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Early and Automated Diagnosis of Dysgraphia Using Machine Learning Approach

SN computer science 2023年第5期4卷 523页

作者： Agarwal, Basant Jain, Sonal Beladiya, Kishan Gupta, Yash Yadav, Avijit Singh Ahuja, Neelu Jyothi Department of Computer Science and Engineering Central University of Rajasthan Ajmer India PG Department of Computer Science and Technology Sardar Patel University Vallabh Vidhyanagar India Department of Computer Science and Engineering Indian Institute of Information Technology Kota Kota India Department of Computer Science School of Computer Science University of Petroleum and Energy Studies Dehradun 248007 India

Dysgraphia is a handwriting problem that impairs a person’s ability to write. Even the diagnosis of this condition is challenging, and there is currently no cure. Researchers from all over the world have studied this issue and offered several solutions. Motivation to work on this problem did arise after meeting with a few students struggling in achieving performance despite putting in sincere efforts. This paper also discusses the various forms of dysgraphia and its associated symptoms and proposes machine-learning models to detect dysgraphia. Unsupervised machine learning techniques are used to detect dysgraphia-related handwriting impairment. To accomplish the goal, a fresh handwriting dataset is created by conducting handwriting exercises and a wide variety of features are extracted to represent various handwriting characteristics. Results indicate that Random forest returns the best accuracy but scores less while detecting dysgraphic samples correctly. One class SVM has been tried to deal with the issue of the availability of dysgraphic samples required to train machines. Results indicate good hope in identification with a scope of improvement with increase in sample size for machine training. This paper also seeks to raise awareness of the dysgraphia issue and its effects on society. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Dysgraphia Learning difficulties Machine Motor ability OCC One class SVM Random forest

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An Insight Survey on Sensor Errors and Fault Detection Techniques in Smart Spaces

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computer Modeling in Engineering & sciences 2024年第3期138卷 2029-2059页

作者： Sheetal Sharma Kamali Gupta DeepaliGupta Shalli Rani Gaurav Dhiman Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjab140401India PG Department of Information Technology Goswami Ganesh Dutta Sanatan Dharma CollegeChandigarh160030India Department of Computer Science Government Bikram College of CommercePatialaIndia Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon University Centre for Research and Development Department of Computer Science and EngineeringChandigarh UniversityMohali140413India Department of Computer Science and Engineering Graphic Era Deemed to be UniversityDehradun248002India Division of Research and Development Lovely Professional UniversityPunjab144001India

The widespread adoption of the Internet of Things (IoT) has transformed various sectors globally, making themmore intelligent and connected. However, this advancement comes with challenges related to the effectiveness ofIoT devices. These devices, present in offices, homes, industries, and more, need constant monitoring to ensuretheir proper functionality. The success of smart systems relies on their seamless operation and ability to handlefaults. Sensors, crucial components of these systems, gather data and contribute to their functionality. Therefore,sensor faults can compromise the system’s reliability and undermine the trustworthiness of smart *** address these concerns, various techniques and algorithms can be employed to enhance the performance ofIoT devices through effective fault detection. This paper conducted a thorough review of the existing literature andconducted a detailed *** analysis effectively links sensor errors with a prominent fault detection techniquecapable of addressing them. This study is innovative because it paves theway for future researchers to explore errorsthat have not yet been tackled by existing fault detection methods. Significant, the paper, also highlights essentialfactors for selecting and adopting fault detection techniques, as well as the characteristics of datasets and theircorresponding recommended techniques. Additionally, the paper presents amethodical overview of fault detectiontechniques employed in smart devices, including themetrics used for evaluation. Furthermore, the paper examinesthe body of academic work related to sensor faults and fault detection techniques within the domain. This reflectsthe growing inclination and scholarly attention of researchers and academicians toward strategies for fault detectionwithin the realm of the Internet of Things.

关键词： Error fault detection techniques sensor faults outliers Internet of Things

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AI Enhanced Phishing Detection System 3

AI Enhanced Phishing Detection System

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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Chinnasamy, P. Krishnamoorthy, P. Alankruthi, K. Mohanraj, T. Kumar, B. Santhosh Chandran, Likha Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India Thiruthangal Nadar College Pg Department of Computer Science Chennai51 India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India Ibrahimpatnam501506 India Mlr Institute of Technology Department of Science and Humanities Hyderabad India

ISBN: (纸本)9798350361186

It attempts to create a sophisticated system that uses artificial intelligence (AI) methods to prevent and detect phishing assaults in advance. Cybersecurity is seriously threatened by phishing attempts, which take advantage of human weakness to trick people into disclosing private information. In order to precisely identify and categorize any phishing attempts, the suggested method makes use of machine learning algorithms and natural language processing (NLP) to examine a variety of characteristics of emails, websites, and digital *** project uses AI-driven methods to improve cybersecurity defenses already in place by offering a more reliable and effective way to detect and stop phishing assaults, protecting people and businesses from the negative effects of cyberattacks. © 2024 IEEE.

关键词： Denial-of-service attack

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Digital Integrity: Enhancing Image Forensics with Error Level Analysis and Deep Learning 2

Digital Integrity: Enhancing Image Forensics with Error Leve...

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

作者： Arumugam, M. Arun, G. Jayanthi, C. Nelson Samuvel, A. Pg Kongu Engineering College Department of Computer Technology Perundurai Tamilnadu Erode638060 India Vel Tech Rangarajan Dr.Sangunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Pg& Research Department of Computer Science Tamilnadu Karur639005 India Kongu Engineering College Perundurai Tamilnadu Erode638 060 India

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368413

The widespread use of digitally altered images highlight the need for trustworthy methods to differentiate between real and altered content. This study introduces ELA as a more effective and economical method for detecting changes in digital images, diverging from the original method described in the base work, which employed Fourier transform of residual images. This study offers a reliable method for differentiating between authentic and altered photos using error level analysis (ELA) in combination with a lightweight convolutional neural network (CNN), which is MobileNet. The proposed methodology uses ELA to detect faint aberrations suggestive of picture alteration, from which MobileNet extracts significant features in an effective manner. An SVM is then used to classify the collected features, thereby drawing a line between genuine and artificial intelligence (AI)-generated images. The results of the experiments show that the suggested design performs robustly in picture classification tasks, achieving a high accuracy of 94.18%. This research adds to the field of image forensics by improving the dependability of fake image recognition and providing a scalable solution that can be used in a variety of real-world circumstances, such as cybersecurity and content verification. This integration of ELA with SVM and MobileNet is a major step forward in the ongoing actions taken to maintain the integrity of digital media and fight false information. © 2024 IEEE.

关键词： Convolutional neural networks

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Arduino Based Smart Trash Can- A Solution for Efficient Waste Management 3

Arduino Based Smart Trash Can- A Solution for Efficient Wast...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Rahul, V. Balakrishnan, E. Pradeep Raj, S. Jawahar Vignesh, E. Tamilarasan, B. Sornalakshmi, M. Sri Sairam Institute of Technology Department of Mechanical Engineering Tamilnadu Chennai India Arulmigu Kalasalingam College of Arts and Science Department of Computer Science Pg Tamilnadu Krishnankoil India

ISBN: (数字)9798350354348

ISBN: (纸本)9798350354348

In this work a solution for managing waste in real time application has been done. Integrating Arduino Uno, ultrasonic sensors, GSM modules, and PIR motion sensors offers a cost-effective yet efficient method for waste management. Adding a lithium battery and a solar panel for alternative power sources is a smart move, ensuring continuous operation and reducing reliance on conventional electricity. The automatic lid opening feature based on the detection of human presence adds convenience and enhances the user experience. It's great to hear that the system's performance met expectations during testing, indicating its reliability and functionality for real-world applications. Overall, this design seems to address the requirements of small-scale waste management effectively while keeping costs reasonable. © 2024 IEEE.

关键词： Lithium batteries

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AI-Driven Assessment and Evaluation: Enhancing Sustainability in Educational Practices 3

AI-Driven Assessment and Evaluation: Enhancing Sustainabilit...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Chinnasamy, P. Pushpalatha, M. Nirmala, V. Vidhya, V. Ayyasamy, Ramesh Kumar Priya, Divya Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamilnadu India Department of Computer Science and Technology Karpagam College of Engineering Coimbatore India Department of MCA Annamacharya PG College of Computer Studies Rajampet Kadapa India India Kampar Perak Malaysia Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

ISBN: (纸本)9798331532420

AI-powered educational technologies are emerging as transformative forces in the quickly changing field of education, where innovation is essential to keeping ahead of the competition. Assessments are one area that is undergoing revolutionary transformation. Artificial Intelligence (AI) is redefining the way we evaluate learning outcomes as traditional evaluation approaches struggle to keep up with the demands of the digital age. The field of artificial intelligence-powered learning assessments has a lot of promising future developments ahead of it. This research proposes novel technique in assessment and evaluation based on online education system using Artificial intelligence in machine learning techniques. Here, the input has been analysed as student performance data based on online education and processed for removing of missing values with noise removal. Then this data has been analysed using spatio LSTM convolutional fuzzy neural network for classification of evaluation and assessment model. the experimental analysis has been carried out for various student performance analysis dataset in terms of training accuracy, average precision, specificity, F-measure and recall. Proposed method training accuracy 97%, Average precision 94%, F-measure 90%, RECALL 93%, SPECIFICITY 96%. © 2024 IEEE.

关键词： Adversarial machine learning

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Effective Return Rate Prediction of Blockchain Financial Products Using Machine Learning

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computers, Materials & Continua 2023年第1期74卷 2303-2316页

作者： K.Kalyani Velmurugan Subbiah Parvathy Hikmat A.M.Abdeljaber T.Satyanarayana Murthy Srijana Acharya Gyanendra Prasad Joshi Sung Won Kim PG and Research Department of Computer Science Marudupandiyar College(Affiliated to Bharathidasan UniversityTiruchirappalli)Thanjavur613403India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and EducationKrishnankoil626126TamilnaduIndia Department of Computer Science Faculty of Information TechnologyApplied Science Private UniversityAmmanJordan Department of Information Technology Chaitanya Bharathi Institute of TechnologyHyderabadTelangana500075India Department of Convergence Science Kongju National UniversityGongju32588Korea Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-siGyeongbuk-do38541Korea

In recent times,financial globalization has drastically increased in different ways to improve the quality of services with advanced *** successful applications of bitcoin Blockchain(BC)techniques enable the stockholders to worry about the return and risk of financial *** stockholders focused on the prediction of return rate and risk rate of financial ***,an automatic return rate bitcoin prediction model becomes essential for BC financial *** newly designed machine learning(ML)and deep learning(DL)approaches pave the way for return rate predictive *** study introduces a novel Jellyfish search optimization based extreme learning machine with autoencoder(JSO-ELMAE)for return rate prediction of BC financial *** presented JSO-ELMAE model designs a new ELMAE model for predicting the return rate of financial ***,the JSO algorithm is exploited to tune the parameters related to the ELMAE model which in turn boosts the classification *** application of JSO technique assists in optimal parameter adjustment of the ELMAE model to predict the bitcoin return *** experimental validation of the JSO-ELMAE model was executed and the outcomes are inspected in many *** experimental values demonstrated the enhanced performance of the JSO-ELMAE model over recent state of art approaches with minimal RMSE of 0.1562.

关键词： Financial products blockchain return rate prediction model machine learning parameter optimization

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Scalable and Efficient SSD-Based Lane Detection Model for Autonomous Vehicle Systems 4

Scalable and Efficient SSD-Based Lane Detection Model for Au...

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4th International Conference on Multimedia Processing, Communication and Information technology, MPCIT 2024

作者： Vidya Sagar, V.S. Prabhakar, C.J. Kuvempu University Department of PG Studies & Research in Computer Science Karnataka Shivamogga India Department of MCA Nitte Meenakshi Institute of Technology Karnataka Bengaluru India

ISBN: (纸本)9798350375466

The ADAS (Advanced Driver Assistance System) is a crucial component of autonomous cars that ensures the safety of both drivers and passengers. ADAS components include Adaptive Cruise Control, Automatic Braking/Steer Away, Lane-Keeping System, Blind Spot Assist, Lane Departure Warning System, and Lane Detection. The lane detection system utilizes a camera to provide information about the geometric characteristics of lane line structures to the intelligent system of the vehicle. This enables the system to accurately determine the position of the lane on the road. This paper presents an enhanced Single Shot MultiBox Detector (SSD) model tailored for lane detection tasks in autonomous driving. By integrating multi-scale feature maps and default boxes, the SSD framework efficiently handles object detection in a single pass through the network. The approach leverages a TuSimple dataset comprising 600 high-resolution images, carefully annotated to capture diverse road conditions and lane Lines. Post-processing techniques, including the Hough Transform, are incorporated to refine the lane detection accuracy. The model achieves a remarkable 93% accuracy, demonstrating its robustness and efficacy in real-world scenarios. Key innovations include the use of VGG16 as the backbone network for feature extraction, multi-scale feature maps for detecting lanes at various scales, and advanced non-maximum suppression (NMS) techniques for filtering overlapping bounding boxes. The localization and confidence loss functions are finely tuned to optimize the detection performance. The results indicate significant improvements over traditional methods, making this model a viable solution for real-time lane detection in autonomous vehicles. Future work will focus on expanding the dataset and incorporating additional contextual information to further enhance detection accuracy. © 2024 IEEE.

关键词： Advanced driver assistance systems

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TAMIL- NLP: Roles and Impact of Machine Learning and Deep Learning with Natural Language Processing for Tamil 8

TAMIL- NLP: Roles and Impact of Machine Learning and Deep Le...

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8th IEEE International Conference on science, technology, Engineering and Mathematics, ICONSTEM 2023

作者： Gokila, S. Rajeswari, S. Deepa, S. Hindustan Institute of Technology and Science Department of Computer Applications Chennai India Shrimathi DevkunvarNanalal Bhatt Vaishnav College for Women Pg Department of Computer Science Chennai India Christ University Department of Computer Science Bangalore India

ISBN: (纸本)9798350347791

Reading information in your mother tongue gives the feeling of enjoying juice of fruit. Researchers are working on regional languages to provide convenient and perfect automated tools to convert the content of knowledge from other languages. There exist many challenges based on the grammar of language. One of the classic regional languages, Tamil which is rich in Morphology, contains more processing challenges. The Natural Language Processing (NLP) technique along with Machine Learning (ML) and Deep Learning (DL) algorithms have been used to overcome those challenges. The accuracy of work is depending on the corpus provided to train the model. Among the reviewed papers using Support Vector Machine (SVM) of ML produced higher accuracy then other ML techniques. As DL techniques for NLP are booming one the researchers are working with different DL algorithms. Most of the NLP with Review Discussion in this paper will direct the researchers doing NLP in Tamil language to move further and to choose the right Machine Learning and Deep Learning algorithm to come out with accurate outcomes. © 2023 IEEE.

关键词： Learning algorithms

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Dysgraphia Detection Using Machine Learning-Based Techniques: A Survey 2nd

Dysgraphia Detection Using Machine Learning-Based Techniques...

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2nd International Conference on Emerging Trends in Expert Applications and Security, ICETEAS 2023

作者： Agarwal, Basant Jain, Sonal Bansal, Priyal Shrivastava, Sanatan Mohan, Navyug Department of Computer Science and Engineering Central University of Rajasthan Ajmer India PG Department of Computer Science and Technology Sardar Patel University Vallabh Vidhyanagar Anand India Department of Computer Science and Engineering Indian Institute of Information Technology Kota Kota India

ISBN: (纸本)9789819919086

Dysgraphia is a handwriting disorder which affects the writing abilities of an individual. Currently there is no cure for this disorder but even the diagnosis is difficult. This paper summarises the current research on detection of Dysgraphia through machine learning. Researchers around the globe have researched this topic and have proposed different solutions for the same. The types of Dysgraphia and their symptoms are also discussed in this paper. Different accuracies were achieved by using a variety of Machine Learning algorithms like Support Vector Machine, AdaBoost, K-Means Clustering, Random Forest, Convolutional Neural Networks, etc. Another aim of this paper is to spread awareness about the problem of Dysgraphia and its implications on society. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Adaptive boosting

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