in improving overall road safety worldwide. © 2024 IEEE.

关键词： Budget control

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A Predictive Framework for Failure Detection and Reallocation in Crowdsourcing Platforms 2024

A Predictive Framework for Failure Detection and Reallocatio...

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16th International Conference on Contemporary computing, IC3 2024

作者： Voleti, Sai Anushree Tathipamula, Harini Sai Poolla, Lakshmi Saranya Nair, Lekshmi S. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798400709722

Crowdsourcing platforms have become essential tools for managing various tasks, relying on distributed labour practices to enhance productivity and goal achievement. However, they often encounter challenges such as unemployment and low-quality task submissions, impeding project effectiveness. Timely resolution of these issues is crucial for project momentum. To address these challenges, we propose a predictive model based on machine learning techniques. Our framework aims to anticipate mistakes and optimize workload reallocation within community spaces. Leveraging state-of-the-art algorithms, our system analyzes employee behaviours, task features, and platform dynamics to detect problematic situations early. Moreover, we introduce a dynamic reallocation technique to expedite project duration while minimizing delays. This method automatically reassigns tasks to alternative tasks. The experimentation results exhibit that our framework has shown that it is better than other methods at accurately detecting failures and reassigning tasks. Furthermore, our framework's adaptability enables its application across various crowd-sourcing fields, including data annotation and content moderation. Described as a proactive response to failures within crowd-sourcing platforms, our predictive framework offers a promising solution to enhance project efficiency. © 2024 Copyright held by the owner/author(s).

关键词： Crowdsourcing

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Thyroid Disease Prediction Using Machine Learning Techniques

Thyroid Disease Prediction Using Machine Learning Techniques

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2024 IEEE International Conference on Electronic Systems and Intelligent computing, ICESIC 2024

作者： Kumar, Sompalli Pranath Reddy, Pasuladi Narsimha Nivethitha, V. Sriram, Suthir Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331522988

Thyroid disorders are primarily observed in people in the age range of 30 to 60 years;however, the disease is said to be very prevalent in women. Some of the risk factors for thyroid disorders include other diseases that are autoimmune in nature, a family history of thyroid disease, and past exposure to radiation of the neck. More correctly, thyroid diseases are meaningful metabolic disorders and determinants of health. Toward this end, this study used a large dataset from the patient medical record to predict the onset of thyroid disease. The models that are used in this work are K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), Logistic Regression, and Naive Bayes. Further each of the models' performances is also assessed based on recall, accuracy, and precision. The MLP model developed a good predictive ability in comparison with the other models, along with an accuracy of 99.4 percent. Therefore, this study puts forward a very important message concerning the vital importance of machine learning. © 2024 IEEE.

关键词： Support vector regression

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Code Confabulator: Harnessing LLMs to Compile Code for Visualization 15

Code Confabulator: Harnessing LLMs to Compile Code for Visua...

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

作者： Rayvanth, Nannapaneni Pranavi, Pasupuleti Suryaa, E. Challa, Venkatahemant Kumar Reddy Belwal, Meena Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

Code visualization tools play a core role for visualization of the code and helping novices in understanding, analyzing, and optimization of their programs. Tools such as Algorithm Visualizer and PythonTutor are developed to make algorithm visualization possible across different programming languages like Python, Java, C, and C++. Despite their advantages, these tools suffer from unnecessary data collection, increased time complexity, and limitation in visualizing a few data structures. In our research, we want to introduce a more humanlike view of understanding and solve the shortcomings mentioned above by proposing a framework, which will make use of LLMs for extracting data from the code in Python, Java, C, or C++ to be visualized. In our framework, we have used LLMs instead of the traditional compiler for feasibility and adaptability. Our objective is the appropriateness evaluation of LLMs for this task, starting from freeform prompts. The LLM is asked to generate outputs in a given format;hence, these outputs are taken and converted into visualizations with a rule-based approach. The task will be done to enhance quality visualization and reduce time complexity while allowing the user to view the code at a human level view. We have also publicly released the framework so that people are able to provide further contributions and improvements. © 2024 IEEE.

关键词： Code visualization Large language Models

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Identifying the Influences Behind the LinkedIn Posts using Topic Modeling and Sentiment Analysis 2

Identifying the Influences Behind the LinkedIn Posts using T...

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2nd IEEE International Conference on Trends in Quantum computing and Emerging Business Technologies, TQCEBT 2024

作者： Nagaraj, R. Rohith Adithya, C.R. Sri Chakra Teja, Sakalabathula Deepika, T. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9798350384277

The proliferation of social networking in the modern world has made it a ubiquitous presence in people's lives. On a daily basis, individuals post and share their opinions, rating products, and engage in business-related activities. Social media platforms play a crucial role in the business field, facilitating the establishment of relationships with prospects and clients. As such, LinkedIn has emerged as the ideal online platform for building trust by sharing success stories, business promotions, and recommendations through posts. This professional website serves as a means of connecting the world's professionals with the general public. At the same time, many people post opinions and professional content regularly, and only a select few post become influenced by reaching larger community. The factors responsible for this influence remain unknown, prompting us to propose a solution based on topic modeling and sentiment analysis. Our project aims to identify the influences behind LinkedIn posts by examining the role of media content in posts, and by utilizing natural language processing techniques to analyze the underlying aspects of posts which then examined using topic modeling and sentiment analysis to identify subtopics and aspects present in the posts. © 2024 IEEE.

关键词： Modeling languages

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Enhanced Pomegranate Grading Using InceptionResNetV2 with Transformer Integration

Enhanced Pomegranate Grading Using InceptionResNetV2 with Tr...

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2024 IEEE International Women in engineering (WIE) Conference on electrical and computer engineering, WIECON-ECE 2024

作者： Kiran, V. Gopi Babu, T. Hemanth Vidula, N.A. Singh, Rimjhim Padam Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331535476

Sorting pomegranates based on quality grades is a crucial stage in their export preparation and packing process. Traditionally reliant on manual sorting methods which are time-consuming, prone to inaccuracies and incur very high costs due to the need for a large workforce prove to be very inefficient. Moreover, manual grading of export-quality of foods poses additional challenges. This paper addresses such issues by proposing an automated system employing modern digital image processing and deep learning techniques for pomegranate quality grading and classification. Initially, pomegranate images are subjected to several augmentation techniques like extracting HSV color features, Local Binary Pattern features, horizontal and vertical transformations, etc. Then a novel classification model having InceptionResNetv2 convolutional network as its backbone and integrated with optimally placed transformer blocks is proposed. The augmented dataset is utilized to train six state-of-art deep learning models namely, Vision transformers, XceptionNet, EfficientNetV2B0, DenseNet121, InceptionResNetV2 and NasNet-Large. It is observed that the proposed InceptionResNetV2 model containing transformer blocks exhibited superior performance, achieving an accuracy of 90.56%. This underscores the ability of the proposed deep learning model in automating quality evaluation and sorting procedures for pomegranates. © 2024 IEEE.

关键词： Deep learning

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Bone Fracture Detection Using YoloV9

Bone Fracture Detection Using YoloV9

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2024 International Conference on Intelligent and Innovative Practices in engineering and Management, IIPEM 2024

作者： Nivethitha, V. Sriram, Suthir Praadeep, G Kesava Prarthana, S. Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amrita School of Computing Chennai India

ISBN: (纸本)9798350390049

This paper presents the design of a Bone Fracture Detection System at the forefront of YOLOv9-T concerning the detection and localization of the actual site of bone fracture in X-ray images. The system would help quickly and reliably in giving a fracture diagnosis, and that is a critical side of health care sometimes it has the power to actually determine the result of some treatment. This included elbow, finger, forearm, humerus, shoulder, and wrist fractures. Efficiency and accuracy of object detection by the model were improved with the use of the YOLOv9-T on object detection tasks. With system bounding box annotation, the testing accuracy reached up to 96.50%. This study, therefore, highlights the needs for developing more advanced models of machine learning into the workflow of medical imaging probably that could be leading to lesser numbers of diagnostic errors and faster time lines in treatments in the clinic. Results The system proposed by this study is likely to be helpful in a medical practice where the efficiency as well as the accuracy about the diagnosis may be augmented. © 2024 IEEE.

关键词： Diagnosis

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