Exploring the Opportunities of AI Integral with DL and ML Models in Financial and Accounting Systems
accounting; case studies; empirical analysis; ethical considerations; Explainable AI; financial decisionmaking; interpretability; regulatory compliance; transparency; XAI techniques
With the integration of artificial intelligence (AI), today's fast financial landscape increasingly promises the most efficient and accurate processes for decision-making in accounting practices. On the other hand, the opacity of models represents a truly difficult challenge, given that transparency and accountability are key for using AI in making financial decisions. This is a research paper that focuses on the explanation of an XAI model application as a way of improving transparency in financial decision-making within the accounting field. The paper begins by outlining how transparency is important and opens the room for trust and understanding in the process of financial decision-making. Traditional black-box AI models, although able to provide remarkable predictions, usually exhibit low interpretability; this entails that stakeholders may have a small degree of understanding regarding the rationale behind the decisions. This provides a cloudy appearance not to hamper trust and supports compliance with regulatory standards like GAAP (Generally Accepted Accounting Principles) and IFRS (International Financial Reporting Standards). The proposed work applies to the accounting domain and brings about some of the different XAI techniques that are designed under this domain. The following techniques aim at demystifying the AI algorithms for effective AI stakeholders' understanding of the model predictions and underlying decision-making processes. 2024 IEEE.
Mishra R.K.; Srivastava S.; Singh S.; Upadhyay M.K.
2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024, pp. 999-1003.
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.1109/ICACITE60783.2024.10616847 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201811077&doi=10.1109%2fICACITE60783.2024.10616847&partnerID=40&md5=b08cfd72a7ae7de7adb7fbe1717062b2
Mishra R.K., Christ University, Bengaluru, India; Srivastava S., Christ University, Bengaluru, India; Singh S., Christ University, Bengaluru, India; Upadhyay M.K., St. Xavier's College of Management & Technology, Patna, India
Comparative Analysis of Predictive Models for Customer Churn Prediction in the Telecommunication Industry
Attrition; customer segmentation; retention
To determine the best model for churn prediction in the telecom industry, this paper compares 11 machine learning algorithms namely Logistic Regression, Support Vector Machine, Random Forest, Decision Tree, XGBoost, LightGBM, Cat Boost, AdaBoost, Extra Trees, Deep Neural Network, and Hybrid Model (MLPClassifier). It also aims to pinpoint the top three factors that lead to customer churn and conducts customer segmentation to identify vulnerable groups. The results indicate that the Logistic Regression model performs the best, with an F1 score of 0.6215, 81.76% accuracy, 68.95% precision, and 56.57% recall. The top three attributes that cause churn are found to be tenure, Internet Service Fiber optic, and Internet Service DSL; conversely, the top three models in this article that perform the best are Logistic Regression, Deep Neural Network, and AdaBoost. The K means algorithm is applied to establish and analyze four different customer clusters. This study has effectively identified customers that are at risk of churn and may be utilized to develop and execute strategies that lower customer attrition. 2024 IEEE.
Proceedings of International Conference on Communication, Computer Sciences and Engineering, IC3SE 2024, pp. 534-539.
Christopher D., Christ (Deemed To Be University), Delhi NCR, India; Anand G., Christ (Deemed To Be University), Delhi NCR, India
Exploring the Adoption Readiness of the Indian Generation for Social Media Payments: An In-Depth Analysis of WhatsApp Payments
Extended UTAUT2; FinTech; Social media payment; WhatsApp payment
Advancements in technologies always get higher acceptance among people. Regarding payment technologies, integrating payment facility in the Social Media platform are considered a second-generation payment technology. With the introduction of Hike wallets and WhatsApp payment, unprecedented opportunities are available to the users. In India, with the introduction of WhatsApp on November 2020, the users of FinTech got opened a gateway to social media payment. Social Media payments are considered easy and convenient, but is the Indian generation, especially people born in the internet phase (Gen Y and Gen Z), ready to adopt WhatsApp payment. The current study was done to investigate the elements that contribute to the acceptance and use of the WhatsApp payment service in India. To attain this objective, we used an extended UTAUT2 model with the moderating effect of generation. The data was gathered from 265 respondents and analyzed using the PLS-SEM method. The results of the study outlined that Gen Z is strengthening the moderating effect only between the facilitating conditions of the users and the actual usage of WhatsApp payment. The practical implications and directions for the further research are mentioned in the study. ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.
Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Vol-517 LNICST, pp. 1-15.
Springer Science and Business Media Deutschland GmbH
Joseph D., Department of Commerce, CHRIST (Deemed to be University), Bangalore, India; Girish S., Department of Commerce, CHRIST (Deemed to be University), Bangalore, India
Optimization of Friction Stir Welding of AlCu Butt Joint Using Taguchi Method
Friction stir welding; Hardened OHNS steel tool; Impact load
In this work, the 5mm thickness of base metals AA6101 and C11000 was welded using a hardened OHNS steel tool by FSW mechanism. The Taguchi method involves the optimization of welding mechanism variables tool rotation speed (rpm), feed rate (mm/min), and tool offset (mm) to gain extremely rigid joints. The ANOVA reveals the percentage contribution of the three welding mechanism variables can be examined. From the Taguchi design of optimization technique, at 1000rpm, 40mm/min, andtool offset towards softer metal will possess maximum impact load. The tools rotating speed produced the greatest contribution to the impact load. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Lecture Notes in Mechanical Engineering, pp. 53-61.
Kumar J.P., Department of Mechanical and Automobile Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560074, India; Raj A., Department of Mechanical and Automobile Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560074, India
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.
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.
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