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2nd International Conference on Emerging Trends in information technology and Engineering, ic-ETITE 2024

作者： Supriya, K. Anitha, A. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350328202

The advancements in technology help in analyzing and predicting the disease of human life using automation. Out of various technologies, Machine Learning (ML) and Deep learning (DL) provide some promising results to help humankind. Automated technologies help improve the prediction process of the healthcare system, especially in carcinoma detection based on past information. Thus, the proposed model made a survival analysis of the patients who are affected by bladder cancer (BC) based on their past health records. The tumor in the urinary bladder indicates that cancer among patients, when not treated can be spread to the other parts of the human body and cause death. However, the health records provide some linear combinations of the data to analyze the survival rate on treatments such as Placebo and Thiotepa. Thus, the proposed work utilized DeepMLPSurv and CoxPH survival model to make an analysis on the tumour recurrence and predicts the survival rate among the bladder cancer patients. © 2024 IEEE.

关键词： Diseases

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CropViT: A light-weight Transformer Model for Crop Disease Detection 3

CropViT: A light-weight Transformer Model for Crop Disease D...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Chemmalar Selvi, G. Charan, H.J. Kumar, Dinesh Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India

ISBN: (纸本)9798350372120

Agricultural industry has grown significantly bring sustainable farming practices in improving the food quality, enhancing agricultural productivity and global food security. However, the crop yield and its quality are impacted when the timely identification and management of the crop diseases are not promptly solved. Thus, the crop disease detection becomes the critical aspect in the smart agriculture. Several researchers addressed this crop disease detection by developing optimized solutions through machine learning, deep learning and image processing techniques. However, these techniques face serious challenges when deployed in real-time crop disease identification problems like annotated training images, capturing long-range dependencies, adaption to real-time changes evolving over time and so on. This leads to performance degradation seeking for more powerful applications like some advanced deep learning techniques, in particular, the transformer models. In this paper, a novel crop disease detection model known as CropViT was proposed based on Vision Transformer. This CropViT was developed by fine-tuning the architecture of existing Vision Transformer and PlantVillage dataset was used in the experimental study. Nine crop species were selected and trained using CropViT whose performance was then compared with traditional Convolutional Neural Network model. The experimental study clearly showed the highest mean accuracy of 98.64% for CropViT when compared to 95.52% mean accuracy for traditional CNN. The experimental results also outperformed other existing state-of-the-art models. Finally, the work was concluded with the discussion of experimental results and its future scope of work. © 2024 IEEE.

关键词： Convolutional neural networks

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Krushi Snehi: A Web-Based Application for Safe and Smart Farming 8th

Krushi Snehi: A Web-Based Application for Safe and Smart Far...

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8th International Conference on information and Communication technology for Competitive Strategies, ICTCS 2023

作者： Jain, Sanyam Thaseen, Sumaiya School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9789819712595

The challenges stemming from crop diseases and a limited grasp of optimal fertilization practices have significantly burdened farmers, leading to reduced crop yields and a ripple effect of interconnected issues. This research paper introduces a novel solution in the form of a web-based application, meticulously crafted to assist farmers in swiftly identifying crop diseases, providing relevant precautions, and offering tailored fertilizer recommendations for specific crops. Notably, the application boasts a user-friendly interface, seamlessly catering to intuitive navigation and content comprehension, presented in the user's preferred language. Of notable importance, the application serves as a conduit for disseminating valuable insights concerning government initiatives, and effectively acquainting farmers with available incentives and authoritative helpline resources. By harnessing the power of this application, farmers can cultivate a comprehensive understanding of pivotal strategies for mitigating crop infections, potentially culminating in a significant upswing in crop production rates. We had achieved an accuracy of 98.98% in classifying the crop diseases. This innovative tool not only promises to effectively tackle longstanding agricultural challenges but also to equip farmers with actionable insights, thereby underpinning the advancement of agricultural productivity. In doing so, it lays the groundwork for the adoption of sustainable and prosperous farming practices, promising a transformative trajectory for farming communities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Fertilizers

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Review on IOMT Security through Distributed Machine Learning 2

Review on IOMT Security through Distributed Machine Learning

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2nd International Conference on Emerging Trends in information technology and Engineering, ic-ETITE 2024

作者： Bhaskar, Swasthika Vanitha, M. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350328202

In this constructive healthcare industry, AI-based IOMT (Internet of Medical Things) is one of the highly used Technologies. The virtual world is customary to lose responsible data in cyberspace. Without any doubt, IOMT comes under this category. IOMT applications do not intensively focus on Security and privacy and it is a Serious downside of IOMT applications. Considering this crucial issue, we use the Multiple-node machine learning Technique. It is widely used for preserving the privacy of the huge records. It does not want to share its sensitive data from one node to another node, which result in providing security and privacy to medical record by adding protection to input records, output tags, parameters, etc. This paper exclusively focuses on the Security and Privacy of Medical Records and also shows the various roles of Distributed Machine Learning. © 2024 IEEE.

关键词： Machine learning

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A Hybrid Approach to Validate and Retrieve Fingerprint Image for Authenticating Database Sensitive information Access and to Verify Some of Topological Multi-Granular Rough Sets Properties on Authenticated Database Fields 2

A Hybrid Approach to Validate and Retrieve Fingerprint Image...

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2nd International Conference on Emerging Trends in information technology and Engineering, ic-ETITE 2024

作者： Raghavan, R. John Singh, K. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350328202

Content based image retrieval has already begun to turn into an intriguing and exceptionally needed field to meet with the client prerequisite. Fingerprint approvals likewise have become a fascinating field to principally uphold the confirmation framework. Notwithstanding foundations regulating guaranteed about information straightforwardly, affiliations are also obliging them to examine the fingerprint endorsement. Despite establishments overseeing ensured about data, directly associations are similarly obliging them to research the fingerprint check. Organizations and foundations both are typically utilizing the idea of fingerprint for the participation and presence reason. Nevertheless, establishments presently managing secured information as like institutes and additionally going along with them to investigate the fingerprint authentication. More than this usual action there are some particular applications where the authentication would be considerable to help the application than using a traditional pattern of password. We proposed a fingerprint authentication system for the scientific based industry database access system. We removed the noise if any found in the input fingerprint using anisotropic Gauss filtering. The noise free fingerprint image is not further passed to the binarization phase as the need of binarization and some form of the drawback of binarization is replaced with a thinning algorithm. The thinned fingerprint image is followed by the matching process and once it is matched, then only the sensitive and highly confidential information are allowed to access from the database. The testing is done using OpenCV and FVC 2004 dataset to assess the projected retrieval method. Further the matched fingerprints are used to prove some of the internal structure properties of multi-granular rough sets (MGRS) on authenticated database fields. The accuracy obtained in this hybrid approach is found to be more satisfactory and hence the security towards acce

关键词： Database systems

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Enhancing User Authentication with a Secure Human-Computable Password Scheme 2

Enhancing User Authentication with a Secure Human-Computable...

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2nd International Conference on Emerging Trends in information technology and Engineering, ic-ETITE 2024

作者： Ayeswarya, S. John Singh, K. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350328202

Authentication in the digital world has become a challenging and difficult task. The username-password combination is no longer reliable and the tools that support authentication mechanism are all breakable. In addition to authentication, to avoid bots, CAPTCHA is used which increases the complexity of the system. Thus the research is going towards making use of human brain which is so unique and powerful in the authentication process. This will also eliminate the need for CAPTCHA since human is computing the password. To make it feasible, the function that is computed by the human should be simple enough. The existing method trusts the computer to store the public images and if there is a key logger installed in the system, the adversary will be able to get the password. This again tends to be a fixed password. Instead, if it is based on a challenge from the server, the password will be dynamic and secure. In this paper a human computable password protocol is suggested. The experimental results prove that the proposed technique is efficient and is less prone to attacks. © 2024 IEEE.

关键词： Authentication

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Transfer Learning Empowered Skin Diseases Detection in Children

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computer Modeling in Engineering & Sciences 2024年第12期141卷 2609-2623页

作者： Meena N.Alnuaimi Nourah S.Alqahtani Mohammed Gollapalli Atta Rahman Alaa Alahmadi Aghiad Bakry Mustafa Youldash Dania Alkhulaifi Rashad Ahmed Hesham Al-Musallam Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Computer Information Systems College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Computer Engineering College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia ICS Department King Fahd University of Petroleum and MineralsDhahran31261Saudi Arabia

Human beings are often affected by a wide range of skin diseases,which can be attributed to genetic factors and environmental influences,such as exposure to sunshine with ultraviolet(UV)*** left untreated,these diseases can have severe consequences and spread,especially among *** detection is crucial to prevent their spread and improve a patient’s chances of ***,the branch of medicine dealing with skin diseases,faces challenges in accurately diagnosing these conditions due to the difficulty in identifying and distinguishing between different diseases based on their appearance,type of skin,and *** study presents a method for detecting skin diseases using Deep Learning(DL),focusing on the most common diseases affecting children in Saudi Arabia due to the high UV value in most of the year,especially in the *** method utilizes various Convolutional Neural Network(CNN)architectures to classify skin conditions such as eczema,psoriasis,and *** proposed method demonstrates high accuracy rates of 99.99%and 97%using famous and effective transfer learning models MobileNet and DenseNet121,*** illustrates the potential of DL in automating the detection of skin diseases and offers a promising approach for early diagnosis and treatment.

关键词： Deep learning MobileNet DenseNet121 skin diseases detection transfer learning

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Facial Attendance Tracker using Enhanced Mean Local Binary Pattern 2

Facial Attendance Tracker using Enhanced Mean Local Binary P...

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

作者： Charanya, R. Jayasurya, Arnold David Krithika, L.B. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350384277

Facial Attendance Tracker is a mobile application where a student can fetch his/her attendance by scanning his/her face from his/her own mobile. The faculty will project the Dynamic QR code when he/she wants to take the attendance. The students have to login into the mobile application using the login credentials and scan the dynamic QR code provided by the faculty. Once, the QR code is scanned successfully, it will redirect to the camera where the student can fetch his/her face. The system comprises of acquiring the image, followed by sending the image to the database, detection of face, pre-processing, extraction of features, classifying the features and post- processing stage. To detect the face from the image, Viola-Jones algorithm has been used and Enhanced Mean Local Binary Pattern (EMLBP) algorithm is used to extract features as it outperforms the other algorithms in a huge margin. © 2024 IEEE.

关键词： Local binary pattern

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Face Recognition Based Attendance Management System Using Deep Learning Method 2

Face Recognition Based Attendance Management System Using De...

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2nd International Conference on Emerging Trends in information technology and Engineering, ic-ETITE 2024

作者： Angulakshmi, M. Susithra, V. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (数字)9798350328202

ISBN: (纸本)9798350328202

Deep learning and transfer learning are extremely important in the internet sector and the health care industry. Face recognition technology is essential in practically every industry in our digital age. Several improvements have been made to automatic facial recognition (AFR) technology in an ever-changing world. Managing the daily student attendance system is made simple with Smart attendance with Real-Time Face Recognition. In the proposed method, Images of the students in class are used to construct a dataset. Both Face detection and recognition are performed using the Multitask Convolution Neural Network algorithm. The data will be uploaded after the face is recognition. The proposed method compared with Haar Cascade Convolution Neural Network Classification method and other few methods. The proposed method produced better resultcompared to other methods © 2024 IEEE.

关键词： Students

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Multifunctional Pre-processing Approach for Diabetes Mellitus Prediction Using Ensemble Learning 3

Multifunctional Pre-processing Approach for Diabetes Mellitu...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Divya, S. Sudha, M. Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India

ISBN: (纸本)9798350372120

Diabetes mellitus when untreated can result in a number of health issues. It is a metabolic disease marked by abnormal blood glucose levels. Early detection of diabetes improves a person's long-term health by halting its progression. Making better medical decisions can be greatly aided through the implementation into practice automated techniques that can precisely identify pre- and diabetic situations. In this paper Synthetic Minority Oversampling Technique and Adaptive Oversampling Technique is used to address class imbalance problem and Chi-square test, Recursive feature elimination methods are involved in detecting and extracting relevant components from unprocessed data. These techniques are applied to the framework and compared using different ensemble learning classifiers such as XGBoost, AdaBoost, LightGBM and CATBoost. The result shows us CATBoost with ADASYN and RFE provides us with better accuracy of 0.96%. © 2024 IEEE.

关键词： Adaptive boosting

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