The Impactful Role of ML Algo in the Field of Enactment Nostrum: An Intensive/Deep Review
Machine Learning; Machine Translation; Natural Language Processing; Support Vector Machines; Translation medicine
Machine translation (MT) research has explored a variety of models, including statistical machine translation (SMT), rule-based machine translation (RBMT), and hybrid approaches. Hybrid MT systems aim to improve translation quality by using the strengths of different models. However, the complex set of functions associated with MT systems is still difficult to understand and optimize. This instant study propose an approach based on ML with respect to hybrid MT that addresses these issues by automatically interpreting and weighting features using ML tools. This research framework includes a classification approach to classify and compare translations from multiple black-box A system that uses ML classifiers trained on feature vectors derived from natural language processing tools. This study presents a method to train and use an SVM-based classifier to generate hybrid interpretations. The test results for English-Chinese pairs show the potential of this research approach to improve translation quality. The proposed framework is a simple and efficient way to combine different MT systems, improving translation results without manual intervention. 2024 IEEE.
Narasimharao J.; Hariharan R.; Sudha E.; Bisht D.; Mohan E.; Almusawi M.; Kumar N.S.
2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024, pp. 1407-1411.
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.1109/ICACITE60783.2024.10617176 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201805857&doi=10.1109%2fICACITE60783.2024.10617176&partnerID=40&md5=d38892adf79b65bd7c1cb3a15374b469
Narasimharao J., Cmr Technical Campus, Department of Cse, Telangana, Hyderabad, India; Hariharan R., Christ (Deemed to Be University), School of Business and Management, Yeshwantpur, Bangalore, India; Sudha E., Christ (Deemed to Be University), Department of Commerce, Yeshwantpur Campus, Bangalore, India; Bisht D., Uttaranchal Institute of Management, Uttaranchal University, Department of Management, Dehradun, 248007, India; Mohan E., Simats, Saveetha School of Engineering, Department of Ece, Tamilnadu, Chennai, India; Almusawi M., The Islamic University, Najaf, Iraq; Kumar N.S., Srm Institute of Science & Technology, Department of Computer Science & Engineering, Chennai, Vadapalani, India
The Development of ID System for Detecting Attacks in WSN Through Ontology Method and its Strategy
Cyberattacks; Internet of Things (IoT); Intrusion Detection System (IDS); ontology; wireless sensor networks
Cybercriminals are becoming increasingly targeted by the rapid expansion of the Internet of Things (IoT), leading to an increase in cyberattacks targeting IoT devices and their communication channels These attacks, if failure to detect may result in significant service disruption, financial loss or damage to sensitive data. Real-time intrusion detection is essential to ensure reliability, security and profitability of IoT applications. This study introduces a new intrusion detection system designed for IoT devices that uses deep learning (DL). Utilizing ontology in wireless sensor networks (WSN), this intelligent system detects suspicious activities that pose a threat to connected IoT devices with configuration-neutral design provides ease of use, while the test performance analysis is simulated and real-world. It highlights its strong performance in determining admissions. The effectiveness of the system against many types of attacks such as denial of service, workholes, blackholes, opportunistic service attacks, etc. is confirmed by experimental research and furthermore, the system achieves F1 scores, accuracy and the number of memories. This advanced deep learning intrusion detection system shows great promise to improve IoT network security due to its high detection rate. 2024 IEEE.
Sheela M.S.; Venkateswarlu M.; Donald C.; Kadiri P.; Al-Hameed M.R.; Safi H.; Saadoun O.N.
2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024, pp. 391-394.
https://doi.org/10.1109/ICACITE60783.2024.10616975 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201793785&doi=10.1109%2fICACITE60783.2024.10616975&partnerID=40&md5=e99456a796a0df95c31caf1a16be0407
Sheela M.S., Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology, Ece, Chennai, Avadi, India; Venkateswarlu M., Kampala International University, Faculty of Science and Technology, Department of Computing, Western Campus, Uganda; Donald C., Christ (Deemed to Be University), Department of Computer Science, Bengaluru, India; Kadiri P., Mohan Babu University, School of Computing, Department of Aiml, Andhra Pradesh, Tirupati, India; Al-Hameed M.R., National University of Science and Technology, College of Health and Medical Technology, Dhi Qar, 64001, Iraq; Safi H., Al-Farahidi University, Baghdad, Iraq; Saadoun O.N., Al-kunooze University College, Basra, Iraq
Oil Price Volatility and Its Impact on Industry Stock Return Bi Variate Analysis
Deep learning model; Oil Price Volatility; Returns; Risk Factor; Volatility Spillovers
Oil price volatility impacts industries differently depending on a countrys status as a net oil importer or exporter. In oil-importing nations like India, sectors such as banking, energy, materials, retailing, transportation, and manufacturing are adversely affected by price fluctuations, while industries like food, beverages, and pharmaceuticals tend to be more resilient. Conversely, oil-exporting countries experience milder effects, with the oil and gas sector bearing the brunt of supply disruptions while other industries remain insulated. Over time, the correlation between oil prices and stock market performance has strengthened, making oil price volatility a systemic risk factor. The source of oil price shocks, whether from demand changes or supply disruptions, significantly influences their impact on stock returns. Notably, there are substantial volatility spillovers between oil and stock markets. This study aims to explore the relationship between oil shocks and industry returns using various multivariate models, highlighting the importance of considering oil as a relevant risk factor in portfolio management. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Bhat S.R.; Yadav J.S.; Kumar C.M.N.; Amar H.A.; Rakesh N.; Kumar S.V.P.
Lecture Notes in Networks and Systems, Vol-1080 LNNS, pp. 102-111.
Springer Science and Business Media Deutschland GmbH
Bhat S.R., Department of Finance, Alliance Ascent College, Alliance University, Karnataka, Bengaluru, India; Yadav J.S., School of Business and Management, Christ University, Karnataka, Bengaluru, India; Kumar C.M.N., Department of Commerce, Jain College, Karnataka, Bengaluru, India; Amar H.A., Department of Commerce, Seshadripuram Institute of Commerce and Management, Karnataka, Bengluru, India; Rakesh N., Department of Commerce, MLA Academy of Higher Learning, Karnataka, Bengaluru, India; Kumar S.V.P., School of Business and Management, Christ (Deemed to Be University), Karnataka, Bengaluru, India
Tracking Sigmoid Regression with Multicollinearity in Phase I: An Approach Incorporating Control Charts
Control Limits; Multicollinearity; Principal Component; Regression Control Chart; Sigmoid Regression
Regression and quality control are two crucial techniques that data analysis employs in improving the decision-making process. We use the sigmoid function to model the connection between independent factors and the dependent variable in sigmoid regression. When there is a significant correlation among the independent variables in a regression model, multicollinearity a statistical phenomenon exists. Multicollinearity presents problems with higher uncertainty when estimating individual coefficients possibly making it harder to identify each variable's distinct contribution to the model. By suggesting a control chart specifically designed for the sigmoid regression model, this research presents a strategy to address the impact of influential observations using regression control charts, by making use of principal component regression class estimators. Principal component regression merges from the principal component analysis and linear regression methodologies, aiming to alleviate multicollinearity issues and enhances the stability of regression models. The performance of the model is evaluated using Pearson's residuals, Deviance residuals, and residuals. This strategy is proven to be useful in real world situations demonstrated through an application in the field of sleep wellness disorder. In conclusion, this study introduces a unique control chart to manage multicollinearity in sigmoid regression, providing a new perspective on the topic to spot differences in the underlying process by highlighting trends in the residuals. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Lecture Notes in Networks and Systems, Vol-1051 LNNS, pp. 244-255.
Nancy M., Department of Statistics and Data Science, CHRIST (Deemed to be University), Bangalore, India; Joshi H., Department of Statistics and Data Science, CHRIST (Deemed to be University), Bangalore, India
Examining the Impacts and Obstacles of AI-Driven Management in Present-Day Business Contexts
artificial intelligence; Business organization; decision making; strategic planning
This paper explores the growing role of Artificial Intelligence (AI) in the management structures of modern business organizations. In order to improve operational effectiveness and overall success, it focuses on the integration and effects of AI within Management Information Systems (MIS). The study finds the many advantages artificial intelligence (AI) offers to knowledge management, resource management systems, and organizational effectiveness through a thorough analysis. The paper uses a wide range of scholarly references to explain different aspects of AI-powered management, such as strategic planning, decision-making, and sustainable marketing tactics. The study highlights a notable void in the all-encompassing comprehension of artificial intelligence's concrete contribution to business improvement, thereby promoting a deeper and more empirical investigation of AI's incorporation into business operations. 2024 IEEE.
Bhonde S.; Chib S.; Prasanna H.N.; Manasa N.; Lakshmi S.R.; Patnayak D.
Proceedings of International Conference on Communication, Computer Sciences and Engineering, IC3SE 2024, pp. 810-814.
Bhonde S., Datta Meghe Institute Of Management Studies, Madhya Pradesh, Nagpur, India; Chib S., Datta Meghe Institute Of Management Studies, Madhya Pradesh, Nagpur, India; Prasanna H.N., College Of Engineering, Sri Venkateswara, Karnataka, Bangalore, India; Manasa N., Christ (Deemed To Be University), Department Of Business And Management, Yeshwantpur campus, Karnataka, Bangalore, India; Lakshmi S.R., Christ University Nagasandra, Business & Management, Karnataka, Bangalore, India; Patnayak D., M. S. Engineering College, Computer Science And Engineering, Karnataka, Bengaluru, India
Algorithmic Trading: Financial Markets Using Artificial Intelligence
Artificial Intelligence; Financial Market; Machine Learning; Market Trends; Stock Marketing
This research study gives an in - depth view of the recent developments in the fields of Machine Learning (ML) and Reinforced Learning (RL) techniques as they are related to various models for forecasting and systems for financial trading. The practical usage of deep learning models, that incorporates Neural Networks such as Recurrent, Convolutional along with hybrid models integrating genetic algorithms with LSTM networks, for forecasting the stock market patterns as well as bank failures, and fluctuations in exchange rate which is addressed in this study in an in - depth review analysis of the latest literature. In addition to this it also investigates how trading algorithm performance as well as risk management can be enhanced by applying techniques of deep reinforcement learning. This study also demonstrates the enhanced, efficacy, precision and the profitability achieved by using these artificial intelligence methods as compared with conventional economic modelling and detailed technical study models by analysing a number of stock markets and different kinds of assets. 2024 IEEE.
Chanda S.; Chib S.; Bose S.S.; Lakshmi S.R.; Zabiullah B.I.; Samal A.
Proceedings of International Conference on Communication, Computer Sciences and Engineering, IC3SE 2024, pp. 806-809.
Chanda S., Datta Meghe Institute Of Management Studies, Madhya Pradesh, Nagpur, India; Chib S., Datta Meghe Institute Of Management Studies, Madhya Pradesh, Nagpur, India; Bose S.S., Amity Business School, Amity University, Chhattisgarh, Raipur, India; Lakshmi S.R., Business & Management, Christ University, Nagasandra, Karnataka, Bangalore, India; Zabiullah B.I., School Of Management, Cmr University, Karnataka, Bangalore, India; Samal A., Sri Venkateshwara College Of Engineering, Department Of Mba, Karnataka, Bangalore, India
Tomato Plant Disease Classification Using Transfer Learning
Accuracy; Convolutional Neural Network; Transfer learning; VGG-19
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.
2024 5th International Conference for Emerging Technology, INCET 2024
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
AI-driven systems; Banking; Biometric authentication; Ethical considerations; Facial recognition; Fingerprint recognition; Regulatory compliance; Security; User experience; Voice authentication
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.
Tandon A.; Anitha C.; Kataria A.; Mohammed N.Q.; Al-Khuzaie M.Y.; Almulla A.A.
2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024, pp. 962-967.
https://doi.org/10.1109/ICACITE60783.2024.10617124 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201831853&doi=10.1109%2fICACITE60783.2024.10617124&partnerID=40&md5=3528714a4fcf5070f41cffb6c3b3d56f
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
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.
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
Artificial intelligence; Deep learning; Image quality; Super resolution
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.
Malathy V.; Poornima M.; Varun V.L.; Venugopal P.; Acharjee P.B.; Krishnaveni S.
Smart Innovation, Systems and Technologies, Vol-390, pp. 127-140.
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
Machine learning (ML); Natural language processing; Random forest (RF); Sentiment analysis; Support vector machine (SVM)
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.
Manoharan G.; Jyothi B.S.; Bhat R.; Acharjee P.B.; Sharma A.; Rawal P.
Smart Innovation, Systems and Technologies, Vol-390, pp. 111-125.
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
6G; Aerial Access Network (AAN); Benders decomposition-based algorithm; point to point communication; Time-Expanding graphs (TEGs)
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.
Kumar V.N.; Al-Attabi K.; Thakur G.; Ambrose S.; Nirsanametla Y.; Murugajothi T.
2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024, pp. 1392-1396.
https://doi.org/10.1109/ICACITE60783.2024.10617062 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201820606&doi=10.1109%2fICACITE60783.2024.10617062&partnerID=40&md5=dd6d858e37b476759df8b1cf863bcd31
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
Data Mining; Healthcare; Machine Learning classifiers; Prediction Analysis; Symptoms
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.
Sharma K.; Madeswaran A.; Chandramauli A.; Narasimha V.; Jasim L.; Reeja Y.M.
2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024, pp. 968-972.
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
AlexNet; cervical cancer; Deep learning; DenseNet121; feature integration; ResNet50; VGG16
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.
Lecture Notes in Networks and Systems, Vol-1046 LNNS, pp. 185-194.
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
Agricultural robotics; estimating yields; farming robotic systems; gathering; land preparation; phenotyping; plant treatment; planting; precision gardening; sowing
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.
Chandrakala P.; Badholia R.; Arunkumar E.; Victor M.; Maheswaran M.; Choubey A.K.
2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024, pp. 345-349.
https://doi.org/10.1109/ICACITE60783.2024.10616892 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201811438&doi=10.1109%2fICACITE60783.2024.10616892&partnerID=40&md5=0beed41eca82cd6abbb31aff7f051581
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)
Artificial Intelligence; Data Analysis; Future Healthcare; Medical Sector; Patient Care; Technology
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.
Sarojini Y.; Pithode K.; Sandhya D.; Sivadharshini B.; Singh K.R.; Choubey S.B.
2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024, pp. 42-47.
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
Applications; Artificial Intelligence; Industry 4.0; Machine Learning; Smart Production; Sustainability; Systematic Literature Review
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.
Praveenraj D.D.W.; Vijayaraj T.; Kumar K.S.; Sakthivel N.K.; Multani F.; Nagaveni P.
2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024, pp. 178-183.
https://doi.org/10.1109/ICACITE60783.2024.10617114 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201804747&doi=10.1109%2fICACITE60783.2024.10617114&partnerID=40&md5=5bda97679cfbd0b5cc549dba6fe853f8
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
Anonymity; Cloud computing; Data traceability; Group data sharing; Group signature
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.
Pandey M.; Subramani K.; Madeswaran A.; Al-Jawahry H.M.; Sudhamalla M.; Misra N.; Kumar K.K.
2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024, pp. 1579-1582.
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
cervical cancer; Deep learning; DenseNet121; feature integration; K means; ResNet50
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.
Lecture Notes in Networks and Systems, Vol-1046 LNNS, pp. 112-122.
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
AI; Biological Computation; Biomedicine; Disease Diagnoses; Healthcare; Living Support; Neural Networks; Predictive Studies; Research; Scientific Successes; Technological Environment
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.
Priyadharsini P.; Khare N.; Praveenraj D.D.W.; Senthurya S.; Arunkumar B.; Reddy G.V.
2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024, pp. 1981-1986.
https://doi.org/10.1109/ICACITE60783.2024.10617363 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201800827&doi=10.1109%2fICACITE60783.2024.10617363&partnerID=40&md5=cd0239100bf98c7c6aa2f549eb7cd28a
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