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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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Latent Variable and Classification Performance Analysis of Bird-Drone Spectrograms with Elementary Autoencoder

IEEE Transactions on Radar Systems

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IEEE Transactions on Radar systems 2024年 3卷 115-123页

作者： White, Daniel Jahangir, Mohammed Mishra, Amit Kumar Baker, Chris J. Antoniou, Michail University Of Birmingham School Of Electrical Electronic And Systems Engineering United Kingdom Aberystwyth University Department Of Computer Science United Kingdom

Deep learning with convolutional neural networks has been widely utilised in radar research concerning automatic target recognition. Maximising numerical metrics to gauge the performance of such algorithms does not necessarily correspond to model robustness against untested targets, nor does it lead to improved model interpretability. Approaches designed to explain the mechanisms behind the operation of a classifier on radar data are proliferating, but bring with them a significant computational and analysis overhead. This work uses an elementary unsupervised convolutional autoencoder (CAE) to learn a compressed representation of a challenging dataset of urban bird and drone targets and subsequently if apparent quality of the representation via preservation of class labels leads to better classification performance after a separate supervised training stage. It is shown that a CAE that reduces the features output after each layer of the encoder gives rise to the best drone vs bird classifier. A clear connection between unsupervised evaluation via label preservation in the latent space and subsequent classification accuracy after supervised fine-tuning is shown, supporting further efforts to optimise radar data latent representations to enable optimal performance and model interpretability. © 2023 IEEE.

关键词： Drones

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Thermal Model of a Soldering Iron 33rd

Thermal Model of a Soldering Iron

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33rd International Conference on Organization and Technology of Maintenance, OTO 2024

作者： Barić, Tomislav Glavaš, Hrvoje Dubravac, Anto Faculty of Electrical Engineering Computer Science and Information Technology Osijek University of Osijek Osijek Croatia

ISBN: (纸本)9783031805967

Soldering irons are a hand tool that is indispensable in the process of making small series of electronic devices. Soldering irons have evolved from very simple devices without temperature control to devices with complex temperature control. The implementation of temperature control is based on the knowledge of the thermal models of the soldering iron. Thermal models of soldering irons are based either on the theory of thermal fields or on the theory of equivalent Kirchhoff thermal circuits. The theory of thermal fields is applied for very accurate modeling. In doing so, the finite element method (FEM) is used. For the above, it is necessary to use software packages based on FEM. Unlike FEM-based modeling, thermal circuit modeling does not require specialized software tools. It is also possible to derive analytical expressions that describe the thermal process of the soldering iron. In this paper, a thermal model of a soldering iron based on equivalent Kirchhoff thermal circuits is presented. Analytical expressions were derived to describe the thermal transient. The validity of the theoretical model is supported by measurement. Measurements were carried out on two soldering irons from different manufacturers. The measurement results were analyzed. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Hand tools

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Enhancing User Authentication through Keystroke Dynamics Analysis using Isolation Forest algorithm 2

Enhancing User Authentication through Keystroke Dynamics Ana...

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2nd International Conference on Emerging Trends in information Technology and engineering, ic-ETITE 2024

作者： Iyapparaja, M. Karunanithi, Isvarya Sahana Bhat, U. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350328202

Authentication in modern era, has evolved significantly to address the increasing complexity and security challenges of our digital world. Traditional methods of authentication, such as passwords and PINS have proven to be vulnerable to various forms of attacks. As a result there is a growing need for innovative and robust authentication techniques. Keystroke dynamics involves the analysis of user typing patterns, including the time interval between keystrokes and the key pressed duration, to establish a unique biometric profile for each individual. Unlike traditional authentication methods keystroke dynamics offer several advantages, such as continuous authentication without the need for additional hardware and the ability to adapt to a user's changing typing behavior. This model will additionally analyze users typing patterns have the capability to identify individuals using the system by considering their typing speed. The first premise involves developing a system that can capture the keystroke dynamics of a user during a training phase and create a unique profile for that user. The second premise involves implementing Mahalanobis distance for Dynamic keystroke Authentication with various machine learning algorithms. This model can maximize the strengths of both methods to improve the accuracy and robustness of the authentication system. Authentication using keystroke dynamics is secured and convenient method and can be used wherever there is requirement of authentication such as baking, e-commerce and secured access control system. From the experiment performed below we can attained that the model functions exceptionally well using Isolation Forest with an accuracy of 90%. © 2024 IEEE.

关键词： Support vector machines

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Survival Analysis of Superficial Bladder Cancer patients using DeepSurv and Cox models 2

Survival Analysis of Superficial Bladder Cancer patients usi...

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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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Knowledge-Driven Possibilistic Clustering with Automatic Cluster Elimination

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computers, Materials & Continua 2024年第9期80卷 4917-4945页

作者： Xianghui Hu Yiming Tang Witold Pedrycz Jiuchuan Jiang Yichuan Jiang The School of Computer Science and Engineering Southeast UniversityNanjing211189China The School of Computer and Information Hefei University of TechnologyHefei230601China The Department of Electrical and Computer Engineering University of AlbertaEdmontonAB T6R 2V4Canada The Systems Research Institute Polish Academy of SciencesWarsaw00-901Poland The School of Information Engineering Nanjing University of Finance and EconomicsNanjing210023China

Traditional Fuzzy C-Means(FCM)and Possibilistic C-Means(PCM)clustering algorithms are data-driven,and their objective function minimization process is based on the available numeric ***,knowledge hints have been introduced to formknowledge-driven clustering algorithms,which reveal a data structure that considers not only the relationships between data but also the compatibility with knowledge ***,these algorithms cannot produce the optimal number of clusters by the clustering algorithm itself;they require the assistance of evaluation ***,knowledge hints are usually used as part of the data structure(directly replacing some clustering centers),which severely limits the flexibility of the algorithm and can lead to *** solve this problem,this study designs a newknowledge-driven clustering algorithmcalled the PCM clusteringwith High-density Points(HP-PCM),in which domain knowledge is represented in the form of so-called high-density ***,a newdatadensitycalculation function is *** Density Knowledge Points Extraction(DKPE)method is established to filter out high-density points from the dataset to form knowledge ***,these hints are incorporated into the PCM objective function so that the clustering algorithm is guided by high-density points to discover the natural data ***,the initial number of clusters is set to be greater than the true one based on the number of knowledge ***,the HP-PCM algorithm automatically determines the final number of clusters during the clustering process by considering the cluster elimination *** experimental studies,including some comparative analyses,the results highlight the effectiveness of the proposed algorithm,such as the increased success rate in clustering,the ability to determine the optimal cluster number,and the faster convergence speed.

关键词： Fuzzy C-Means(FCM) possibilistic clustering optimal number of clusters knowledge-driven machine learning fuzzy logic

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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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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 Alzheimer's Disease Detection with Chaotic Moth Flame Optimization Algorithm: A Feature Selection Approach

Enhancing Alzheimer's Disease Detection with Chaotic Moth Fl...

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2024 IEEE International Conference on Emerging Technologies in computer science for Interdisciplinary Applications, ICETCS 2024

作者： Sabari Vasan, S. Jayalakshmi, P. Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India

ISBN: (纸本)9798350372502

Advances in medical science have led to a better understanding of Alzheimer's disease (AD), as shown by the clear description of AD using numerical, medical and MRI architectural imagery details. Early diagnosis of AD is possible using clinical data in addition its presence can be identified with huge correctness. Still, managing the massive and broadening AD data record is seemly an issue and utilising the unrefined data record unless removing redundant and duplicate data can significantly impact prediction or classification accuracy. Therefore, this research proposes an improved memetic metaheuristic behavioural founded feature selection framework called chaotic moth flame optimization algorithm (CMFOA). The suggested framework of MFO incorporates chaotic mapping to boost the search process, distinguishing it from the traditional form. CMFOA outperforms Ant colony optimization (ACO) and Firefly algorithm (FFA) in terms of ease, variable reduction, optimal efficiency, quick implementation, and rapid computing, resulting in improved subsets of characteristics for AD prediction. The study utilized the Alzheimer's disease neuroimaging initiative (ADNI) dataset to ascertain potential characteristics implicated in the diagnosis of Alzheimer's disease. The performance of the three feature subset selection approaches was validated using a deep neural network (DNN).The results demonstrated that CMFOA outperformed the other models in terms of accuracy. © 2024 IEEE.

关键词： Feature Selection

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