A Predictive Modelling of Factors Influencing Job Satisfaction Through a CNN-BiGRU Algorithm
Bidirectional Gating Recurrent Unit (BiGRU); Convolutional Neural Network (CNN); Long short Term Memory (LSTM)
The fields of humanities, psychology, and sociology are where the word 'job satisfaction' originated. According to psychology, it is a condition in which a worker experiences his circumstances emotionally and responds by experiencing either pleasure or suffering. It is regarded as a variable in various sociological categories pertaining to how each employee assesses and thinks about his work. Because a satisfied employee contributes to and builds upon an organization's success, job satisfaction is intimately tied to an employee's performance and the quality of the work they do. As a result, job satisfaction directly correlates to an organization's success. The proposed strategy incorporates data preprocessing, feature selection, and model training. The missing value is a common feature of data preparation. Feature selection is chosen using the ANOVA F-Test Filter, the Chi-Square Filter, and the full Data Set Construction procedure. The model's efficacy can be evaluated with the help of CNN-BiGRU. The proposed technique is compared to two more models: BiGRU and CNN. It has been shown that our proposed technique outperforms two other models. 2023 IEEE.
Kar R.; Renjith Krishnan K.; Antony P.; Rashmi B.H.; Padmapriya S.; Unni M.V.
International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023 - Proceedings, pp. 68-73.
Institute of Electrical and Electronics Engineers Inc.
Kar R., St. Claret College, Department of Management, Bangalore, India; Renjith Krishnan K., College of Economics and Business Administration, University of Technology and Applied Sciences, Shinas, Oman; Antony P., Jyoti Nivas College Autonomous, Department of Mba, Bangalore, India; Rashmi B.H., School of Business and Management, Christ University, Bangalore, India; Padmapriya S., Christ University, Department of Commerce, Bangalore, India; Unni M.V., St. Claret College, Department of Management, Bangalore, India
Predicting Work Environment and Job Environment Among Employees using Transfer Learning Approach
Environmentally Positive Behavior(EPB); Job S atisfaction; Organizational Citizenship Behavior (OCB)
Today's enterprises face numerous challenges as a result of the world's rapid evolution. Maintaining a content workforce is crucial to a company's success and survival in today's fast-paced business environment. The efficacy, productivity, efficiency, and dedication of the company's staff are directly associated with the company's capacity to meet the needs of its employees in the workplace. The focus of this system is to identify the factors that contribute to a satisfying work environment for the participants. Preprocessing, feature selection, and model training are the first three steps in the suggested methodology. Data mining systems should get in the habit of normalizing data as a preliminary processing step. The multiple elements assessing company culture and worker satisfaction were consolidated using Principal Components Analysis (PCA) in the feature selection phase. Once features have been selected, KNN-SVM is utilized for model training. When compared to the two most popular alternatives, SVM and KNN, the proposed technique performs better. 2023 IEEE.
Shukla T.D.; Giri P.; Rana P.; Krishna V.P.; Thulasimani T.; Vanisri S.
2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023 - Proceedings, pp. 771-776.
https://doi.org/10.1109/ICACRS58579.2023.10404165 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185383619&doi=10.1109%2fICACRS58579.2023.10404165&partnerID=40&md5=8c532fa188bb9a8b74c8b1c708ef2b16
Shukla T.D., University Institute of Teachers Training and Research, Chandigarh University, Mohali, India; Giri P., School of Business and Management, Christ (Deemed to be University,), Delhi NCR, India; Rana P., Department of MBA, School of Commerce and Management, IIMT University, Meerut, India; Krishna V.P., School of Management and Commerce, Malla Reddy University, Hyderabad, India; Thulasimani T., Department of Mathematics, Bannari Amman Institute of Technology, Tamilnadu, India; Vanisri S., Department of MCA, M.Kumarasamy College of Engineering, TamilNadu, Karur, India
A Intelligent Approach for Fault Detection in Solar Photovoltaic Systems based on BERT-BiGRU Network
Gated Recurrent Unit (GRU); Solar photovoltaic (PV); Transform Invariant Low-rank Textures (TILT)
Large-scale photovoltaic (PV) plant problem identification and diagnosis is expected to grow more difficult in the future as more and more plants of increasing capacity enter into existence. To keep large-scale PV installations safe, reliable, and productive, automatic identification and localization of any mal-operation among thousands of PV modules is necessary. In order to identify problems in PV plants, the suggested method compares the 'residuals' (fault indicator signals) generated by each string to a predetermined threshold. The suggested method relies on three distinct processes: data preparation, feature extraction, and model training. Preprocessing employs the method of Transform Invariant Low-rank Textures (TILT). The most useful and efficient measurements from feature extraction are kept while less important ones are discarded in favor of the Reduced Kernel PCA technique. Let's move on to model training with BERT-BiGRU. The proposed method is clearly superior compared to the two leading options, BERT and GRU. The proposed method had a 97.36% success rate. 2023 IEEE.
Hafeezuddin M.; Garapati B.; Vijayakumar T.; Priyanka G.; Chauhan A.; Vidya A.
International Conference on Sustainable Communication Networks and Application, ICSCNA 2023 - Proceedings, pp. 685-690.
https://doi.org/10.1109/ICSCNA58489.2023.10370188 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183008871&doi=10.1109%2fICSCNA58489.2023.10370188&partnerID=40&md5=2fbb091d0eb0f45d5bc5c5f9c328acc4
Hafeezuddin M., Geethanjali College of Engineering and Technology, Department of Eee, Hyderabad, India; Garapati B., Geethanjali College of Engineering and Technology, Department of Eee, Telangana, Cheeryal, India; Vijayakumar T., Dr.Mahalingam College of Engineering and Technology, Department of Artificial Intelligence and Data Science, Coimbatore, India; Priyanka G., Sri Eshwar College of Engineering, Department of Computer Science and Business Systems, Coimbatore, India; Chauhan A., School of Sciences, Christ (Deemed to Be University), Department of Life Sciences, Karnataka, Bengaluru, India; Vidya A., K.Ramakrishnan College of Engineering, Department of Electronics and Communication Engineering, Trichy, Samayapuram, India
Evaluating the Effectiveness of a Facial Recognition-Based Attendance Management System in a Real-World Setting
Accuracy; API cognitive services; Face Recognition; Haar Cascade; LBPH; MLP; Mobile Application
Face recognition technology has been extensively used in multiple verticals of security, surveillance, and human-computer interaction. Conventional techniques including manual sign-ins, identity cards, or biometric verification have been used by traditional attendance systems. Face recognition systems have, however, become a popular way to track attendance, thanks to developments in computer vision and machine learning. The construction of an attendance registration application is the main topic of this research study, which also offers a thorough overview of facial recognition attendance systems. This study seeks to provide light on the benefits, drawbacks, and potential applications of these fast-developing technologies. Face recognition technology may be integrated into attendance systems to increase productivity, accuracy, and user comfort. However, issues like privacy worries and technological constraints must be resolved. With predicted future improvements in machine learning algorithms and hardware capabilities, face recognition attendance systems look to have a bright future. This research article adds to a deeper understanding and successful application of facial recognition technology in attendance systems by examining these features. 2023 IEEE.
2023 14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023
Nijgal D.R., Christ University, Dept. of Computer Science, Bangalore, India; George S., Christ University, Dept. of Data Science, Bangalore, India; Subramanian P., Christ University, Dept. of Computer Science, Bangalore, India
Enhancement of Accuracy Level in Parking Space Identification by using Machine Learning Algorithms
Accuracy; Computer Vision; Convolutional Neural Networks; Deep Learning; Machine Learning; Support Vector Machine
Parking space identification is a crucial component in the development of intelligent transportation systems and smart cities. Accurate detection of parking spaces in urban areas can significantly improve traffic management, reduce congestion, and enhance overall parking efficiency. This proposed model is focuses on enhancing the accuracy of parking space identification through the utilization of Support Vector Machine (SVM) algorithms. The proposed methodology involves the following steps. First, a dataset comprising labelled parking space images is collected and pre-processed to ensure optimal quality and consistency. Next, feature extraction techniques are applied to capture certain relevant spatial and textural information from the images in the dataset, enabling the creation of informative feature vectors. These feature vectors are then utilized to train a SVM model, which is well-known for its capability to handle complex classification tasks. To measure the effectiveness of the SVM-based approach, a comprehensive set of experiments is carried out using real-world parking data. The performance metrics is to analysis accuracy level of the parking space identification. Comparative analysis has been done by comparing the proposed SVM approach with other popular machine learning algorithmsto demonstrate the superiority. The results indicate that the SVM-based model achieves a significantly higher accuracy level in parking space identification compared to other existing algorithms. 2023 IEEE.
2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023 - Proceedings, pp. 595-600.
https://doi.org/10.1109/ICACRS58579.2023.10404454 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185373931&doi=10.1109%2fICACRS58579.2023.10404454&partnerID=40&md5=d6c76b583efaa5752d21a459696bc5fc
Diniz A.M., Department of CSE, CHRIST (Deemed to be University), India; Samanta D., Department of CSE, CHRIST (Deemed to be University), India; Jayapandian N., Department of CSE, CHRIST (Deemed to be University), India
Area and Energy Efficient Method Using AI for Noise Cancellation in Ear Phones
Active Noise Cancellation; Active Noise Reduction; Digital Signal Processing; Normalized Least Mean Square; Recursive Least Square and Block LMS
Adaptive filters are suitable for most of the Digital Signal Processing (DSP) applications such as channel equalization, noise cancellation, echo cancellation, channel estimation and system identification. Nowadays due to the advancement in semiconductor technology, the need for Active Noise Cancellation (ANC) headphones in compact devices is increased. The major idea behind this proposed work is to design an area and energy efficient novel adaptive filter suitable for in-ear headphones by combining Normalized Least Mean Square (NLMS) and Block LMS (BLMS). The proposed filter is designed and simulated using Xilinx ISE 13.2. The simulation results shows that the proposed design mitigates the unwanted noises in various frequency bands. 2023 IEEE.
Manikandan R.; Ramkumar S.; Prabhakaran M.
International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023 - Proceedings, pp. 117-121.
https://doi.org/10.1109/ICSSAS57918.2023.10331813 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85181067301&doi=10.1109%2fICSSAS57918.2023.10331813&partnerID=40&md5=463fa972febd5d576d8b59dbf93e7068
Manikandan R., School of Business and Management Christ (Deemed to Be University), Department of Lean Operations and Systems, Karnataka, Bengaluru, India; Ramkumar S., School of Sciences, Christ (Deemed to Be University), Department of Computer Science, Karnataka, Bengaluru, India; Prabhakaran M., Alliance College of Engineering and Design, Alliance University, Department of Computer Science and Engineering, Karnataka, Bengaluru, India
An Efficient and Robust Explainable Artificial Intelligence for Securing Smart Healthcare System
Explainable Artificial Intelligence; IoT; Security; Smart Healthcare
The advent of IoT technologies has a tremendous impact on the healthcare sector enabling efficient monitoring of patients and utilizing the data for better analytics. Since every activity related to a patients health is monitored, the focus on smart healthcare applications has significantly transferred from service provision to a security perspective. As most healthcare applications are automated security plays a vital role. The technique of machine learning has been widely used in securing smart healthcare systems. The major challenge is that these applications require high-quality labeled images, which are difficult to acquire from real-time security applications. Further, it highly time-consuming and cost-expensive process. To address these constraints, in this paper, we define an efficient and robust explainable artificial intelligence technique that takes a small quantity of labeled data to train and de-ploy the security countermeasure for targeted healthcare applications. The proposed approach enhances the security measure through the detection of drifting samples with explainability. It is observed that the proposed approach improved accuracy, high fidelity, and explanation measures. Also, this approach is proven to be considerably resistant against numerous security threats. 2023 IEEE.
Seetharaman T.; Sharma V.; Balamurugan B.; Grover V.; Agnihotri A.
2023 2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023, pp. 1066-1071.
https://doi.org/10.1109/SmartTechCon57526.2023.10391664 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184799769&doi=10.1109%2fSmartTechCon57526.2023.10391664&partnerID=40&md5=c133e7ba5cc502b676dd4c3bda212643
Seetharaman T., Department of CSE, CMR University, Bangalore, India; Sharma V., Computer Science Department, CHRIST (Deemed to be University), India; Balamurugan B., Shiva Nadar University, India; Grover V., School of Management, Noida Institute of Engineering and Technology, India; Agnihotri A., School of Business, Galgotias University, India
Unraveling Campus Placement Success Integrating Exploratory Insights with Predictive Machine Learning Models
EDA; Machine Learning; Performance analysis; Placement Prediction; Students
The dynamics of campus placements have garnered considerable attention in recent years, with educational institutions, students, and employers all keenly invested in understanding the factors that drive successful recruitment. This surge in interest stems from the potential implications for academic curricula, student preparation, and hiring strategies. In this study, we aimed to unravel the myriad factors that influence a student's placement success, drawing from a comprehensive dataset detailing a range of academic and demographic attributes. Our methodology combined thorough exploratory data analysis with advanced predictive modeling. The exploratory phase unveiled notable patterns, particularly highlighting the roles of gender, academic performance analysis, Degree and MBA specialization in placement outcomes. In the predictive modeling phase, the spotlight was on state-of-the-art machine learning models, with a particular emphasis on their capacity to forecast placement success. Notably, algorithms like Logistic Regression and Support Vector Machines not only confirmed the insights from our exploratory analysis but also showcased remarkable predictive prowess, with accuracy scores nearing perfection. These findings not only demonstrate the capabilities of machine learning in the academic and recruitment spheres but also emphasize the enduring importance of core academic achievements in influencing placement outcomes. As a prospective direction, future research might benefit from examining how placement trends evolve over time and integrating qualitative insights to provide a holistic view of the campus recruitment process. 2023 IEEE.
Amzad Basha M.S.; Kumar Raju R.P.; Oveis P.M.; Sarim M.
7th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023 - Proceedings
Amzad Basha M.S., Gitam School of Business, Gandhi Institute of Technology and Management (Deemed to Be University), Bengaluru, India; Kumar Raju R.P., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Oveis P.M., Gitam School of Business, Gandhi Institute of Technology and Management (Deemed to Be University), Bengaluru, India; Sarim M., Gitam School of Business, Gandhi Institute of Technology and Management (Deemed to Be University), Bengaluru, India
A Comparative Analysis of LSB & DCT Based Steganographic Techniques: Confidentiality, Contemporary State, and Future Challenges
Data Hiding; Data Security; Image Steganography; Imperceptibility; Information Hiding; LSB insertion; Steganalysis
In order to maintain anonymity and security, the steganography is the technique of cloaking confidential data within what seems like harmless digital material. Several steganographic methods have been established devised over time, but those centered around the discrete cosine transformation (DCT) and the least significant bit (LSB) have drawn the most consideration. In this study, two common steganographic methods are compared and contrasted with an emphasis on the secrecy they can keep, the usage they are now receiving, and any potential difficulties in the future. As an alternative, the DCT-based method uses the frequency domain properties of cover media to obfuscate hidden information. Since it spreads the concealed information across several frequency coefficients, it provides greater security than LSB-based techniques. The resilience and imperceptibility of the concealed data are improved by a variety of DCT-based algorithms, such as the modified quantization and matrix encoding approaches, which we explore in detail. We also give a general summary of both approaches'current state in terms of their application, constraints, and areas in which they may be used. We evaluate the benefits and drawbacks of each approach, considering elements like payload size, computing difficulty, and detection resistance. 2023 IEEE.
Upreti K.; Verma A.; Parashar J.; Vats P.; Verma A.; Singh J.
Proceedings of International Conference on Contemporary Computing and Informatics, IC3I 2023, pp. 1581-1588.
Upreti K., CHRIST (Deemed to Be University) Delhi NCR, Department of Computer Science, Ghaziabad, India; Verma A., Department of Computer Science & Engineering ADGITM, New Delhi, India; Parashar J., Department of Computer Science & Engineering ADGITM, New Delhi, India; Vats P., SCSE Manipal University Jaipur Jaipur, Department of CSE, Rajasthan, India; Verma A., KIET Group of Institutions Delhi -NCR, Department of Computer Applications, Ghaziabad, India; Singh J., School of CSE and Technology, Bennett University Greater, Noida, India
Prior Cardiovascular Disease Detection using Machine Learning Algorithms in Fog Computing
Decision tree; Fog computing; Heart disease; K- Nearest Neighbor; Logistic regression; Random Forest; Support Vector Machine
The term latent disease refers to an infection that does not show symptoms but remains forever. In this paper, proposed a novel methodology for addressing latent diseases in machine learning by integrating fog computing techniques. Here there is a link between HIV to heart disease, that is when a person progresses to the next stage of HIV, a plague infection develops, causing cholesterol deposits to form. Plaque development causes the inside of the arteries to constrict over time, which may stimulate the release of numerous heat shock proteins and immune complexes into the bloodstream, potentially leading to heart disease. Heart disease has long been considered as a significant life-threatening illness in humans. Heart disease is driven by a range of factors including unhealthy eating, lack of physical exercise, gaining overweight, tobacco, as well as other hazardous lifestyle choices. Five different classifiers are used to perform the precision; they are Support vector machine, K-nearest neighbor, decision tree, and random forest, after we have used the classifier, the recommended ideal will split disease into groups which is created based on their threat issues. This will be beneficial to doctors assisting doctors in analyzing the risk factors associated with their patients. 2023 IEEE.
Suresh K.; Subramani R.; Rio N.; Donald C.; Sinha S.; Soman S.
2023 IEEE 3rd Mysore Sub Section International Conference, MysuruCon 2023
https://doi.org/10.1109/MysuruCon59703.2023.10396992 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184811941&doi=10.1109%2fMysuruCon59703.2023.10396992&partnerID=40&md5=8241092230fbdf9cb72cdca907f410e6
Suresh K., CHRIST (Deemed to Be University), Department of Computer Science, Bangalore, India; Subramani R., CHRIST (Deemed to Be University), Department of Mathematics, Bangalore, India; Rio N., CHRIST (Deemed to Be University), Department of Computer Science, Bangalore, India; Donald C., CHRIST (Deemed to Be University), Department of Computer Science, Bangalore, India; Sinha S., CHRIST (Deemed to Be University), Department of Computer Science, Bangalore, India; Soman S., GITAM University, Department of Computer Science, Bangalore, India
Enhancing Customer Experience and Sales Performance in a Retail Store Using Association Rule Mining and Market Basket Analysis
Apriori algorithm; Association rule mining; Eclat algorithm; FP-Growth algorithm; Market Basket Analysis; Sales performance
The retail business grows steadily year after year andemploys an abounding amounts of people globally, especially with the soaring popularity of online shopping. The competitive character of this fast-paced sector has been increasingly evident in recent years. Customers desire to blend the advantages of old purchasing habits with the ease of use of new technology. Retailers must thus guarantee that product quality is maintained when it comes to satisfying customer demands and requirements. This research paper demonstrates the potential value of advanced data analytics techniques in improving customer experience and sales performance in a retail store. Apriori, FP-Growth, and Eclat algorithms are applied in the real time transactional data to discover sociations and patterns in transactional data. Support, confidence and lift ratio parameters are used and apriori algorithm puts out several candidate item sets of increasing lengths and prunes those that fail to offer the assistance that is required threshold. We identified lift values are more when considering frozen meat, milk, and yogurt. if the customer decides to buy any of these items together, there is a chance that the customer will buy 3rd item from that group. Research arrived High confidence score is for Items like Semi Finished Bread and Milk so these products should be sold together, Followed by Packaged food and rolls. As retailers continue to face increasing competition and pressure to improve their operations, The aforementioned techniques may provide you a useful tool to comprehend consumer buying habits and tastes and for utilising that knowledge to come up with data-driven decisions that optimise product placement, enhance customer satisfaction, and attract sales. 2023 IEEE.
https://doi.org/10.1109/ICCCNT56998.2023.10307411 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179844042&doi=10.1109%2fICCCNT56998.2023.10307411&partnerID=40&md5=07a92cac65fcfa33b862d61a1160b5b5
Helen Josephine V.L., School of Business and Management, Christ University, Business Analytics, Bangalore, India; Rajan D., Cmr Institute of Technology, Department of Mca, Bangalore, India
A Dynamic Anomaly Detection Approach for Fault Detection on Fire Alarm System Based on Fuzzy-PSO-CNN Approach
Convolutional Neural Network (CNN); Fire Alarm System; Particle Swarm Optimization (PSO)
Early detection is crucial due to the catastrophic threats to life and property that are involved with fires. Sensory systems used in fire alarms are prone to false alerts and breakdowns, endangering lives and property. Therefore, it is essential to check the functionality of smoke detectors often. Traditional plans for such systems have included periodic maintenance; however, because they don't account for the condition of the fire alarm sensors, they are sometimes carried out not when necessary but rather on a predefined conservative timeframe. They describe a data-driven online anomaly detection of smoke detectors, which analyzes the behavior of these devices over time and looks for aberrant patterns that may imply a failure, to aid in the development of a predictive maintenance approach. The suggested procedure begins with three steps: preprocessing, segmentation, and model training. A pre-processing unit can enhance data quality by compensating for sensor drifts, sample-to-sample volatility, and disturbances (noise). The proposed approach normalizes the data in preparation. The smoke source can be detected by using segmentation to differentiate it from the background. Following segmentation, Fuzzy-PSO-CNN is used to train the models. CNN and PSO, two of the most used alternatives, are both outperformed by the proposed method. 2023 IEEE.
Kanagaraj K.; Amruthaluru U.; Selvaganesan J.; Singh Y.; Janakiraman V.; Chauhan A.
International Conference on Sustainable Communication Networks and Application, ICSCNA 2023 - Proceedings, pp. 1124-1129.
Kanagaraj K., Srm Institute of Science and Technology, Department of Computational Intelligence, Chennai, India; Amruthaluru U., Vardhaman College of Engineering, Department of Cse, Telangana, India; Selvaganesan J., Veltech Rangarajan Dr Sagunthala R&d Institute of Science and Technology, Department of Electronics and Communication Engineering, Chennai, India; Singh Y., Mausam Bhawan, India Meteorological Department, New Delhi, India; Janakiraman V., Prince Shri Venkateshwara Padmavathy Engineering College, Department of Mechanical Engineering, Chennai, India; Chauhan A., School of Sciences, Christ (Deemed to Be University), Department of Life Sciences, Karnataka, Bengaluru, India
Utilizing Machine Learning for Sport Data Analytics in Cricket: Score Prediction and Player Categorization
Cricket analytics; Cricket prediction; Machine Learning; Player Classification; Regression and Classification; Score prediction
Cricket is a popular sport with complex gameplay and numerous variables that contribute to team performance. In recent years, sports analytics has gained significant attention, aiming to extract valuable insights from large volumes of cricket data. Cricket has many fans in India. With a strong fan following, many try to use their cricket intuition to predict the outcome of a match. A set of rules and a points system govern the game. The venue and the performance of each player greatly affect the outcome of the match. The game is difficult to predict accurately as the various components are closely related. The CRR (Current Run Rate) approach is used to predict the final score of the first innings of a cricket match. Total points are calculated by multiplying the average number of runs scored in each over by the total number of overs. For ODI cricket, these methods are useless as the game can change very quickly regardless of the current run rate. The game may be decided by 1 or 2 overs. For more accurate score predictions, a system is needed that can more accurately predict the outcome of an inning. This research paper explores the application of machine learning techniques to predict scores and classify players based on their roles in the squad. The study utilizes a comprehensive dataset comprising various attributes of cricket matches, including player statistics, match conditions, and historical performance. Linear Regression, Logistic Regression, Naive Bayes, Support Vector Machines (SVM), Decision Tree, and Random Forest regression models are employed to predict scores. Additionally, player categorization is performed using a classification approach. The results demonstrate the effectiveness of machine learning techniques in enhancing performance analysis and decision-making in the game of cricket. 2023 IEEE.
Suguna R.; Praveen Kumar Y.; Suriya Prakash J.; Neethu P.S.; Kiran S.
https://doi.org/10.1109/MysuruCon59703.2023.10396955 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184821608&doi=10.1109%2fMysuruCon59703.2023.10396955&partnerID=40&md5=69cf6c86786d9dc70ec4b7a34b10ed00
Suguna R., Vel Tech Rangarajan Dr. Sagunthala RD Institute of Science and Technology, Department of Computer Science and Engineering, Chennai, India; Praveen Kumar Y., Vidhya Jyothi Institute of Technology, Department of Computer Science and Engineering, Hyderabad, India; Suriya Prakash J., JAIN (Deemed-to-be-University), Department of Computer Science and Engineering, Bengaluru, India; Neethu P.S., CHRIST (Deemed to Be University), School of Engineering and Technology, Bengaluru, India; Kiran S., Nitte Meenakshi Institute of Technology, Department of Mathematics, Bangalore, India
The Troubling Emergence of Hallucination in Large Language Models - An Extensive Definition, Quantification, and Prescriptive Remediations
The recent advancements in Large Language Models (LLMs) have garnered widespread acclaim for their remarkable emerging capabilities. However, the issue of hallucination has parallelly emerged as a by-product, posing significant concerns. While some recent endeavors have been made to identify and mitigate different types of hallucination, there has been a limited emphasis on the nuanced categorization of hallucination and associated mitigation methods. To address this gap, we offer a fine-grained discourse on profiling hallucination based on its degree, orientation, and category, along with offering strategies for alleviation. As such, we define two overarching orientations of hallucination: (i) factual mirage (FM) and (ii) silver lining (SL). To provide a more comprehensive understanding, both orientations are further sub-categorized into intrinsic and extrinsic, with three degrees of severity - (i) mild, (ii) moderate, and (iii) alarming. We also meticulously categorize hallucination into six types: (i) acronym ambiguity, (ii) numeric nuisance, (iii) generated golem, (iv) virtual voice, (v) geographic erratum, and (vi) time wrap. Furthermore, we curate HallucInation eLiciTation (), a publicly available dataset comprising of 75,000 samples generated using 15 contemporary LLMs along with human annotations for the aforementioned categories. Finally, to establish a method for quantifying and to offer a comparative spectrum that allows us to evaluate and rank LLMs based on their vulnerability to producing hallucinations, we propose Hallucination Vulnerability Index (HVI). Amidst the extensive deliberations on policy-making for regulating AI development, it is of utmost importance to assess and measure which LLM is more vulnerable towards hallucination. We firmly believe that HVI holds significant value as a tool for the wider NLP community, with the potential to serve as a rubric in AI-related policy-making. In conclusion, we propose two solution strategies for mitigating hallucinations. 2023 Association for Computational Linguistics.
Rawte V.; Chakraborty S.; Pathak A.; Sarkar A.; Tonmoy S.M.T.I.; Chadha A.; Sheth A.; Das A.
EMNLP 2023 - 2023 Conference on Empirical Methods in Natural Language Processing, Proceedings, pp. 2541-2573.
Association for Computational Linguistics (ACL)
Rawte V., AI Institute, University of South Carolina, United States; Chakraborty S., Christ University, India; Pathak A., Christ University, India; Sarkar A., Christ University, India; Tonmoy S.M.T.I., Islamic University of Technology, Bangladesh; Chadha A., Stanford University, United States, Amazon AI, United States; Sheth A., AI Institute, University of South Carolina, United States; Das A., AI Institute, University of South Carolina, United States
Autonomous Driving; Driverless; Kalman Filter; Lane Detection
Autonomous vehicles are the future of transportation. Modern high-tech vehicles use a sequence of cameras and sensors and in order to assess their atmosphere and aid to the driver by generating various alerts. While driving, it is always a challenging task for drivers to notice lane lines on the road, especially at night time, it becomes more difficult. This research proposes a novel way to recognize lanes in a variety of environments, including day and night. First various pre-processing techniques are used to improve and filter out the noise present in the video frames. Then, a sequence of procedure with respect to lane detection is performed. This stable lane detection is achieved by Kalman filter, by removing offset errors and predict future lane lines. 2023 Elsevier B.V.. All rights reserved.
Subramani R.; Suresh K.; Donald C.; Vijayalakshmi C.
Procedia Computer Science, Vol-230, pp. 598-607.
https://doi.org/10.1016/j.procs.2023.12.115 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184082311&doi=10.1016%2fj.procs.2023.12.115&partnerID=40&md5=466a6174636b3cb1d0f87d66fa82abf1
Subramani R., Department of Mathematics, CHRIST (Deemed to be University, Bengaluru, India; Suresh K., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India; Donald C., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India; Vijayalakshmi C., Department of Statistics and Applied Mathematics, Central University of Tamil Nadu, Thiruvarur, India
An Enhanced Data-Driven Weather Forecasting using Deep Learning Model
Convolutional Neural Network; Deep Learning; Weather Forecasting; Weather Prediction
Predicting present climate and the evolution of the ecosystem is more crucial than ever because of the huge climatic shift that has occurred in nature. Weather forecasts normally are made through compiling numerical data on from the atmospheric state at the moment and also applying scientific knowledge in the atmospheric processes to forecast on how the weather atmosphere would evolve. The most popular study subject nowadays is rainfall forecasting because of complexity in handling the data processing in addition to applications in weather monitoring. Four different state temperature data were collected and applied deep learning methods to predict the temperature level in the forthcoming months. The results brought out with the accuracy from 92.5% to 97.2% for different state temperature data. 2023 IEEE.
Krishnasamy L.; Anand D.V.; Vigneshwaran T.; Pathak L.K.; Maheswaran S.
https://doi.org/10.1109/ICCCNT56998.2023.10307319 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179841437&doi=10.1109%2fICCCNT56998.2023.10307319&partnerID=40&md5=6d8c79ae8d95be2f6ff764096fd0a33a
Krishnasamy L., Christ University, School of Engineering and Technology, Department of Cse, Bengaluru, India; Anand D.V., Kongu Engineering College, Department of Information Technology, Erode, India; Vigneshwaran T., Christ University, School of Engineering and Technology, Department of Cse, Bengaluru, India; Pathak L.K., Christ University, School of Engineering and Technology, Department of Cse, Bengaluru, India; Maheswaran S., Kongu Engineering College, Department of Electronics and Communication Engineering, Erode, India
A Study on the Factors Affecting Infants' Health-Related Issues and Child Mortality using Machine Learning
Child mortality; decision support; infants' health; machine learning; predictive modeling; risk assessment
Child mortality and infant health-related issues remain significant challenges worldwide. Understanding the factors that influence these outcomes is crucial for implementing effective interventions and improving child health outcomes. In this study, we employ machine learning techniques to identify and analyze the key factors affecting infants' health-related issues and child mortality. Further, we identify several significant factors that influence infants' health-related issues and child mortality. These factors include maternal health indicators, access to healthcare services, socioeconomic status, environmental factors, and demographic characteristics. The machine learning models provide insights into the relative importance of these factors, enabling policymakers and healthcare professionals to prioritize interventions and allocate resources effectively. Additionally, we investigate the potential interaction effects among these factors and their impact on child health outcomes. This analysis helps in understanding the complex relationships and causal pathways involved in infants' health-related issues and child mortality. The findings of this study contribute to the existing knowledge by leveraging machine learning techniques to identify and analyze the factors affecting infants' health-related issues and child mortality. The insights gained from this research can inform evidence-based policies and interventions aimed at reducing child mortality rates and improving infant health outcomes globally. By addressing the underlying factors identified through this study, we can work towards achieving better health outcomes for infants and reducing the burden of child mortality worldwide. 2023 IEEE.
2023 2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023, pp. 615-624.
https://doi.org/10.1109/SmartTechCon57526.2023.10391399 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184811405&doi=10.1109%2fSmartTechCon57526.2023.10391399&partnerID=40&md5=c2a75a52748fb1dbcc35e83a4f0c9370
Verma I., School of Computing Science and Engineering, Galgotias University, UP, Greater Noida, India, CHRIST (Deemed to be university) Delhi NCR, Ghaziabad, India; Prasad S.K., School of Computing Science and Engineering, Galgotias University, UP, Greater Noida, India
An Integrated and Optimized Fog Computing enabled Framework to minimize Time Complexity in Smart Grids
Cloud Computing; Edge Computing; Fog Computing; Smart Grid
A distributed computing paradigm known as 'cloud computing'works as a connection between IoT devices and cloud data centres. The environment system model in this work is on basis of clouds and fog and includes smart grids, which we explore. Prior to understanding the use of fog computing in smart grids we discuss about various features of cloud computing and talk about how to manage the connection between fog and cloud computing. Along with the usual performance of low latency, low cost, and high intelligence, the distinctive characteristics and service scenarios are also explored. Based on the outcome of the simulation, it appears that our suggested PSO-SA algorithm outperforms other optimization algorithms. It recorded a least mean response time of 3.86 seconds only. While the model build up delay was 4.6 seconds, the model execution delay was also found to be only 4.9 seconds with PSO-SA method. The improved efficiency of the technique can be credited to the best aspects of particle swarm optimisation (PSO) and a modified inertia weight obtained by simulated annealing. 2023 IEEE.
Nag R.; Samantraj A.S.; Mishra S.; Sharma V.
Proceedings of International Conference on Contemporary Computing and Informatics, IC3I 2023, pp. 455-459.
Nag R., School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, India; Samantraj A.S., School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, India; Mishra S., School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, India; Sharma V., CHRIST (Deemed to Be University), Computer Science Department, Delhi NCR, India
AUTONOMOUS IOT MOVEMENT IN HOSTILE AREAS USING ROBOTICS AND DEEP FEDERATED ALGORITHMS
Autonomous IoT; Deep Federated Algorithms; Hostile Areas; Robotics
Innovative solutions are required when Internet of Things (IoT) devices are deployed in hostile or difficult locations to ensure dependable and effective operation. In order to enable autonomous IoT mobility in such challenging circumstances, this study suggests a novel approach integrating robotics and deep federated algorithms. Robotics and IoT can work together to create a system that can adapt to dangerous environments, extreme weather conditions, and unexpected terrain. Deep federated algorithms further improve system performance by facilitating dispersed device collaboration for learning while protecting data privacy. The suggested framework covers the issues of communication stability, energy optimization, and real-time decision-making. We illustrate the practicality of this strategy in strengthening the dependability and efficiency of IoT deployments in hostile situations through simulations and tests. 2023 IEEE.
Archana U.; Roslin Dayana K.; Jayadurga R.; Sahaana G.; Praveena K.; Saravanan V.
2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering, RMKMATE 2023
Archana U., Department of Computer Science and Design, R.M.K. Engineering College, Tamilnadu, Kavaraipettai, India; Roslin Dayana K., Department of Computer Science and Engineering, R.M.D. Engineering College, Tamilnadu, Kavaraipettai, India; Jayadurga R., Department of Computer Science, Christ University, Yeshwantpur(YPR) Campus, Karnataka, Bengaluru, India; Sahaana G., Department of ECE, Veltech Multitech Dr Rangarajan Dr Shakuntala Engineering College, Tamilnadu, Chennai, India; Praveena K., Department of ECE, Mohan Babu University, Erstwhile Sree Vidyanikethan Engineering College, Andhra Pradesh, Tirupati, India; Saravanan V., Department of Computer Science, Dambi Dollo University, Oromia Region, Dambi Dollo, Ethiopia
Mapping the Landscape of Business Intelligence Research: A Bibliometric Approach
Diamonds; Estimated values; Machine learning; Regression Models and Cross validation
The integration of Business Intelligence (BI) is an essential element in contemporary enterprises, facilitating the conversion of voluminous data into valuable insights to support informed decision-making. Consequently, a considerable body of literature has been devoted to investigating the utilization of Business Intelligence (BI) in enhancing company efficiency and competitiveness. The present investigation employs bibliometric methods as a means to examine the research pertaining to Business Intelligence (BI). This includes an examination of the main writers and universities, publication patterns, and the intellectual framework of the domain. This investigation centers on the timeframe spanning from 2000 to 2022 and scrutinizes a corpus of 3729 Scopus articles pertaining to business intelligence. The findings suggest that the domain of Business Intelligence (BI) has experienced a substantial expansion recently. The study's results reveal significant contributors, establishments, nations, and references in the discipline, along with developing research patterns and prospects for further investigation. In general, this research emphasizes the significance of bibliometric evaluation as a means of comprehending the present status of BI research and discovering approaches to enhance the utilization of BI in contemporary organizational decision-making procedures. This study has the potential to provide valuable insights into the present state of research within the field, pinpoint significant trends and themes, and highlight potential avenues for future research. 2023 IEEE.
Basha M.S.A.; Raju R.P.K.; Uma Devi A.; Raja S.; Maheshwari A.
Basha M.S.A., Gitam School of Business, Gandhi Institute of Technology and Management, Deemed to Be University, Bengaluru, India; Raju R.P.K., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Uma Devi A., Srm Valliammai Engineering College, Department of Management Studies, Chengalpattu, India; Raja S., School of Management, Vel-Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology, Avadi, India; Maheshwari A., School of Commerce, Finance and Accountancy Christ (Deemed to Be University), Ghaziabad, India