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Enabling technology Integrated Learning for Autistic Children Using Augmented Reality Based Cognitive Rehabilitation

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SN computer science 2024年第1期5卷 151页

作者： Abdullah, A. Sheik Karthikeyan, J. Gomathi, V. Parkavi, R. Rajarajeswari, P. School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering National Engineering College Kovilpatti India Department of Information Technology Thiagarajar College of Engineering Madurai India

Augmented reality (AR)-based rehabilitation is an excellent teaching tool (ICT tool) for simulating daily activities for autistic children. Children with autism may become more mentally capable with this type of AR-based rehabilitation. It is intended to observe the children's performance in terms of concentration, attention, and identification. The observation has been done through placards as a target image to display the 3D objects on a mobile phone or tablet. In this project, observations are made for 21 autism children in the age group of 7–14, out of whom 17 are boys and 5 are girls. Those 21 children are given practice identifying 15 different objects in an augmented reality environment. Their performance was initially evaluated using conventional instructional techniques. The majority of the kids were having more difficulty identifying things during that observation. Then, with an Augmented Reality environment, the identical observation has been made once more. Using a mobile device or tablet, the 3D objects from the provided placard photos are produced in an augmented reality environment with animation and voice in the languages of English and Tamil. Children with autism are able to recognize and also grasp the behaviors of those objects while viewing them in 3D. Their efforts are measured using a two-point scale (0, 1, 2). The pre-assessment and post-assessment reports for the above observations are tabulated. All the observations are made in the presence of the special education teacher (therapist). However, the children observed in this project fall into three different categories: mild, moderate, and severe. In the Mild category, statistical significance is evident with p values of 0.002 in pre-assessment and 0.014 in post-assessment. Likewise, in the Moderate category, where p values are 0.023 in pre-assessment and 0.033 in post-assessment, significance is observed, as all p values fall below the chosen significance level of 0.05. This leads to rejecti

关键词： Augmented reality (AR) Autism ICT tools Observation Rehabilitation Teaching methodology

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Review of AI & XAI-based breast cancer diagnosis methods using various imaging modalities

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Multimedia Tools and Applications 2025年第5期84卷 2209-2260页

作者： Karthiga, R. Narasimhan, K. Thanikaiselvan, V. Hemalatha, M. Amirtharajan, Rengarajan Department of ECE SRM Institute of Science and Technology Ramapuram Chennai600089 India School of Electrical & Electronics Engineering SASTRA Deemed University Thanjavur613 401 India School of Electronics Engineering Vellore Institute of Technology Vellore India Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia

Breast cancer is a significant health concern;early detection and treatment are critical to improving patient outcomes. Artificial Intelligence has the potential to assist healthcare professionals in the diagnosis and treatment of breast cancer, and clinical decision support systems using AI algorithms can help to improve the accuracy and speed of breast cancer detection and diagnosis. By analysing large amounts of data from various imaging modalities, AI algorithms can help identify breast cancer early, increasing the chances of successful treatment. This survey aims to briefly overview the most prevalent breast cancer types, their staging, and the methods and modalities for their diagnosis to address these problems. Our investigation showed that, when compared to ML, DL techniques achieved the highest accuracy, with an approximate 2% improvement in accuracy. This review article mainly focuses on four commonly used imaging modalities: Mammogram, Thermogram, Ultrasound imaging and histopathology images. This survey critically analyzes breast cancer detection approaches and modalities, as well as a cost-to-accuracy comparison. Explainable AI (XAI) techniques and their application for breast cancer diagnosis are critically examined, as well as their advantages and limitations. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Lung cancer

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A HARVESTING IDENTIFICATION FOR SMART HYDROPONICS AGRICULTURAL SYSTEM DESIGN USING AGRO MACHINE LEARNING MODEL

A HARVESTING IDENTIFICATION FOR SMART HYDROPONICS AGRICULTUR...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Madiajagan, M. Atyam, Nageswara Rao Moharana, Rajeeb Lochan Gopal, Vidhya Prabhukumar, S. Singh, Barinderjit School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798350305708

The technologies on them have also increased in recent developments in the field of agriculture. Many technologies are being introduced, especially for its yield and harvest. Those agricultural products also give good returns when harvested at the right season. Thus the farmers can also do the work required for the next crop. But when the season fails, agricultural products face a variety of dangers. Thus making it impossible to harvest at the right time. An improved agricultural machine learning system has been introduced in this paper. This method is designed to calculate the planting period of the crop, the period of application of required fertilizers and nutrients and the period of harvest and to provide the required instructions to the farmers based on it. This will benefit the farmers and the harvest of the crop will take place in a timely manner. © 2023 IEEE.

关键词： agricultural machine learning agriculture farmers fertilizers harvest season fails yield

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Handwritten Signature Forgery Identification and Prediction using Harris Corner Detection and Siamese Network 2

Handwritten Signature Forgery Identification and Prediction ...

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2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Anitha, A. Priya, M. Kamaraj, Balakrishnan Yadav, Niraj Kumar Srivastav, Subhojit School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Madurai Medical College Tamilnadu Madurai India Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350328202

Signature is a powerful identity of a person for various legal acts, to authorize a transactions to avoid financial crimes. Now-a-days, we are using touch-sensitive pad for the verification of the signatures. Thus there is high risk of forgery to happen due to replicate the signature patterns. So it is essential to detection and verify the signatures while using public platforms to avoid crime of accessing the personal data. The advancements in image processing and artificial neural network helps in identifying and validating the real signature with the fack ones. This paper proposed a signature validation process using harris corner detection and Siamese neural network. Harris corner detector helps to extract the corners and infer the features of an signature image, whereas Siamese network identifies the pattern of signature by learning with the help of similarity function by taking the probability of the signature images. The proposed model achieves better accuracy of 98% for the new signature identification to the original signatures. © 2024 IEEE.

关键词： Neural networks

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A Federated Learning Approach to Converting Photos to Sketch

A Federated Learning Approach to Converting Photos to Sketch

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International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2022

作者： Meedinti, Gowri Namratha Popat, Anannya Gupta, Lakshya School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9789819909803

Worldwide, communication frequently involves the use of images. Numerous applications use images, such as medical imaging, remote sensing, educational imaging, and electronic commerce. In the age of information and the introduction of big data, we require a greater level of security protection for our photographs. Since public networks are used to transfer and view our photographs, in order to train models using input from several clients without forcing each client to provide all of their data to a central server, federated learning (FL), a decentralized, privacy-preserving machine learning technique, is used. In this paper, we provide a novel method that employs machine learning (ML) to train the camera filter and provide generic filter capabilities while taking privacy concerns into account and using the concepts of federated learning and auto-encoding with the help of the CUHK Face Sketch Database (CUFS) dataset to transform any given image into a sketched representation of itself. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Encoding (symbols)

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Checking the Truthfulness of News Channels using NLP Techniques

Checking the Truthfulness of News Channels using NLP Techniq...

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2023 International Conference on the Confluence of Advancements in Robotics, Vision and Interdisciplinary technology Management, IC-RVITM 2023

作者： Vipin, C. Harshit, S. Sailesh Kumar, K.L. Vijay, K. Zaid, Mohamed Vellore Institute of Technology Department of Computer Science and Engineering Vellore India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350306088

Data analytics are difficult with unstructured data since it lacks pre-formatting, which makes up a large amount of information on social media. Research has looked into the potential of machine learning and natural language processing to interpret unstructured data and uncover significant information that could have gone unnoticed. We classify social media users using segmentation approaches on the basis of population, relationship status, and other criteria, allowing for the recognition of prominent people within particular categories. Behavior analysis assigns distinct roles to social media participants, such as users, recommenders, prospective users, and detractors, facilitating the tailoring of messages and reactions to address their concerns effectively. The study involves the accumulation of data from three news channels, data cleaning, and exploratory data analysis (EDA) to unearth patterns. Sentiment analysis is performed, and its results are compared for each of the news channels. Topic modeling is used to reveal different themes in the data. This study offers a solid basis for sentiment analysis and visualization, providing insightful information on the sentiment levels across various source kinds and features. It holds the potential to empower government agencies and businesses in harnessing the power of social media analytics for informed decision-making and improved communication strategies. © 2023 IEEE.

关键词： Visualization

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IoT-Enabled Ethanol Detection System: A Transformative Stride in Ensuring Food Authenticity

IoT-Enabled Ethanol Detection System: A Transformative Strid...

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2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Deenadayalan, T. Deepakraj, E. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350315127

In the present era, the rapid rise of IoT technology has brought forth innovative solutions, particularly in ensuring food safety. This study introduces an IoT-based ethanol detection system tailored to preserve the quality and security of food products. Ethanol, a key indicator of food spoilage, can endanger consumer well-being in excessive amounts. Food adulteration with hazardous substances like ethanol poses a serious public health challenge;the Food Safety and Standards Authority of India (FSSAI) reported that 30% of tested food samples (2017-2019) exceeded safe ethanol levels. Addressing this, we present an IoTbased Ethanol Detection System (IoTEDS), merging sensitive ethanol sensors and IoT tech. IoTEDS monitors ethanol levels in real-time across the food supply chain, enabling remote data analysis and swift risk detection. Through advanced algorithms, the system processes sensor data, generating precise alerts to prevent compromised product distribution. This tech enhances food safety, cuts losses, and safeguards public health. By incorporating touch-sensitive ethanol sensors with IoT capabilities, this research pioneers modern quality assurance, fostering transparent supply chains and industry accountability. IoTEDS' real-time monitoring, data analysis, and risk mitigation position it as a transformative stride in ensuring food authenticity. © 2023 IEEE.

关键词： Quality control

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An Analysis of Cotton Crop for Detection of Pests and Diseases Using ML and DL Techniques

An Analysis of Cotton Crop for Detection of Pests and Diseas...

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2023 IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2023

作者： Susheel, K. Sai Rajkumar, R. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9781665462631

Agriculture is a significant source of energy that includes several well-known crops that supply food for the population and income for agronomists in India. Cotton is one of the attractive, drought-tolerant crops that can be cultivated commercially in every environment, among many other renowned crops. Although, this cotton harvest is plagued by many diseases and pests that may vary depending on climatic conditions, resulting in significant yield losses. The most liable to pests and diseases are the leaf and seedling stages of the plant, which destroy the whole crop. The cotton pests and diseases based on prior knowledge of IP, ML, and DL techniques are analyzed. Along with studies the comparison of the cotton crop with other crops, and also analyses the status of the area, production, and yield of the cotton crop. Eventually, the SVM classifier models applied fewer portraits than different algorithms regarding reliability and efficiency, while the multi-dimensional convolutional neural network provided better accuracy and effectiveness than further models in this deep analysis. © 2023 IEEE.

关键词： Cotton

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A Trust-based Framework for the Detection and Avoidance of Blackhole Nodes in Opportunistic Networks 9

A Trust-based Framework for the Detection and Avoidance of B...

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9th International Conference on Signal Processing and Communication, ICSC 2023

作者： Kundu, Protyasha Saha, Swarnadeep Kundu, Anindita Barai, Smritikona Department of Computer Science and Engineering Heritage Institute of Technology Kolkata India Department of Software Systems School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350383201

An Opportunistic Network (OppNet), as opposed to a ubiquitous centralized network, relies on sporadic and opportunistic encounters between nodes to facilitate communication. The uncertainty about the node's nature presents challenges when nodes with malicious intents are present in the network. One category of malicious nodes includes blackhole nodes, which receive messages but drop them intentionally. In this paper, we introduce a novel trust function designed to detect blackhole nodes within the network. Our function has been widely tested and simulated to validate and increase the efficacy of transmission of messages in the network. We have performed a comparative analysis with various protocols (PROPHET, Epidemic and Spray And Wait) on several parameters such as Delivery Ratio, Overhead Ratio and Number of Messages dropped. The results demonstrate that our trust function is able to correctly detect and avoid blackhole nodes. We observed significant improvement in delivery ratio in Epidemic Routing (47%), Prophet (41%), and Spray And Wait Routing (17%) protocols when adjusting blackhole nodes while keeping the total network nodes constant. © 2023 IEEE.

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AIoT Enhanced Night Vision Object Detection for Vehicle Safety Using Transfer Learning on Raspberry Pi 5 7

AIoT Enhanced Night Vision Object Detection for Vehicle Safe...

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7th International Conference on Internet Applications, Protocols, and Services, NETAPPS 2024

作者： Sujatha, R. Chavan, Kunal Singh, Shivang Balaji, Keertan School of Electronics Engineering Vellore Institute of Technology Vellore India Department of Computer Science S.R.M Institute of Technology Chennai India

ISBN: (纸本)9798331519513

We are now in the twenty-first century, but car safety issues persist, ensuring vehicle safety, especially in low-light conditions, is vital for a modern transportation system. Among these challenges, one of the major problem is detecting objects clearly and accurately at night, which significantly affects driver’s response time and overall *** primary idea behind this Artificial Intelligence of Things (AIoT) based object detection project is to utilize the computational capabilities of the Raspberry Pi 5 as well as the YOLO (You Only Look Once) v5 through v8 advanced object detection algorithms. The goal is to achieve higher performance and efficiency of the vehicular object detection system without increasing computational resources. The experiment is implemented with the help of an infrared camera to grab low-light images, which are further pre-processed and then input into the YOLO models that were previously trained using transferred learning. The software part is automatic. By using the system time, the system turns on the night mode when it’s dark and switches between traffic and highway modes when necessary. To this end, the technology is scalable with fine-grain identification and positioning (DLA-Id). This setup involves the use of the latest version of a Raspberry Pi 5, an infrared camera, a buzzer, a breadboard, and an LCD, all of which have been integrated and form a compact and efficient prototype. The results indicate that the device facilitates real-time object detection and distance calculation an accuracy within ±20 cm, along with identifying lane kinds and directions under all meteorological and driving situations, demonstrating its reliability under all meteorological and driving conditions . © 2024 IEEE.

关键词： Deep learning

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