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9th International Conference on Science, technology, engineering and Mathematics, ICONSTEM 2024

作者： Sudhakaran, Pradeep Preetha, M. Kataria, Anshul Sindhu, V. Rajveer Mathur, S. School of Computing Srm Institute of Science And Technology Department of Computing Technologies Chengalpattu District Tamil Nadu Kattankulathur603203 India Career Development Centre Srm Institute of Science And Technology Chengalpattu District Tamil Nadu Kattankulathur603203 India Sri Krishna College of Engineering And Technology Department of Information Technology Tamil Nadu Coimbatore India

ISBN: (纸本)9798350365092

Deaf people rely heavily on sign language as their primary means of communication since it is a sort of symbolic representation that they use. In contrast to sound patterns that are transferred by acoustic methods, sign language involves the employment of body posture and physical communication in order to assist the efficient presentation of a person's own phrases. When talking with those who have trouble speaking vocally, as well as with others who do not have hearing impairments, it is possible to use this method. Using developing technologies such as internet applications, machine learning, and natural language processing, the purpose of this initiative is to close the gap that exists between those who are deaf or hard of hearing or who have hearing impairments and the general community. This will be accomplished by bridging the gap between the two groups. To provide aid to those who are deaf or hard of hearing, the major aim of this project is to design a software or interface that transforms audio and speech into the proper sign language. This will be accomplished in order to provide support to such individuals. Hand forms, posture, and body actions are all in sync with one another whenever they occur at the same time. The method is made up of two distinct components: first, it converts speech into text by utilizing the voice-to-text application programming interface (API);second, it represents the text by utilizing Parse Trees and employs Natural Language Processing semantics (NLTK in particular) for the purpose of lexical analysis of Sign Language Grammar. Both of these components are a part of the method. In this work, the regulations of Indian Sign Language (ISL) are adhered to, and the work develops upon those norms while also adhering to the grammatical requirements of ISL. © 2024 IEEE.

关键词： Semantics

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Machine Learning-Based Crack Detection Methods in Ancient Buildings 24

Machine Learning-Based Crack Detection Methods in Ancient Bu...

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2024 Guangdong-Hong Kong-Macao Greater Bay Area International Conference on Digital Economy and Artificial Intelligence, DEAI 2024

作者： Fang, Tianke Hui, Zhenxing Rey, William P. Yang, Aihua Liu, Bin He, Yuanrong School of Information Technology Mapua University Manila1200 Philippines School of Computing and Information Engineering Xiamen University of Technology Xiamen Fujian361024 China Big Data Institute of Digital Natural Disaster Monitoring in Fujian Xiamen University of Technology Xiamen Fujian361024 China School of Information Media Zhangzhou Vocational College of Science and Technology Zhangzhou Fujian363200 China College of Software Engineering Xiamen University of Technology Xiamen Fujian361024 China

ISBN: (纸本)9798400717147

This research paper introduces an innovative machine learning-based methodology for detecting cracks in ancient buildings, a vital aspect of architectural heritage conservation. Recognizing the limitations of conventional techniques in addressing the complex nature of historical structures, this study explores the integration of deep learning algorithms with enhanced image processing methods tailored for aged and varied materials typical of ancient architecture. The paper includes a comprehensive process involving meticulous data collection from diverse ancient structures, employing specialized preprocessing methods to handle the unique challenges of old surfaces. The development of a custom machine learning model, designed to adapt to the intricacies of historical construction, is thoroughly detailed. Experimental results demonstrate the model's superior performance in accurately identifying structural cracks compared to traditional methods. This research not only significantly contributes to the preservation of historical buildings but also exemplifies the versatile application of machine learning in the field of architectural conservation. The findings hold promise for revolutionizing the approach to maintaining and restoring our architectural heritage, ensuring longevity and integrity. © 2024 ACM.

关键词： Image enhancement

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Fraud Detection in Credit Card through the Machine Learning Algorithms 1

Fraud Detection in Credit Card through the Machine Learning ...

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1st IEEE International Conference on Advances in computing, Communication and Networking, ICAC2N 2024

作者： Kummarikunta, Sandhya Muppagowni, Ganesh Karthik Patam, Keerthi Pennera, Mounika Patimeedi, Upendra Harshith Reddy, Poreddy Chandra Mohan Babu University School of Computing Dept. AIandML Tirupati India GITAM University-Bengaluru Campus GITAM School of Technology Department of CSE Bengaluru India Sree Vidyanikethan Engineering College Dept. Computer Science and Engineering Tirupati India

ISBN: (纸本)9798350356816

The threat of credit card fraud is high and so there should be efficient ways to combat it. The research is being employed to focus on how to enhance credit card fraud detection, machine learning methodologies can be employed. The major benefit of this structured design model is that it observes many transaction variables and find out any potential threat transaction since its trained to do so. To start the project, a large dataset of both authentic and fraudulent credit card transactions will be collected and preprocessed. For the machine to learn how we want it to extract correct location, time, date and users' behavior and an important amount of transaction has been made. This improves our dataset by adding relevant information. This research is based on identifying fake activities it will discuss how to XGBoost, CatBoost and Logistic Regression used for this purpose. © 2024 IEEE.

关键词： Adversarial machine learning

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Application of Deep Learning in the Development of a Drug Discovery System 15th

Application of Deep Learning in the Development of a Drug Di...

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15th International Conference on Soft computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired computing, NaBIC 2023

作者： Praveen Kumar, B.V. Nageswara Rao, A. Tummala, Navya Deepthi Bikku, Thulasi Reddy Madhavi, K. Computer Science and Engineering Usha Rama College of Engineering and Technology Andhra Pradesh Telaprolu India Computer Science and Engineering CMR Institute of Technology Telangana Hyderabad India Computer Science and Engineering Vignan’s Nirula Institute of Technology and Science for Women Andhra Pradesh Guntur India Computer Science and Engineering Amrita School of Computing Amaravati Amrita Vishwa Vidyapeetham Andhra Pradesh Amaravati522503 India School of Computing Mohan Babu University Tirupati India

ISBN: (纸本)9783031810824

Efficient drug discovery hinges on the accurate prediction of interactions between potential drugs and target proteins. Deep learning models have shown promise in this domain;however, their adoption can be challenging for individuals not well-versed in both computer science and cheminformatics. In response to this challenge, we present DeepPurpose, an extensive and user-friendly deep learning library tailored for drug-target interaction (DTI) prediction. DeepPurpose offers a range of features, including the ability to customize interaction types through chemical and protein encoders, as well as access to over 50 neural architectures. As the effectiveness of existing aseptic medications diminishes, the identification of viable DTIs becomes pivotal in the drug development process. DeepPurpose employs an encoder-decoder structure to model interactions, taking compound SMILES strings and pairs of protein amino acid sequences as inputs. The system generates a binding affinity score for the given compound-protein pair. To achieve this, DeepPurpose encodes input proteins and compounds using multiple deep learning encoders, combining their deep embeddings and passing them through a deep neural network decoder to determine binding propensity. DeepPurpose represents a valuable tool in the field of drug discovery, providing researchers with accessible and effective means to predict and explore potential drug-target interactions. Its user-friendly design and comprehensive functionality aim to bridge the gap between deep learning and DTI prediction for a wider audience of researchers and practitioners. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep neural networks

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Text-to-Sketch Synthesis via Adversarial Network

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Computers, Materials & Continua 2023年第7期76卷 915-938页

作者： Jason Elroy Martis Sannidhan Manjaya Shetty Manas Ranjan Pradhan Usha Desai Biswaranjan Acharya Department of Information Science and Engineering NMAM Institute of Technology(Nitte Deemed to be University)NitteIndia Department of Computer Science and Engineering NMAM Institute of Technology(Nitte Deemed to be University)NitteIndia School of Computing Skyline University CollegeUniversity CitySharjahUAE Department of Electronics and Communication Engineering SR UniversityWarangalTelanganaIndia Department of Computer Engineering–AI&BDA Marwadi UniversityRajkotGujaratIndia

In the past,sketches were a standard technique used for recognizing offenders and have remained a valuable tool for law enforcement and social security ***,relying on eyewitness observations can lead to discrepancies in the depictions of the sketch,depending on the experience and skills of the sketch *** the emergence of modern technologies such as Generative Adversarial Networks(GANs),generating images using verbal and textual cues is now possible,resulting in more accurate sketch *** this study,we propose an adversarial network that generates human facial sketches using such cues provided by an ***,we have introduced an Inverse Gamma Correction Technique to improve the training and enhance the quality of the generated *** evaluate the effectiveness of our proposed method,we conducted experiments and analyzed the results using the inception score and Frechet Inception Distance *** proposed method achieved an overall inception score of 1.438±0.049 and a Frechet Inception Distance of 65.29,outperforming other state-of-the-art techniques.

关键词： Generative adversarial networks inverse gamma correction sketch attributes text-to-sketch synthesis deep learning techniques

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Detection of Face Mask and Social Distance Calculation Using Deep Learning 9

Detection of Face Mask and Social Distance Calculation Using...

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9th International Conference on Science, technology, engineering and Mathematics, ICONSTEM 2024

作者： Sudhakaran, Pradeep Sindhu, V. Gupta, Sarthak Preetha, M. School of Computing Srm Institute of Science And Technology Department of Computing Technologies Chengalpattu District Tamil Nadu Kattankulathur603203 India Sri Krishna College of Engineering And Technology Department of Information Technology Tamil Nadu Coimbatore India Srm Institute of Science And Technology Career Development Centre Chengalpattu District Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798350365092

The COVID 19 epidemic is causing a worldwide health disaster. The epidemic caused governments all over the world to institute lockdowns in order to limit viral spread. According to study data, wearing a face mask and keeping social distance in public places significantly reduces the risk of transmission. The project's ultimate goal is the development of a real-time, very accurate system that can detect any non-masked faces anywhere and compulsively require people to wear masks in the interest of public health. The proposed system combines state-of-the-art deep learning algorithms with geometric methods to provide a reliable modal that is effective at detection, tracking, and validation. Consequently, the proposed method is helpful to society because it reduces the spread of the corona virus while saving time. In light of the current situation, where the lockdown is being removed to permit inspections at public gatherings, retail malls, and other areas, this may prove to be effective. Automated inspection saves resources since it reduces the need for human inspectors. In addition, it is applicable in any circumstance. © 2024 IEEE.

关键词： Learning algorithms

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Cross-chain Sharing of Personal Health Records: Heterogeneous and Interoperable Blockchains

Cross-chain Sharing of Personal Health Records: Heterogeneou...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Lv, Yongyang Li, Xiaohong Wang, Yingwenbo Chen, Kui Hou, Zhe Feng, Ruitao Tianjin University College of Intelligence and Computing Tianjin China Griffith University School of Information and Communication Technology Brisbane Australia Southern Cross University Faculty of Science and Engineering Gold Coast Australia

ISBN: (纸本)9798350386226

With the widespread adoption of medical informatics, a wealth of valuable personal health records (PHR) has been generated. Concurrently, blockchain technology has enhanced the security of medical institutions. However, these institutions often function as isolated data silos, limiting the potential value of PHRs. As the demand for data sharing between hospitals on different blockchains grows, addressing the challenge of cross-chain data sharing becomes crucial. When sharing PHRs across blockchains, the limited storage and computational capabilities of medical Internet of Things (IoT) devices complicate the storage of large volumes of PHRs and the handling of complex calculations. Additionally, varying blockchain cryptosystems and the risk of internal attacks further complicate the cross-chain sharing of PHRs. This paper proposes a scheme for sharing PHRs across heterogeneous and interoperable blockchains. Medical IoT devices can encrypt and store real-time PHRs in an InterPlanetary File System, requiring only simple operations for data sharing. An enhanced proxy re-encryption(PRE) algorithm addresses the differences in blockchain cryptosystems. Multi-dimensional analysis demonstrates that this scheme offers robust security and excellent performance. © 2024 IEEE.

关键词： Electronic health record

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Machine Learning Approach for Handling Imbalanced Students' Performance Data 15

Machine Learning Approach for Handling Imbalanced Students' ...

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15th International Conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Sujatha, E. Divya, S. Sakthivelan, R.G. Gopirajan, P.V. Thandalam Chennai India Chennai India Department of Computer Science and Engineering Gopalan College of Engineering and Management Bengaluru India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur campus Chennai India

ISBN: (纸本)9798331300579

Imbalanced student performance data in educational institutions is crucial for any machine learning prediction model. It affects the efficiency of classifiers and challenges the sampling methods for having a more significant number of features. The proposed model was designed to predict the student's performance by comparing the results with popular similar models. The Kaggle dataset having 386 rows and 33 columns of student data, was used in this study. In addition, the Portugal database was considered for experimental analysis, containing 394 students and 19 attributes. It proves that the Random Forest classifier yields the highest accuracy at 84.15% in handling imbalanced datasets. The results show that the SVM-SMOTE is higher accuracy as, 94.76%, than the other sampling methods in predicting the student's performance with various features. © Grenze Scientific Society, 2024.

关键词： Adversarial machine learning

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Brain Tumor detection Using Machine Learning and Deep Learning Approaches

Brain Tumor detection Using Machine Learning and Deep Learni...

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2022 IEEE International Conference on Advances in computing, Communication and Applied Informatics, ACCAI 2022

作者： Khan, Oneza Tehreem Rajeswari, D. School of Computing College of Engineering and Technology Department of Data Science and Business Systems Kattankulathur603203 India

ISBN: (纸本)9781665495295

Recent years has provided extremely interesting and exciting developments and applications of Machine Learning (ML) and Deep Learning (DL) to automize healthcare delivery. ML techniques are surpassing human performances in detecting, predicting, diagnosing any disease and even recommending treatments for the same. ML along with Deep Learning has revolutionized Medical Imaging technology. Brain Tumor detection is one of the many healthcare area Artificial Intelligence has marveled at. A tumor is basically an area or part of the body suffering by swelling caused by distorted tissues where the cells start multiplying uncontrollably. Magnetic Resonance Imaging (MRI) is the technique used to detect the tumor. MRI is quite commonly used in medical science to detect abnormalities in any part of the body. Tumor in the brain is far more perilous and different to treat than in any other part of the body which makes the early prediction and monitoring of brain tumor extremely expedient. This paper discusses the various algorithms, techniques, new approaches and their comparison with the existing ones and the general process of brain tumor detection with the help of MRI. © 2022 IEEE.

关键词： Magnetic resonance imaging

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Secure Data Sharing with Confidentiality,Integrity and Access Control in Cloud Environment

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Computer Systems Science & engineering 2022年第2期40卷 779-793页

作者： V.Rajkumar M.Prakash V.Vennila Department of Computer Science and Engineering Krishnasamy College of Engineering and Technology(Affiliated to Anna UniversityChennai)Cuddalore607109India Department of Computer Science and Engineering School of ComputingSRM Institute of Science&TechnologyKattankulathur603203India Department of Computer Science and Engineering K.S.R.College of Engineering(Affiliated to Anna UniversityChennai)Tiruchengode637215India

Cloud storage is an incipient technology in today’s *** of security in cloud environment is one of the primary challenges faced these *** scenario poses new security issues and it forms the crux of the current *** current study proposes Secure Interactional Proof System(SIPS)to address this *** methodology has a few key essential components listed herewith to strengthen the security such as authentication,confidentiality,access control,integrity and the group of components such as AVK Scheme(Access List,Verifier and Key Generator).It is challenging for every user to prove their identity to the verifier who maintains the access *** is conducted by following Gulliou-Quisquater protocol which determines the security level of the user in multi-step authentication ***,RSA algorithm performs the key generation process while the proposed methodology provides data integrity as well as confidentiality using asymmetric *** methodological operations such as time consumption have been used as performance evaluators in the proposed SIPS *** proposed solution provides a secure system for firm data sharing in cloud environment with confidentiality,authentication and access *** Timed Petri(STPN)Net evaluation tool was used to verify and prove the formal analysis of SIPS *** evidence established the effectiveness of the proposed methodology in secure data sharing in cloud environment.

关键词： Secure interactional proof system access control multi-step authentication Gulliou-Quisquater protocol

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