Tomato Plant Disease Classification Using Transfer Learning
subject: Accuracy; Convolutional Neural Network; Transfer learning; VGG-19
description: Detecting and categorizing diseases in tomato plants poses a significant hurdle for farmers, resulting in considerable agricultural losses and economic harm. The prompt underscores the significance of promptly identifying and classifying diseases to enact successful management strategies. Convolutional Neural Networks (CNNs) have demonstrated their effectiveness in tasks involving image classification, notably in categorizing diseases that impact tomato plants. However, CNN models can be computationally expensive to train and require large datasets of labeled images. Utilizing advanced CNN models can enhance the efficacy of classification models for tomato plant diseases, simultaneously decreasing computational expenses and the demand for extensive training data. Enhanced CNN models can be developed using a variety of techniques, such as transfer learning, data augmentation, and residual networks. This project aims to implement a tomato plant disease classification model using an enhanced convolution neural network. This work uses the lifelong learning method which is the model that allows one to learn new tasks without forgetting previous knowledge. Leveraging sophisticated CNN models can improve the effectiveness of classification models for tomato plant diseases, while also reducing computational costs and the need for extensive training data. It is beneficial for tasks where there is limited data available to train a model from scratch. 2024 IEEE.
creator: Poly A.; Vetriveeran D.; Balamurugan M.
source: 2024 5th International Conference for Emerging Technology, INCET 2024
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835036115-5
format: Online
language: English
type: Conference paper
coverage: Poly A., Depart of Computer Science and Engineering, CHRIST (Deemed to be) University, Bangalore, Kengeri, India; Vetriveeran D., Depart of Computer Science and Engineering, CHRIST (Deemed to be) University, Bangalore, Kengeri, India; Balamurugan M., Depart of Computer Science and Engineering, CHRIST (Deemed to be) University, Bangalore, Kengeri, India

Allometry Authentication in the Field of Finance: Creation of Well Secured System using AI Algo Based Systems
subject: AI-driven systems; Banking; Biometric authentication; Ethical considerations; Facial recognition; Fingerprint recognition; Regulatory compliance; Security; User experience; Voice authentication
description: It is true the banking sector is increasingly under pressure to tighten security in an ever-changing digital arena, even as the customer experience needs to be strengthened. Thus, the use of biometric authentication through enhanced AI-driven systems that would enhance the security protocols while at the same time smoothening the users' interactions was a promising way in response. The paper that follows explores the integration of biometric authentication within banking systems in a bid to make clear its effectiveness in relation to reinforcing security and enhancing user experience. Accordingly, bijson etal. argue that biometric security fits perfectly in banks, since with the increasing cyber threats, banks are bound to deploy more advanced security mechanisms. These traditional means, suchjson, use of passwords and PINs, have shown vulnerabilities that are liable to exploitation and should be changed into something much more resilient. The authentication under biometrics also validates a user's identity by basing it on unique physiological or behavioral traits, such as a fingerprint, features of the face, patterns of the iris, and the voice. Biometric systems authenticate users with a very high level of confidence through AI-based algorithms, averting the security risks associated with unauthorized access and identity theft. Further, biometric authentication overcomes the flaws that prevail with the traditional mode of methods and hence, it ensures a very comfortable and user-friendly mode of system security. 2024 IEEE.
creator: Tandon A.; Anitha C.; Kataria A.; Mohammed N.Q.; Al-Khuzaie M.Y.; Almulla A.A.
source: 2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024, pp. 962-967.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835036016-5
format: Online
language: English
type: Conference paper
coverage: Tandon A., Jagannath International Management School, Department of It, Vasant Kunj, New Delhi, India; Anitha C., Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College, Department of Computer Science and Engineering, Tirupati, India; Kataria A., Christ Deemed to Be University, School of Business and Management, Bangalore, India; Mohammed N.Q., AL-Nisour University College, Department of Medical Laboratories Technology, Baghdad, Iraq; Al-Khuzaie M.Y., National University of Science and Technology, College of Health and Medical Technology, Dhi Qar, Iraq; Almulla A.A., Al-Hadi University College, Baghdad, Iraq

Automated Verification of Open/Closed Principle: A Code Analysis Approach
subject: Open Closed Principle; SOLID Principles
description: The SOLID principles are foundational to software engineering, focusing on the maintainability, scalability, and extensibility of software systems. The Open/Closed Principle (OCP), a pivotal element among these principles, underscores the need to design software modules that are open for extension yet closed for modification. This research explores automated verification techniques for OCP, addressing the validation of software modules through extensibility and adaptability assessments. The principal objectives involve the development of a code analysis approach and a methodology capable of automating the verification of adherence to OCP in developed codes, providing actionable insights to software developers. The system focuses on specific aspects of OCP, including inheritance, abstraction, and polymorphism, and aims to provide clear indications of where violations occur within a codebase. The implementation uses the Abstract Syntax Tree (AST) analysis to examine class definitions. The automated analysis of Python code using the defined rules offers a clear understanding of OCP adherence. Results are presented in Pandas DataFrames, indicating potential violations and providing developers with actionable insights to enhance code quality and maintainability. Overall, the automated code verification system aims to enhance code quality and adherence to fundamental design principles, paving the way for advancements in automated code analysis and software engineering practices. 2024 IEEE.
creator: Roy G.; Manohar M.; Jacob B.A.
source: 2024 5th International Conference for Emerging Technology, INCET 2024
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835036115-5
format: Online
language: English
type: Conference paper
coverage: Roy G., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India; Manohar M., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India; Jacob B.A., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India

Improving Image Clarity with Artificial Intelligence-Powered Super-Resolution Methods
subject: Artificial intelligence; Deep learning; Image quality; Super resolution
description: Super-resolution has advanced significantly in the last 20years, particularly with the application of deep learning methods. One of the most important image processing methods for boosting an image's resolution in computer vision is image super-resolution besides providing an extensive overview of the most recent developments in artificial intelligence and deep learning for single-image super-resolution. This study delves into the subject of image enhancement by investigating sophisticated AI-based super-resolution techniques. High-quality photographs have become more and more in demand in a variety of industries recently, including medical imaging, satellite imaging, entertainment, and surveillance. Pixilation reduction and detail preservation are two areas where traditional image enhancing techniques fall short. Artificial intelligence has demonstrated amazing promise in addressing these issues, especially with regard to Deep Learning models. The applications, benefits, and difficulties of modern super-resolution techniques are thoroughly examined in this work. We also suggest new approaches and push the limits of image enhancement by experimenting with state-of-the-art artificial intelligence algorithms. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
creator: Malathy V.; Poornima M.; Varun V.L.; Venugopal P.; Acharjee P.B.; Krishnaveni S.
source: Smart Innovation, Systems and Technologies, Vol-390, pp. 127-140.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2024-01-01
rights: Restricted Access
relation: ISSN: 21903018; ISBN: 978-981972715-5
format: Online
language: English
type: Conference paper
coverage: Malathy V., School of Engineering, SR University, Telangana, Warangal, India; Poornima M., Department of Mathematics, SJB Institute of Technology, Bengaluru, India; Varun V.L., Department of Mathematics, SJB Institute of Technology, Bengaluru, India; Venugopal P., Department of Mathematics, SJB Institute of Technology, Bengaluru, India; Acharjee P.B., Computer Science, CHRIST University Pune, Lavasa Campus, Lavasa, India; Krishnaveni S., Department of Management Studies, Adithya Institute of Technology, Coimbatore, Kovilpalayam, India

Utilizing Machine Learning for Advanced Natural Language Processing and Sentiment Analysis in Social Media Platforms
subject: Machine learning (ML); Natural language processing; Random forest (RF); Sentiment analysis; Support vector machine (SVM)
description: Social media is increasingly regarded as one of the most abundant online resources for information gathering and knowledge exchange. Among the most widely used social media sites is Twitter available today. When attempting to comprehend the information in any unknown word-based data (such as social media), natural language processing (NLP) techniques are crucial since they help remove noise from data, identify stem words, etc. It also helps with comprehension of the sentiment or semantic contents. Using social media, we apply machine learning techniques (clustering and classification) to determine the viewpoint's polarity in the information. Several classifiers and clusters, including SVM, RF, Naive Byes, and KNN, are used to detect content on social media. Sentiment analysis is the process of automatically classifying user-generated content as neutral, negative, or positive. It is possible to utilize the text, sentence, feature, or aspect as criteria to group feelings into distinct categories. This study demonstrates the application of machine learning techniques to the analysis of emotions expressed on the Twitter network. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
creator: Manoharan G.; Jyothi B.S.; Bhat R.; Acharjee P.B.; Sharma A.; Rawal P.
source: Smart Innovation, Systems and Technologies, Vol-390, pp. 111-125.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2024-01-01
rights: Restricted Access
relation: ISSN: 21903018; ISBN: 978-981972715-5
format: Online
language: English
type: Conference paper
coverage: Manoharan G., School of Business, SR University, Telangana, Warangal, India; Jyothi B.S., CSE (DS), Rajeev Gandhi Memorial College of Engineering and Technology, Nandyala, India; Bhat R., Amity School of Communication, Amity University Kolkata, Kolkata, India; Acharjee P.B., Computer Science, CHRIST University Pune Lavasa Campus, Lavasa, India; Sharma A., Jagran Lakecity Business School, Faculty of Management and Commerce, Jagran Lakecity University, Madhya Pradesh, Bhopal, India; Rawal P., Faculty of Management and Commerce, Jagran Lakecity University, Madhya Pradesh, Bhopal, India

An Integration of Satellite A Based Network with Higher Level Type Network with the use of P-P Connection: A Deep Review
subject: 6G; Aerial Access Network (AAN); Benders decomposition-based algorithm; point to point communication; Time-Expanding graphs (TEGs)
description: The Aerial Access 6g Network (AAN) is seen as a way to access remote and sparsely populated areas not served by traditional terrestrial networks, especially with the advent of 6G technology. This study presents a new approach for efficient data collection and transmission in point to point access networks using low earth orbit (LEO) satellites and high altitude platforms (HAPS). Incorporating LEO satellites as backlinks and HAPs as airborne base stations, the system provides low-bandwidth transmission to ground users. A Time Augmented Graph (TEG) model is proposed to represent the dynamic topology of the air access network according to time slots. With this example, this study can create an entire programming problem with the goal of maximizing data transfer to the country's data processing centre (DPC) while respecting resource constraints. Benders' decomposition-based algorithm (BDA) is proposed to solve the NP-hardness of the problem and is shown to perform well in producing near-optimal solutions. The effectiveness and efficiency of the proposed strategy is verified through simulation results performed in a realistic environment, showing high speed and performance comparable to search methods. By informing the design and optimization of future communication systems, this study will provide a better understanding of how HAP and LEO satellites work together in aerial access networks for the collection and delivery of remote terrain data. 2024 IEEE.
creator: Kumar V.N.; Al-Attabi K.; Thakur G.; Ambrose S.; Nirsanametla Y.; Murugajothi T.
source: 2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024, pp. 1392-1396.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835036016-5
format: Online
language: English
type: Conference paper
coverage: Kumar V.N., Cmr Technical Campus, Department of Cse, Telangana, Hyderabad, India; Al-Attabi K., The Islamic University, Najaf, Iraq; Thakur G., Uttaranchal Institute of Technology, Uttaranchal University, Department of Civil Engineering, Dehradun, 248007, India; Ambrose S., Christ (Deemed to Be University), Department of Commerce, India; Nirsanametla Y., Chitkara University, Centre of Research Impact and Outreach, Punjab, Rajpura, 140401, India; Murugajothi T., Psna College of Engineering and Technology, Department of Ece, Dindigul, India

A Deep Assessment of ML Based Procedure used as a Classifiers in the Clinical Field
subject: Data Mining; Healthcare; Machine Learning classifiers; Prediction Analysis; Symptoms
description: In the unexpectedly evolving panorama of healthcare technology, the mixing of data mining and machine mastering gives exceptional possibilities for the advancement of sickness prediction fashions. This research paper introduces a unique Machine Learning Smart Health Procedure designed to harness the predictive energy of those era for forecasting illnesses. By meticulously reading ancient healthcare facts, which includes affected individual signs and symptoms and effects, this system leverages cutting-edge algorithms which includes Nae Bayes, Support Vector Machines (SVM), and neural networks to expect capacity health problems with accelerated accuracy. This method now not best pursuits to facilitate early and specific evaluation but also strives to noticeably enhance affected individual care and treatment consequences. Through the strategic utility of statistics mining and prediction analysis in the healthcare area, our proposed machine demonstrates the capacity to revolutionize conventional diagnostic techniques, developing a proactive and predictive healthcare model more plausible and effective than ever earlier than. 2024 IEEE.
creator: Sharma K.; Madeswaran A.; Chandramauli A.; Narasimha V.; Jasim L.; Reeja Y.M.
source: 2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024, pp. 968-972.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835036016-5
format: Online
language: English
type: Conference paper
coverage: Sharma K., Chitkara University, Centre of Research Impact and Outreach, Punjab, Rajpura, 140401, India; Madeswaran A., Christ (Deemed to Be University), School of Business and Management, Bangalore Yeshwanthpur campus, India; Chandramauli A., Uttaranchal University, Uttaranchal Institute of Technology, Department of Civil Engineering, Dehradun, 248007, India; Narasimha V., Cmr College of Engineering & Technology, Department of Cse, India; Jasim L., The Islamic University, Najaf, Iraq; Reeja Y.M., Saveetha Institute of Medical and Technical Sciences, Saveetha University, Electronics and Communication Engineering, Saveetha School of Engineering, Chennai, India

Comparative Performance Analysis of Deep Learning Models in Cervical Cancer Detection
subject: AlexNet; cervical cancer; Deep learning; DenseNet121; feature integration; ResNet50; VGG16
description: Cervical cancer one of the four most common malignancies worldwide and poses a significant threat, particularly in resource-constrained regions. Automated diagnostic approaches, leveraging colposcope image analysis, hold great promise in curbing the impact of this disease. In this paper, we deploy a range of deep learning methods, including DenseNet 121, ResNet 50, AlexNet and VGG 16 to classify the cervical intraepithelial neoplasia. Our methodology is deployed on a dataset sourced from a Cancer Research institute in India. The current experiment aims to establish the execution of the state-of-the-art pretrained frameworks in deep learning. This will be a baseline experiment for researcher who aim to develop further deep learning models for cervical cancer diagnosis. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
creator: Mukku L.; Thomas J.
source: Lecture Notes in Networks and Systems, Vol-1046 LNNS, pp. 185-194.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2024-01-01
rights: Restricted Access
relation: ISSN: 23673370; ISBN: 978-303164812-0
format: Online
language: English
type: Conference paper
coverage: Mukku L., CHRIST (Deemed to Be University), Bangalore, India; Thomas J., CHRIST (Deemed to Be University), Bangalore, India

A Design of Agricultural Robotics for the use of Sowing and Planting
subject: Agricultural robotics; estimating yields; farming robotic systems; gathering; land preparation; phenotyping; plant treatment; planting; precision gardening; sowing
description: Agricultural robots is always getting better to deal with problems like population growth, fast urbanization, fierce competition for high-quality goods, worries about protecting the environment, and a lack of skilled workers. This in-depth study looks at the main uses of farming robotic systems, covering jobs like preparing the land, sowing, planting, treating plants, gathering, estimating yields, and phenotyping. Each robot is judged on how it moves, what it will be used for, whether it has sensors, a robotic arm, or a computer vision program, as well as its development stage and where it came from. The study finds trends, possible problems, and things that stop business growth by looking at these shared traits. It also shows which countries are putting money into studying and developing (R&D) for these products. The study points out four important areas - movement systems as a whole sensor, computer vision computer programs, and communication technologies - that need more research to make smart agriculture better. The results make it clear that spending money on farming robotic systems can pay off in the long run by helping with things like accurate yield estimates and short-term benefits like keeping an eye on the harvest. 2024 IEEE.
creator: Chandrakala P.; Badholia R.; Arunkumar E.; Victor M.; Maheswaran M.; Choubey A.K.
source: 2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024, pp. 345-349.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835036016-5
format: Online
language: English
type: Conference paper
coverage: Chandrakala P., Prince Shri Venkateshwara Padmavathy Engineering College, Department of Electrical and Electronics Engineering, TN, India; Badholia R., Ies Institute of Technology and Management, Ies University, Department of Mechanical Engineering, M.P., Bhopal, India; Arunkumar E., Karpagam College of Engineering, Department of Electronics and Communication Engineering, TN, Coimbatore, India; Victor M., Christ University, School of Business and Management, Bangalore, India; Maheswaran M., Nehru Institute of Engineering and Technology, Mechatronics Engineering Dept, Tamil Nadu, T.M. Palayam, India; Choubey A.K., Baderia Global Institute of Engineering & Management, Madhya pradesh, Jabalpur, India

The Latest Technology and its Integration for the Development of Healthcare(Medical)
subject: Artificial Intelligence; Data Analysis; Future Healthcare; Medical Sector; Patient Care; Technology
description: Healthcare advances that use Artificial Intelligence (AI) to analyze data, use devices, and identify patients offer new possibilities for better patient care, cutting costs, and growing the medical sector. The age of specialized human health tests has begun. It uses noninvasive instruments, sound, visual the use of photography, electronic health tools, embedded health instruments, fluidic diagnostic tracking, and combined data analysis to provide people with tailored medical suggestions. These technologies contribute to early identification and comprehending of health issues linked to chronic illnesses and general health using information analysis and AI-driven ideas. Notable uses include a Parkinson's and Huntington's Under certain circumstances, diabetes, cancer, kidney disease, heart problems, elderly care, and a number of healthcare areas. Industry changes are expected as a result of the latest breakthroughs in outdoor monitors, AI-driven evaluation of data, and healthcare testing technologies. AI systems give data to people and health workers, possibly better their way of life and cutting healthcare costs. These include: tracking the effectiveness of medicines, finding chronic illnesses early, and offering individualized care using medical trends and DNA. In relation to healthcare studies and sensor tracking, this study explains new technologies and advances in diverse fusion methods, materials, and processes. Precise diagnostic info, small merchandise dimensions, and cost are high considerations. Healthcare workers, patients, consumers all benefit from more personal health care services thanks to the merging of AI with information streams. The text highlights both advantages and hurdles while showing the way toward upcoming displays and academic papers that follow a path of growth in the industry. 2024 IEEE.
creator: Sarojini Y.; Pithode K.; Sandhya D.; Sivadharshini B.; Singh K.R.; Choubey S.B.
source: 2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024, pp. 42-47.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835036016-5
format: Online
language: English
type: Conference paper
coverage: Sarojini Y., Nehru Institute of Engineering and Technology, Department of Cs and Bs, Tamil Nadu, India; Pithode K., Ies College of Technology, Department of Cs and E, M.P., Bhopal, India; Sandhya D., Christ University, School of Business and Management Dept, Bangalore, India; Sivadharshini B., Prince Shri Venkateshwara Padmavathy Engineering College, Department of Information Technology(IT), TN, India; Singh K.R., Karpagam Academy of Higher Education, Department of Computer Science (CSE), India; Choubey S.B., Sreenidhi Institute of Science and Technology, ghatkesar, Hyderabad, Yamnampet, India

Advanced Technological Improvements in Making of Smart Production Using AI and ML
subject: Applications; Artificial Intelligence; Industry 4.0; Machine Learning; Smart Production; Sustainability; Systematic Literature Review
description: The necessity for adaptation and creativity in the manufacturing sector demonstrates the importance of sustainable manufacturing by the merging of advanced technologies. To encourage sustainability, a global view on the integration of smart manufacturing procedures is important. Artificial intelligence (or AI) has appeared as a crucial factor in achieving environmentally conscious manufacturing, with methods like the use of machine learning (ML) getting popularity. This study carefully studies the scientific papers related to the usage of AI and ML in business. The emergence of Industry 4.0 as a whole has positioned machine learning (ML) and artificial intelligence (AI) as drivers for the smart industry change. The study categorizes material based on release year, writers, scientific field, country, institution, and terms, applying the Web of Biology and SCOPUS databases. Utilize UCINET alongside NVivo 12 software, thereby the analysis covers empirical studies on machine learning (ML) and artificial intelligence (AI) via 1999 until the present, showing their growth before and after the start of Industry 4.0. Notably, the USA displays a substantial addition to this area, with a noticeable surge in desire following the rise of Industry 4.0. 2024 IEEE.
creator: Praveenraj D.D.W.; Vijayaraj T.; Kumar K.S.; Sakthivel N.K.; Multani F.; Nagaveni P.
source: 2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024, pp. 178-183.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835036016-5
format: Online
language: English
type: Conference paper
coverage: Praveenraj D.D.W., Christ University, School of Business and Management, TN, Bangalore, India; Vijayaraj T., Prince Shri Venkateshwara Padmavathy Engineering College, Department of Mechanical Engineering, TN, India; Kumar K.S., Department of Electronics and Communication Engineering, Chennai, Tamil Nadu, Avadi, India; Sakthivel N.K., Nehru Institute of Engineering and Technology, Department of Computer Science and Engineering, Tamil Nadu, T.M. Palayam, India; Multani F., Ies College of Technology, Department of Computer Science& Engineering, M.P, Bhopal, India; Nagaveni P., Karpagam Academy of Higher Education, Department of Electrical and Electronics Engineering, TN, India

An Innovative Way of Trackable GDS in the Field of CC
subject: Anonymity; Cloud computing; Data traceability; Group data sharing; Group signature
description: It is important to provide security and efficient data exchange in cloud infrastructure and achieve traceability and anonymity of data. mean For high levels of safety and performance in one Anonymously, this article addresses the topic It allows data to be exchanged and stored between members of the same group in the cloud. Proposed arrangement creates unique and traceable group data sharing policies using group signatures and special agreements Strategies to accomplish these goals. this Facilitates anonymous communication between systems Public clouds have many users and. Real people following up when needed. Also, the system implements the main agreement programs to make it easier for team members to. Obtain a shared session key for secure data exchange and storage facilities. Basic generation processes a Symmetric Balanced Incomplete Block Theory (SBIBD), significantly reducing the workload of team members a shared session key must be introduced. In cloud computing contexts, the suggested system guarantees efficiency and security for group data sharing, as shown by theoretical analysis and experimental validation. 2024 IEEE.
creator: Pandey M.; Subramani K.; Madeswaran A.; Al-Jawahry H.M.; Sudhamalla M.; Misra N.; Kumar K.K.
source: 2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024, pp. 1579-1582.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835036016-5
format: Online
language: English
type: Conference paper
coverage: Pandey M., Christ (Deemed to Be University), School of Business and Management, Bangalore Yeshwanthpur Campus, India; Subramani K., Christ (Deemed to Be University), School of Business and Management, Bangalore Yeshwanthpur Campus, India; Madeswaran A., Christ (Deemed to Be University), School of Business and Management, Bangalore Yeshwanthpur Campus, India; Al-Jawahry H.M., The Islamic University, Najaf, Iraq; Sudhamalla M., Cmr Technical Campus, Department of Ece, Telangana, Hyderabad, India; Misra N., Uttaranchal University, Uttaranchal Institute of Management, Department of Management, Dehradun, 248007, India; Kumar K.K., Saveetha University, Sse, Saveetha Institute of Medical and Technical Science - Simats, Department of Computer Science and Engineering, Chennai, India

Early-Stage Cervical Cancer Detection via Ensemble Learning and Image Feature Integration
subject: cervical cancer; Deep learning; DenseNet121; feature integration; K means; ResNet50
description: Cervical cancer ranks as the fourth most common malignancy worldwide and poses a significant threat, particularly in resource-constrained regions. Automated diagnostic approaches, leveraging colposcope image analysis, hold great promise in curbing the impact of this disease. In this study, we introduce an ensemble of machine learning and deep learning models, including DenseNet 121, ResNet 50, and XGBoost to classify the cervical intraepithelial neoplasia. A novel feature integration is proposed which ensembles the results of the individual models in five fold validation process. Our methodology is deployed on a dataset sourced from the International Agency for Cancer Research. The results from the proposed framework have shown to be accurate, robust and dependable. This method can be utilized for achieving automatic identification of cervical cancer in early stages so it can be treated appropriately. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
creator: Mukku L.; Thomas J.
source: Lecture Notes in Networks and Systems, Vol-1046 LNNS, pp. 112-122.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2024-01-01
rights: Restricted Access
relation: ISSN: 23673370; ISBN: 978-303164812-0
format: Online
language: English
type: Conference paper
coverage: Mukku L., CHRIST (Deemed to be University), Bangalore, India; Thomas J., CHRIST (Deemed to be University), Bangalore, India

An Integration of AI Technique in the Field of Healthcare Industry
subject: AI; Biological Computation; Biomedicine; Disease Diagnoses; Healthcare; Living Support; Neural Networks; Predictive Studies; Research; Scientific Successes; Technological Environment
description: Over the last few years, the field of intelligent machines (AI) has experienced fast improvements in software algorithms to hardware deployment, and varied uses, especially in the area of healthcare. This thorough study aims to capture recent developments in AI uses within biomedicine, spanning disease diagnoses, living support, biological computation, and research. The primary goal is to record recent scientific successes, discern what is happening in the technological environment, perceive the enormous future scope of AI on biomedicine along and serve as a source of stimulus for researchers through related fields. It is obvious that, similar to the development of AI itself, the use of it in biology continues to remain in its infant state. This review expects ongoing breakthroughs and improvements that will push the limits and broaden the range of AI uses in the near future. In order to communicate the changing possibility of AI in biology, the study dives into individual case studies. These include anticipating of epileptic seizure events and the uses of AI in treating a faulty urine bladder. By studying these cases, the overview seeks to explain the visible impact of AI off healthcare and reinforce the chance of immediate developments in this evolving and promising field. 2024 IEEE.
creator: Priyadharsini P.; Khare N.; Praveenraj D.D.W.; Senthurya S.; Arunkumar B.; Reddy G.V.
source: 2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024, pp. 1981-1986.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835036016-5
format: Online
language: English
type: Conference paper
coverage: Priyadharsini P., Nehru Institute of Engineering and Technology, T.M.Palayam, Coimbatore, India; Khare N., Ies University, Ies Insititute of Technology and Management, M.P., Bhopal, 462044, India; Praveenraj D.D.W., Christ University, Bangalore, India; Senthurya S., Prince Shri Venkateshwara Padmavathy Engineering College, Chennai, India; Arunkumar B., Karpagam Academy of Higher Education, Coimbatore, India; Reddy G.V., Cmr Technical Campus, Hyderabad, Medchal, India

Towards a Framework for Supply Chain Financing for Order-Level Risk Prediction: An Innovative Stacked A-GRU Based Technique
subject: Gated Recurrent Unit (GRU); Stacked Autoencoder Feature Extraction (SAFE); Supply Chain Finance
description: Order financing is changing the game in the banking and financial supply chain industry. It's great for SMEs and opens up new revenue streams for logistics and finance companies. But in order to find the weak spots offered by banks and other financial institutions, companies need to undertake thorough risk assessments right now. Careful timing is crucial for training the model, extracting features, and preprocessing. Outlier identification and missing value handling are the first steps in preprocessing, which also includes normalization and standardization to improve data integrity and reduce unit discrepancies. Principal component analysis makes use of multivariate statistics to aid in feature extraction, guaranteeing effective data representation. Careful consideration of every detail is required during the training of a Stacked-A-GRU model, which follows attribute selection. Impressively outperforming state-of-the-art algorithms SAFE and GRU, the suggested solution achieves a remarkable correctness rating of 97.34%, indicating notable progress in predicting accuracy. 2024 IEEE.
creator: Amarnath Reddy B.; Vinesh Kumar G.; Dhiliphan Kumar M.; Veena S.; Chauhan A.; Anwer Basha H.
source: 1st International Conference on Electronics, Computing, Communication and Control Technology, ICECCC 2024
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835037180-2
format: Online
language: English
type: Conference paper
coverage: Amarnath Reddy B., Vignana Jyothi Institute of Management, Department of Finance, Telangana, Hyderabad, India; Vinesh Kumar G., Vardhaman College of Engineering, Department of Management Studies, Telangana, Hyderabad, India; Dhiliphan Kumar M., Deemed University, Business Administration, Kalasalingam Academy of Research and Education, Virudhunagar, India; Veena S., Deemed University, Business Administration, Kalasalingam Academy of Research and Education, Virudhunagar, India; Chauhan A., School of sciences, CHRIST (Deemed to be university), Department of life sciences, Karnataka, Bengaluru, India; Anwer Basha H., Saveetha College of Liberal Arts and Sciences, Saveetha Institute of Medical and Technical Sciences, Department of Computer Science, Chennai, India

The Role of IOT in Creating SC'S through Ultra Fast Updation of the Status for Accurate Action Plan
subject: AI-powered systems; blockchain; data management; healthcare; IoT; machine learning; smart cities; smart technology; urban efficiency; wireless sensor networks
description: The idea of a smart city includes the merging of technologies and advances aimed at improving urban efficiency, scientific progress, the preservation of the environment, and social inclusion. Coined in the year 2000, the term became widely used in politics, business, management, and urban planning groups to drive tech-based changes in urban areas. It reacts to the difficulties posed by postindustrial communities handling problems such as pollution to the environment, demographic changes, population growth, health care monetary crises, and resource shortages. Beyond technical answers, the smart city idea includes non-technical innovations for healthy urban life. Particularly encouraging is the application that uses Internet of The circumstances (IoT)based sensors in healthcare, applying machine learning for effective data management. This paper discusses the application of AI-powered Ai and Wireless Sensor Networks, more commonly known as the field of health care, acting as a basic study to understand the impact of IoT in smart cities, especially in healthcare, for the sake of future research. 2024 IEEE.
creator: Hariharasudhan S.; Subasree S.; Bhuvaneswari M.; Victor M.; Kumar R.; Kumar K.S.
source: 2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024, pp. 188-193.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835036016-5
format: Online
language: English
type: Conference paper
coverage: Hariharasudhan S., Prince Shri Venkateshwara Padmavathy Engineering College, Department of Information Technology, India; Subasree S., Nehru Institute of Engineering and Technology, Department of Computer Science and Engineering, Tamil Nadu, T.M. Palayam, India; Bhuvaneswari M., Karpagam Academy of Higher Education, Department of Biomedical Engineering, TN, Coimbatore, India; Victor M., Christ University, School of Business and Management, Bangalore, India; Kumar R., Ies University, Department of Mech Engg, Ies Institute of Technology and Management, M.P., Bhopal, 462044, India; Kumar K.S., Vel Tech Rangarajan Dr. Sagunthala R D Inst. of Science and Technology (Deemed to Be University), Chennai, Tamil Nadu, Avadi, India

Advances in Crime Identification: A Machine Learning Perspective
subject: Crime Patterns; Crime prevention; Detection; Machine Learning; Prediction
description: Crime profoundly impacts individuals, communities, and families. Technological advancements have provided perpetrators with new opportunities for criminal activities. The primary objective of the police department is to resolve crimes, ensuring justice for the victims. Additionally, preventing such incidents is crucial for creating a safer world. The landscape of criminal justice has undergone a significant shift with the integration of machine learning techniques, unlocking unparalleled potential for accuracy and efficiency. This study thoroughly examines the concept of various applications of machine learning in crime detection, prediction, and prevention. We examine the evolution of these technologies, from early developments to state- of-the-art methodologies, conducting a thorough analysis of their strengths, limitations, and ethical considerations. Moreover, the paper sheds light on crimes discussed in academic circles, serving as a repository for scholars and researchers. This facilitates informed discussions and guides future research endeavours. 2024 IEEE.
creator: Rachana N.; Norton Stanley S.A.; Sandeep J.; Sreeja C.S.
source: Proceedings of International Conference on Communication, Computer Sciences and Engineering, IC3SE 2024, pp. 68-74.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835036684-6
format: Online
language: English
type: Conference paper
coverage: Rachana N., Christ (Deemed To Be University), Department Of Computer Science, Bangalore, India; Norton Stanley S.A., Christ (Deemed To Be University), Department Of Computer Science, Bangalore, India; Sandeep J., Christ (Deemed To Be University), Department Of Computer Science, Bangalore, India; Sreeja C.S., Christ (Deemed To Be University), Department Of Computer Science, Bangalore, India

A Systematic Review of Various Advancements Implementation in the Field of Crop (Plant) Production
subject: agricultural robots; challenges; crop protection; Pest management
description: An essential component of agricultural output is pest management, especially in fertigation-based farming. Although fertigation systems in Malaysia are beneficial for irrigation and fertilization, they frequently don't have effective pest control techniques. Because pests usually live beneath crop leaves, hand spraying is difficult and labor-intensive. Insect pests have the power to seriously harm, weaken, or even kill agricultural plants, which can lead to lower yields, worse-quality goods, and unsalable outcomes. Furthermore, insects may still cause harm to processed or stored items after harvest. Therefore, creating an autonomous pesticide sprayer specifically designed for chilli fertigation systems is the main goal of this research. The main goal is to create a sprayer arm that is flexible enough to reach under crop leaves. The goal of this project is to build an autonomous, unmanned pesticide sprayer. The goal of autonomous operation is to reduce the amount of dangerous pesticides that people are exposed to, especially in enclosed spaces like greenhouses. In addition, the sprayer arm's adaptability to different agricultural circumstances makes it a valuable tool in both greenhouse and outdoor settings. It is expected that the successful adoption of the autonomous pesticide sprayer would completely transform fertigation-based farming's approach to pest management. 2024 IEEE.
creator: Babu P.B.; Bisht D.; Shilpa K.; Alzubaidi L.H.; Sharma A.; Rajesh Kanna R.
source: 2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024, pp. 1306-1310.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835036016-5
format: Online
language: English
type: Conference paper
coverage: Babu P.B., Aditya Engineering College, Department of Electronics and Communication Engineering, Surampalem, India; Bisht D., Uttaranchal Institute of Management, Uttaranchal University, Department of Management, Dehradun, 248007, India; Shilpa K., Cmr Technical Campus, Department of Cse, Telangana, Hyderabad, India; Alzubaidi L.H., The Islamic University, Najaf, Iraq; Sharma A., Chitkara University, Chitkara Centre for Research and Development, Himachal Pradesh, 174103, India; Rajesh Kanna R., Christ(Deemed to Be University), Department of Cse, Bangalore, India

Enhanced Security in Payment Gateways Through Face Detection: An Advanced Approach Using DenseNet 121- BiLSTM Models
subject: Facial Recognition (FR); Field Programmable Gate Arrays (FPGA); Payment System
description: Because it is one of the most promising applications of image analysis, face recognition has been the subject of intense research and development for many decades. Many modern identification and verification requirements have found a potential new home with the introduction of face recognition (FR) technology. Facial recognition is just one of numerous uses for biometric pattern recognition algorithms. Sequencing is essential for many tasks, including as feature extraction, model training, and preprocessing. Eliminating background noise and obtaining dense vertical edges are part of the preprocessing procedures. Facial feature extraction will be employed to extract features after feature extraction. Use attributes cautiously when training a Desnet121-BiLSTM model. In every respect, the suggested method outperforms two state-of-the-art algorithms, Desnet121 and BiLSTM. An accuracy rating of 97.19% was indicative of a considerable improvement in the figures. 2024 IEEE.
creator: Gowsic K.; Sowmiya M.; Nirmala Devi M.; Chauhan A.; Arul Mary Rexy V.; Singh D.
source: 1st International Conference on Electronics, Computing, Communication and Control Technology, ICECCC 2024
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835037180-2
format: Online
language: English
type: Conference paper
coverage: Gowsic K., Mahendra Engineering College (Autonomous), Department of Computer Science and Engineering, Namakkal, India; Sowmiya M., Mahendra Engineering College (Autonomous), Department of Computer Science and Engineering, Namakkal, India; Nirmala Devi M., School of Sciences, CHRIST (Deemed to be University), Department of Life Sciences, Karnataka, Bengaluru, India; Chauhan A., St. Martin's Engineering College, Department of English, Secunderabad, India; Arul Mary Rexy V., Saveetha College of Liberal Arts and Sciences, SIMATS, Department of Commerce, Kuthambakkam, India; Singh D., School of Management Sciences, Department of Applied Science, Lucknow, India

A Study Examining the Relationship Between College Students Demographic Characteristics and Financial Literacy- With Special Reference to a Union Territory in India
subject: Borrowings; Demographic Behaviour; Financial Literacy; Financial well-being
description: Over the past ten years, the importance of financial literacy has been growing across the world. Prior research has found that a lack of financial knowledge can have several negative consequences, the inability to make correct financial decisions, high levels of debt, high-cost borrowing and misuse of credit. Limited knowledge of financial concepts also has an impact on the economy as a whole. This study attempts to measure the level of financial literacy of college students in Goa, India. A total of 378 respondents were surveyed and their level of financial literacy was measured through a percentage analysis. The respondents level of financial literacy was also studied concerning various demographic characteristics. The results show an association between financial literacy and sex, level of education, field of education, percentage of respondents and income level. The findings of the study suggest a need for the strengthening of initiatives by policymakers to introduce the concept of financial literacy for students all over the state as well as the country, in every field. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
creator: Dave K.A.; Ashfaque M.Z.; Guruprasad D.; Narasimha M.H.; Kazim S.; Mukthar K.P.J.
source: Lecture Notes in Networks and Systems, Vol-1080 LNNS, pp. 412-425.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2024-01-01
rights: Restricted Access
relation: ISSN: 23673370; ISBN: 978-303167443-3
format: Online
language: English
type: Conference paper
coverage: Dave K.A., St. Josephs College of Commerce (Autonomous), Karnataka, Bengaluru, India; Ashfaque M.Z., St. Josephs College of Commerce (Autonomous), Karnataka, Bengaluru, India; Guruprasad D., Department of Commerce, Manipal Academy of Higher Education, Bengaluru, India; Narasimha M.H., CHRIST (Deemed to be University), Bengaluru, India; Kazim S., Jain (Deemed to be University) CMS, Bengaluru, India; Mukthar K.P.J., Kristu Jayanti College (Autonomous), Karnataka, Bengaluru, India