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Sudoku Solver Algorithms: A Comparative Analysis 1

Sudoku Solver Algorithms: A Comparative Analysis

引用

1st International Conference for Women in computing, InCoWoCo 2024

作者： Manyam, Yashaswini Sundaram, Srinidhi Sahay, Apurvan Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798331518943

The exploration of advanced algorithms for solving Sudoku puzzles reveals unique insights into their performance and effectiveness. The study analyzes and compares effectiveness in solving Sudoku puzzles with the classic backtracking method, an innovative approach based on ant colony optimization, and advanced constraint propagation algorithm. The project evaluates the performance of these algorithms about solution quality, computation time, and memory usage concerning the complexities of different puzzles: $9\times 9,\ 16\times 16$, and $25\times 25$ grid dimensions. Each individual of the different grid sizes tests time and space computations for each algorithm. These algorithms are putting up certain types of strengths and weaknesses in practical applications. The results, on average, produce that out of all, the ant colony optimization algorithm, which gives a good overall performance in terms of time and memory. © 2024 IEEE.

关键词： Optimization algorithms

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Tri-Model Synergy: Redefining Depression Detection through the Convergence of ANN, CNN, and SVM 5

Tri-Model Synergy: Redefining Depression Detection through t...

引用

5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： George, Angelina Baniya, Achal Jose, Alphonsa Kumar, K. Dinesh Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798331504403

Depression being a universal mental health challenge that affects millions globally, necessitating reliable detection methods in order to aid in timely intervention. Like physical health, mental health is not to be ignored. This study presents a novel hybrid model that combines Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Support Vector Machines (SVM) to analyze and detect depression in individuals. The hybrid model achieved a training accuracy of 95.6% and a test accuracy of 91.2%, demonstrating its robustness and reliability. The total training time was recorded at 3.01 seconds, with ANN contributing 1.11 seconds and CNN contributing 1.9 seconds. Furthermore, the model's performance metrics included an F1-Score of 0.91 and 0.91 for detecting the classes of absence and presence of depression respectively, highlighting its effectiveness in correctly identifying depressive states. This hybrid approach provides a comprehensive analysis by leveraging the strengths of each individual model, paving the way for more reliable and scalable mental health assessment tools. © 2024 IEEE.

关键词： Convolutional neural networks

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Enhancing Cervical Cancer Diagnosis: Deep Learning and Explainable AI in Multiclass Image Classification 1

Enhancing Cervical Cancer Diagnosis: Deep Learning and Expla...

引用

1st International Conference for Women in computing, InCoWoCo 2024

作者： Shreya, Kuthati Divakarla, Lasya Priya Kumar, Kanderi Johith Singh, Tripty Afnaan, K. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331518943

Cervical Cancer is a major health concern amongst many women around the world. Cervical Cancer is usually caused by the Human Papilloma Virus (HPV). This causes unusual growth in the cervix region. All though this is such a serious health concern, proper treatment and diagnosis are not that prominent and available to the patients. Regular checkups and tests are quite necessary which can help in early detection and diagnosis. It has been observed if these cancerous growths are detected at an early stage then the patient has a high recovery rate. If not detected early then there are chances that these cancer cells spread to the other organs which can be a fatal condition for the patient. A technique has been introduced involving the use of deep learning models to categorize cancer cells into four distinct classes, aiding in accurate identification and effective diagnosis. Explainable AI techniques like Local Interpretable Model Agnostic Explanations (LIME) have been used with these models in order to identify the regions in the medical images that contribute towards the image classification of the cancer cells which can further provide a better understanding for performing diagnosis. © 2024 IEEE.

关键词： Lung cancer

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FishNet Freshwater Fish Disease Detection using Deep Learning Techniques 2

FishNet Freshwater Fish Disease Detection using Deep Learnin...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Sanjay Kumaar, A. Vishnu Vignesh, A. Deepak, K. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350371314

Fish diseases pose significant challenges to aquaculture, causing economic losses and environmental issues. Early and accurate detection is important for effective management and prevention of diseases. This study proposes using three deep convolutional neural network architectures: Inception, VGG16, and a new CNN model called Fishnet. The efficacy of the models is assessed in two scenarios: using transfer learning and without it. The new FishNet model outperforms the pre-trained VGG16 network, demonstrating a 2% improvement in accuracy for disease detection in freshwater fishes. © 2024 IEEE.

关键词： Convolutional neural networks

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Deep Learning-Driven Low-Resolution Thermal Image Reconstruction for Enhanced CWSI Analysis 5

Deep Learning-Driven Low-Resolution Thermal Image Reconstruc...

引用

5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： George, Anzen T. Deepak, Devithaa M. Kunnath, Gayatri Pradeep Devi, Gowri Parvathy Sarath, S. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350379945

The timely detection of drought stress in chili plants is a prerequisite for the healthy life of the crop and maximum production. This study proposes a new method to address the problem with the help of deep learning for the purpose of effectively identifying drought resistance in chili plants and focus on the detail analysis of plant health. We intend to use deep learning frameworks to design Convolutional Neural Network (CNN) models to upscale the low-resolution thermal images of chili plants to their high-resolution images. CWSI is computed based on the generated images, further employed to evaluate drought stress in chili plants. A real-world dataset of chili plants under various water stress circumstances will be gathered in order to accomplish this. The Crop Water Stress Index(CWSI) can be computed using the high-resolution thermal images generated via the CNN models. Compared to conventional visual inspection techniques, CWSI offers a more objective and accurate assessment by providing a quantitative measure of a plant's water stress level. Our method seeks to greatly increase the accuracy of drought resistance evaluation in chili plants by enhancing the integrity and visual clarity of low-resolution thermal images. Improved accuracy and effectiveness in agricultural monitoring and resource management strategies will follow from this. Ultimately, it could empower farmers to make data-driven decisions about irrigation techniques, maximizing water use effectiveness and guaranteeing the productivity and long-term health of their chili crops. © 2024 IEEE.

关键词： CNN CWSI ESPCN RDN SRCNN

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Cloud-Based Remote Monitoring and Management for Diabetic Patient Healthcare 2

Cloud-Based Remote Monitoring and Management for Diabetic Pa...

引用

2nd International Conference on Distributed computing and Optimization Techniques, ICDCOT 2024

作者： Thangam, S. Niranjan, D.K. Chetan, Kulala Venkat Sujan, Gontla Shreyaa, Hothur Satya Sai Chakresh, Mamidisetti Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350382952

The rising frequency of diabetes necessitates advanced technologies for timely monitoring, enabling visionary operation. The proposed methodology introduces a new approach by integrating sensor data collection, cloud-predicated storage, and machine learning models to enhance diabetes monitoring, visualization, and real-time monitoring of patient's health conditions. In the realm of diabetes operation, there exists a critical need for advanced systems that go beyond traditional periodic assessments and enable real-time monitoring of health parameters. The current terrain lacks a comprehensive result that seamlessly integrates sensor-predicated data collection, cloud-predicated storage, and machine learning models to give immediate perceptivity into dynamic physiological changes associated with diabetes. Addressing this gap is imperative to enhance the perfection and promptitude of interventions. In the proposed system non-invasive detectors such as the Pulse detector, TCS 3200, are used to calculate Blood Pressure and glucose levels. The acquired sensor data is transmitted to a cloud-predicated platform and a robust machine-learning model ensures real-time analysis for responsive and reliable monitoring. The approach leverages machine learning models like Random Forest Classifiers to produce a robust model. The objectification of cloud computing enhances scalability, effectiveness, and real-time processing. The study has redounded in the real-time monitoring of data which is visualized and duly studied by a health care professional enabling to monitor and improve the health conditions of a patient. © 2024 IEEE.

关键词： Machine learning

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Deep Learning based Terrain Classification of Mars Raw Images using UNet and FCN Models 18

Deep Learning based Terrain Classification of Mars Raw Image...

引用

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

作者： Samhitha, D. Sai Ashwini, K. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science Engineering Chennai India

ISBN: (纸本)9789380544519

The exploration of Mars has consistently been of utmost significance in understanding planetary development, suitability for life, and the possibility of establishing civilizations. Precise classification of Martian terrain is essential for effective navigation, identification of resources, and prevention of potential dangers, to enhance the effectiveness of Mars missions. Traditional methods of terrain categorization frequently depend on manual examination and have constraints in terms of scalability and precision. This paper introduces an innovative method that utilizes deep learning algorithms to completely transform the process of classifying Martian terrain. This study employed the Unet and FCN models to categorize the Martian surface by utilizing a wide variety of images obtained from the AI4MARS dataset, from which a collection of 5K to 15K images are extracted. The results show the effective performance, the FCN model attained an Intersection over Union (IoU) score of 73%, while the Unet model acquired a score of 60%. The results emphasize the capacity of deep learning to revolutionize the classification of the Martian landscape, providing a more precise and adaptable answer in contrast with traditional approaches. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： AI4MARS Deep Learning Fully Convolution Network (FCN) Mars Exploration ResNet50 Terrain Recognition UNet

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Optimising Energy Efficient Routing protocols in Wireless Sensor Networks - A comparative study 2

Optimising Energy Efficient Routing protocols in Wireless Se...

引用

2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Manideep, Thotapalli Sri Surya Rohan, Gamidi Saragadam, Sneha Sahay, Apurvanand Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350368413

This study presents a comparative overview of optimization techniques that include, among others, Ant Colony Optimization, Firefly Algorithm, Bat Algorithm, Bee Colony Optimization, Q-learning, Dynamic Programming, and Branch and Bound Algorithm. Addressing each domain in detail, explanations will be offered for the methods described: for example, ACO's foraging behavior, Firefly's light attraction, Bat's echolocation, and BCO's collaborative approach. The Travelling Salesman Problem (TSP) is treated with the DP algorithm and Branch and Bound. Different tests in each algorithm are made, and their efficiency and weaknesses are approximated to guide the selection of a proper approach to be used in future optimizations. This research highlights the strengths and drawbacks of each method, thus enhancing the understanding of how these optimization techniques operate in various fields and contributing to the informed selection of algorithms for specific problems. The results provide practical insights and suggest directions for further improvement and application in real-world scenarios. © 2024 IEEE.

关键词： Ant colony optimization

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Modeling Temple Run Gameplay with Finite State Machines 3

Modeling Temple Run Gameplay with Finite State Machines

引用

3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Datta, P. Tapan Lalith Kumar, D. Tanoj, S. Radha, D. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350364569

The ambitious task of implementing the popular Temple Run game in a new approach that is used to create a version of the Temple Run game that is represented in Non-Deterministic Finite Automata (NFA) and Deterministic Finite Automata (DFA). Temple Run, celebrated for its dynamic gameplay and constantly changing obstacles, offers an ideal platform for these automata models in gaming mechanics. The initiative focuses on using Non-Deterministic Finite Automata to generate diverse and evolving pathways within the game environment, ensuring players encounter a variety of challenges and opportunities. Conversely, Deterministic Finite Automata will regulate the behavior of in game elements like obstacles, abilities, and enemies, establishing clear rules and patterns for their movements and interactions. Through the integration of both NFA and DFA models, an immersive Temple Run experience is aimed to be created, seamlessly combining unpredictability with structured gameplay, ensuring each gaming session is both engaging and rewarding for players. © 2024 IEEE.

关键词： Digital elevation model

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Energy Conservation Using Piezo Electric Effect Through Modern Technology 9

Energy Conservation Using Piezo Electric Effect Through Mode...

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

作者： Chinnasamy, P. Deepalakshmi, P. Durga, Kanaparthi Dhathri, Kammarapalli Sri, Kanchukommula Sai Lavanya, Kandagaddala Naga Lakshmi School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India

ISBN: (纸本)9798350377972

This paper seeks to establish the energy conservation by using piezo-electric effect through some Modern Technology's and the project aims to investigate the application of modern technology in various fields to reduce energy consumption and increase energy efficiency. We focus on three key areas: buildings, transportation, and industry. In each of these areas, we identify the most significant energy consumption sources and potential opportunities for energy savings using modern technology. This aims to explore innovative solutions for sustainable energy generation by harnessing mechanical energy from human movement. This research focuses on integrating piezoelectric cells into everyday footwear and flooring tiles to convert mechanical stress into electrical energy. The project involves designing and fabricating prototypes of energy-harvesting shoes and tiles. The efficiency of energy conversion, the durability of the materials, and the overall feasibility of integrating such systems into daily life will be evaluated. The study will also address the scalability of this technology and its potential impact on reducing the reliance on traditional power sources. © 2024 IEEE.

关键词： Piezoelectricity

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