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A Smart Pill Container for Improved Medication

A Smart Pill Container for Improved Medication

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： Thangam S T. Sudeep Reddy Dhanush T. Krithin J. Jesy Janet Kumari Dept of Computer Science and Engineering Amrita Vishwa Vidyapeetham Amrita School of Computing Bengaluru India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Medication non-adherence is a significant problem in healthcare, leading to the gradual worsening of patient health, increased hospitalizations, and higher costs. Current patient-driven solutions rely heavily on the reliability of patient compliance and self-reporting, which are often inaccurate, leaving real-time monitoring and intervention gaps. The proposed system of a smart pill box, integrated with a Raspberry Pi 4, weight sensors, and a camera, addresses these weaknesses. The weight sensors will detect medication intake and verify dosages by sensing changes in weight, while the camera will capture images of the pills. Using a self-made dataset with over 400 images of each of 10 different tablets, the system will employ deep learning to identify the pills and verify dosages, alerting patients and caregivers to anomalies such as skipped doses. This automated adherence monitoring system reduces reliance on self-reporting and increases tracking accuracy. The expected outcomes of this research include improved medication adherence, reduced medication errors, and enhanced monitoring mechanisms, ultimately leading to better healthcare provision and outcomes for patients. Innovating health technology in this manner promises significant improvements in patient care.

关键词： Image sensors Deep learning Medical services Cameras Sensor systems Real-time systems Reliability Monitoring Intelligent sensors Biomedical imaging

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Malware-Induced Data Disturbance: A LeNet Perspective with FGSM and PGD

Malware-Induced Data Disturbance: A LeNet Perspective with F...

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Advances in Electrical, computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： Harshith Katragadda S. Baghavathi Priya Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

Malware, as a pervasive cyber threat, jeopardizes both digital infrastructure security and the broader societal functioning and safety. This paper investigates the resilience of the LeNet convolutional neural network (CNN) against malware-like adversarial perturbations on the Fashion MNIST dataset. Employing the Fast Gradient Sign Method (FGSM) and the Projected Gradient Descent (PGD) as representative attack vectors, simulate malware-induced data disturbances to evaluate their effect on LeNet’s performance. The experiments systematically assess the degradation in classification accuracy as a function of the intensity of adversarial noise. Results demonstrate a notable decline in model fidelity with increasing perturbation strength, accentuating LeNet’s vulnerability to such targeted data corruption. The research emphasizes the necessity for fortified defenses in machine learning infrastructures, particularly in applications where data integrity is critical. The findings of this research aim to catalyze advancements in the robustness of Convolutional Neural Networks (CNNs) against malware-exploited vulnerabilities in data-driven domains.

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Securing Smart Home IoT devices using Hyperledger Fabric

Securing Smart Home IoT devices using Hyperledger Fabric

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International Conference on computing and Networking Technology (ICCNT)

作者： R Pranesh B Lalith Guptha K Aswinkumar B Pramoth K Abirami Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The rapid growth of Smart Home applications has broadened the scope for a wide range of security concerns. Blockchain technology, specifically Hyperledger Fabric, offers a promising solution. The proposed model explores its application, leveraging the Fablo toolkit to enhance the security of Smart Home IoT devices. The decentralized nature of Hyperledger Fabric, with features such as its consensus mechanism and chaincode, helps mitigate common security issues like unauthorized access and device tampering. The resultant tamperproof network, coupled with favorable metrics like latency and transaction throughput, underscores the robustness of the proposed model. Our model promises low latency and high transaction throughput. The metrics assist the model in securely and efficiently handling a large number of transactions without compromising on speed and security.

关键词： Technological innovation Distributed ledger Smart cities Smart homes computer architecture Throughput Fabrics Consensus protocol Security Internet of Things

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Artificial Intelligence in Healthcare: Predicting AIDS Progression with Machine Learning Models

Artificial Intelligence in Healthcare: Predicting AIDS Progr...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Akshay Rajan Gouri Santhosh Balu Manoj Nandana Manohar T Anjali Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

A comprehensive analysis was performed using the AIDS Clinical Trials Group 175 dataset to improve the accuracy of predicting AIDS disease progression. The primary objective was to integrate machine learning techniques to predict AIDS disease outcomes based on clinical, demographic, and treatmentrelated variables. Several machine learning models, including advanced neural network architectures, were developed and thoroughly evaluated. The study highlighted the ability of machine learning to accurately identify patterns and risk factors associated with AIDS disease progression, thereby improving treatment strategies and patient management. Extensive comparisons of several machine learning models were performed to evaluate their performance and robustness. The aim is to demonstrate the important role of artificial intelligence in predicting and diagnosing AIDS early, thereby contributing to better healthcare outcomes. The comprehensive evaluation of model performance in this study is expected to support future advances in prediction models for AIDS and other chronic diseases.

关键词： Accuracy Acquired immune deficiency syndrome Biological system modeling Stacking Machine learning Predictive models Clinical trials Prediction algorithms Robustness Random forests

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Music Recommendation based on Emotions recognized through Speech and Body Gestures

Music Recommendation based on Emotions recognized through Sp...

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Innovation in Technology (INOCON), IEEE International Conference for

作者： Kirti R Nambiar Suja Palaniswamy Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

Throughout history, music has consistently remained a widely enjoyed source of entertainment, and technology has swiftly acknowledged its popularity. The emergence of numerous music streaming platforms has provided users with a plethora of choices, emphasizing the need for a system that simplifies the organization and search management of music. A project utilizing human-computer interaction (HCI) techniques to suggest songs by analyzing the user’s emotions extracted from speech input can significantly enhance the application’s personalization. This approach aims to create a more tailored and emotionally resonant experience for the user, establishing a deeper connection between the music recommendations and the user’s emotional state. However, relying solely on emotions from speech may not be very effective as it is subject to variations across people of different genders, race and culture. Speech emotion recognition may also be ineffective for the speech-impaired people. Hence, the objective of this project is to build a music recommendation system capable of discerning emotions from both body gestures and speech, enabling more comprehensive and nuanced song recommendations. In order to make effective recommendations using minimal amount of samples, meta learning models are used in both gesture and speech emotion recognition. This data is in turn used by the recommendation module. We have used the GEMEP Audio-Video dataset for emotion recognition using speech and body gestures. These identified emotions are then correlated with suitable song moods, forming the basis for the generated recommendations. The Emotion module of the model obtained an accuracy of 78.63% for multi-modal emotion recognition. The recommendation module obtained an accuracy of 98.02%.

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Citation Worthiness Assessment using Deep Learning Techniques

Citation Worthiness Assessment using Deep Learning Technique...

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IEEE International Conference on Electronics, computing and Communication Technologies (CONECCT)

作者： Manjusha R Logkesh M Ashwath V A Sriram R S S Pravin Sabari Bala Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350385922

ISBN: (纸本)9798350385939

In the realm of scientific and technological research, the identification of citation worthiness, which involves determining whether a sentence merits citation, is crucial for scholarly integrity and knowledge dissemination. This paper delves into the intricate process of citation worthiness assessment by focusing on two key steps: identifying the location within a research paper where a citation is required and ranking potential citation candidates through ranked retrieval. To address this task, we employ state-of-the-art techniques, specifically Bidirectional Long Short-Term Memory (Bi-LSTM) and Transformers, alongside traditional models like K-Nearest Neighbours (KNN), Logistic Regression, and Multinomial Naïve Bayes (MNB). Our comprehensive evaluation and comparison of these models reveal the effectiveness and comparative advantages of Bi-LSTM and Transformers in citation worthiness assessment. The findings not only validate the utility of these advanced techniques but also offer insights into their strengths and and constraints, opening the door for additional progress in this domain.

关键词： Deep learning Logistic regression Electric potential Focusing Nearest neighbor methods Transformers Robustness

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

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

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

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

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

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.

关键词： Productivity Visualization Accuracy Image resolution Droughts Crops Convolutional neural networks Stress Water resources Resilience

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Modelling Complex Associations for Image Captioning Using Vision Transformers

Modelling Complex Associations for Image Captioning Using Vi...

引用

International Conference for Convergence of Technology (I2CT)

作者： Chavda Ritul Nalini Sampath Department of Computer Science & Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

Captioning an image is the method of writing a plain English the description of an image. This is a difficult assignment since it calls for the model to comprehend the image's information and to produce correct and fluid text. It has been demonstrated that a novel class of neural network called vision transformers is useful for picture captioning. Vision transformers are able to learn long-range dependencies in images, which is essential for generating accurate and fluent captions. In this project, will explore the use of vision transformers for modelling complex associations in images. The work will investigate how vision transformers can be used to generate captions that are more informative and comprehensive. The work will also explore how vision transformers can be used to generate captions that are more creative and *** work is based on Flickr8k Dataset, which is a large dataset of images and captions. The vision transformer model will be trained on this dataset. The model is then assessed using a held-out test set. Compared to conventional picture captioning models, the vision transformer model will be able to provide captions that are more detailed and insightful. It will also be able to generate more creative and engaging captions. This project has implications for the development of new image captioning models. Vision transformers are a promising new approach for image captioning, and they have the potential to generate more accurate, fluent, informative, creative, and engaging captions.

关键词： Fluids Neural networks Writing Transformers Convergence

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Design of an Automatic Washing Machine Control System using Deterministic Finite Automata

Design of an Automatic Washing Machine Control System using ...

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Global Conference for Advancement in Technology (GCAT)

作者： C Madhuja Shreya Bhanot Srinidhi M D. Radha Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

A Deterministic Finite Automaton (DFA) is a theoretical model of computation used to design and analyze the behavior of systems with a finite number of states, where each state transitions deterministically based on input symbols. This model ensures a clear and predictable progression from one state to another. The integration of Deterministic Finite Automata (DFA) into the control system of new-age automatic washing machines marks a significant leap forward in efficiency and user-friendliness. A logical and well-organized model for the machine’s behavior by detailing every step of the washing process as separate states inside the DFA framework has been created in this project. Based on variables like stain intensity, load size, and user preference the proposed model dynamically modifies its cleaning strategy to ensure best performance while consuming the least amount of water and energy. Furthermore, by incorporating all possible states and transitions, the DFA structure allows the project to resolve possible problems and ensure continuous operation for the duration of the wash cycle. The comprehensive incorporation of all possible states and transitions within the DFA allows for resolving potential issues, ensuring uninterrupted operation throughout the wash cycle. This organized depiction of control logic improves efficiency and reliability and provides users with a clearer understanding of their washing machine’s functionality, enabling seamless operation and maintenance. In conclusion, utilizing DFA in the control system of washing machines facilitates the creation of an error-free machine, enhancing both performance and user satisfaction.

关键词： Computational modeling Energy resolution Automata Symbols Predictive models Control systems Maintenance Washing machines User preference Load modeling

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Smart Surveillence Drone with Navigation System

Smart Surveillence Drone with Navigation System

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computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

作者： A Padmavathi B Vyasasreevedansh Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

At present, in this time of technological advancement and an urgent necessity for advanced spying abilities, the “Smart Surveillance Drone with Navigation System” program is leading the way in redefining the field of surveillance and safeguarding. The way in which this project is leading is through the integration of the most advanced drone technology, complex navigation systems, and real-time computer vision, striving to create an independent drone model that can be identified and followed by human subjects in real-time while travelling between predetermined waypoints. The key aims of the program involve the assembling and integrating of a range of sensors, such as GPS, IMU, cameras, ultrasonic sensors, and LiDAR, alongside the production of independent navigation algorithms and computer vision technology. By combining cutting-edge technologies such as wireless communication, real-time data processing, and machine learning, the revolutionising of surveillance in several domains is being set out by this project. The adaptability and reliability of the system will be verified by rigorous field testing, while the experience will be enhanced by user feedback and training. With such advancements, the future of disaster response, agriculture, search and rescue operations, and security is promised to be innovative and efficient. This project that’s set to change how we think about surveillance and security is captured by this short summary, and the way is being paved for a safer and more secure world.

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