An Early-Stage Diabetes Symptoms Detection Prototype using Ensemble Learning
Diabetes; Ensemble Learning; Healthcare; Machine Learning; Smart App
Diabetes is one of the most increasing health issues that the whole world is facing. Recent research has shown that diabetes is spreading quickly in India. Having more than 77 million sufferers, India is actually regarded as the diabetes capital of the world. The lifestyle and eating patterns of people who move from rural to urban settings alter, which raises the prevalence of diabetes. Diabetes has been linked to consequences like vision loss, renal failure, nerve damage, cardiovascular disease, foot ulcers, and digestive issues. Diabetes can harm the blood arteries and neurons in a variety of organs. FPG (Flaccid Plasma Glucose) is a popular test that is done to find out whether a person is a diabetic patient or not. However, not all people consistently take medication and neither monitor their blood sugar levels on a regular basis. Early detection of this disease is also an important thing that people usually don't do. Technology these days has emerged a lot in the healthcare zone. Many prototypes have already been made for the detection of diabetes. The prototype discussed in this paper is an ensemble learning approach for the detection of diabetes in a very early stage. Ensemble learning which includes the use of multiple model prediction has been used to make the outcome stronger and more trustworthy. The overall accuracy achieved by the model is 96.54%. XGBoost also records the minimal execution time of 2.77 seconds only. 2023 IEEE.
Nandi A.; Pal S.; Mishra A.; Sharma V.; Mishra S.; Alkhayyat A.
Proceedings of International Conference on Contemporary Computing and Informatics, IC3I 2023, pp. 507-512.
Institute of Electrical and Electronics Engineers Inc.
Nandi A., Kalinga Institute of Industrial Technology, Deemed to Be University, Bhubaneswar, India; Pal S., Kalinga Institute of Industrial Technology, Deemed to Be University, Bhubaneswar, India; Mishra A., Kalinga Institute of Industrial Technology, Deemed to Be University, Bhubaneswar, India; Sharma V., CHRIST (Deemed to Be University), Computer Science Department, Delhi NCR, India; Mishra S., Kalinga Institute of Industrial Technology, Deemed to Be University, Bhubaneswar, India; Alkhayyat A., College of Technical Engineering, The Islamic University, Najaf, Iraq
Sustainable Technologies for Recycling Process of Batteries in Electric Vehicles
Batteries; Electric Vehicle; Energy; MATLAB; Re-cycle; sustainability
The effective management of batteries has always been a key concern for people because of the imposing challenges posed by battery waste on the environment. This paper explores strategic perspectives on the sustainable management of batteries incorporating modern techniques and scientific methodologies giving batteries a second-life application. A paradigm shift towards the legitimate use of the batteries by the introduction of round economy for battery materials and simultaneously checking the biological impression of this fundamental innovation area. 2023 IEEE.
Majhi I.; Kapoor S.; Mishra S.; Sharma V.
2023 10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering, UPCON 2023, pp. 1082-1088.
https://doi.org/10.1109/UPCON59197.2023.10434404 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187356352&doi=10.1109%2fUPCON59197.2023.10434404&partnerID=40&md5=66251539ccd4696d9cab64ff9adee84e
Majhi I., Kalinga Institute of Industrial Technology, Deemed to Be University, India; Kapoor S., Kalinga Institute of Industrial Technology, Deemed to Be University, India; Mishra S., Kalinga Institute of Industrial Technology, Deemed to Be University, India; Sharma V., Christ (Deemed to Be University), Delhi NCR, India
An Improved Image Up-Scaling Technique using Optimize Filter and Iterative Gradient Method
corner averaging; high resolution; low resolution; optimized filter component
In numerous realtime applications, image upscaling often relies on several polynomial techniques to reduce computational complexity. However, in high-resolution (HR) images, such polynomial interpolation can lead to blurring artifacts due to edge degradation. Similarly, various edge-directed and learning-based systems can cause similar blurring effects in high-frequency images. To mitigate these issues, directional filtering is employed post corner averaging interpolation, involving two passes to complete the corner average process. The initial step in low-resolution (LR) picture interpolation involves corner pixel refinement after averaging interpolation. A directional filter is then applied to preserve the edges of the interpolated image. This process yields two distinct outputs: the base image and the detail image. Furthermore, an additional cuckoo-optimized filter is implemented on the base image, focusing on texture features and boundary edges to recover neighboring boundary edges. Additionally, a Laplacian filter is utilized to enhance intra-region information within the detailed image. To minimize reconstruction errors, an iterative gradient approach combines the optimally filtered image with the sharpened detail image, generating an enhanced HR image. Empirical data supports the effectiveness of the proposed algorithm, indicating superior performance compared to state-of-the-art methods in terms of both visual appeal and measured parameters. The proposed method's superiority is demonstrated experimentally across multiple image datasets, with higher PSNR, SSIM, and FSIM values indicating better image degradation reduction, improved edge preservation, and superior restoration capabilities, particularly when upscaling High-Frequency regions of images. 2023 IEEE.
Poonia R.C.; Upreti K.; Hundekari S.; Dadhich P.; Malik K.; Kapoor A.
3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023
Poonia R.C., CHRIST(Deemed to Be University), Dept. of Computer Science, Ghaziabad, Delhi NCR, India; Upreti K., CHRIST(Deemed to Be University), Dept. of Computer Science, Ghaziabad, Delhi NCR, India; Hundekari S., MIT ADT University Rajbaug Loni Kalbhor, Pune, India; Dadhich P., School of Computer Application, Manav Rachna International Institute of Research and Studies, India; Malik K., School of Law, CHRIST(Deemed to Be University), Ghaziabad, Delhi NCR, India; Kapoor A., Maharaja Surajmal Institute of Technology, Dept. of Computer Science, Delhi, India
Artificial Intelligence; Chatbots; ChatGPT; Education; Technology
An artificial intelligence based Chatbot, ChatGPT was launched by Open AI in November 2022. In the field of education, ChatGPT has several benefits as well as challenges. Chat GPT can be considered as an advanced and a powerful tool to enhance the learning experience. It adds value to the education system only when it is used wisely. However, it is important to understand that the challenges must be addressed. It may act as a good source for collating the information, but it is always advised by the researchers that ones own perspective must be added to draw inferences from the output generated by ChatGPT. Our study supports the finding that ChatGPT can be used for the generation of ideas or to learn a new language. It also becomes imperative for the faculties to motivate students to use ChatGPT and add their inferences as well. AI models like ChatGPT can provide assistance, answer questions and provide explanations on various topics, making learning more accessible and tailored to individual needs. With this paper, we aim to provide a more informed discussion around the usage of ChatGPT in education. 2023 IEEE.
Singh P.; Phutela N.; Grover P.; Sinha D.; Sinha S.
4th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2023
Singh P., Symbiosis Centre for Management Studies, NOIDA Symbiosis International (Deemed University), Pune, Noida, India; Phutela N., Symbiosis Centre for Management Studies, NOIDA Symbiosis International (Deemed University), Pune, Noida, India; Grover P., Symbiosis Centre for Management Studies, NOIDA Symbiosis International (Deemed University), Pune, Noida, India; Sinha D., Christ (Deemed to be University), Bangalore, India; Sinha S., Christ (Deemed to be University), Bangalore, India
Machine Learning Based Spam E-Mail Detection Using Logistic Regression Algorithm
E-Mail; Logistic Regression; Machine Learning; Nae Bayes; Spam detection; Support Vector Machines
The rise of spam mail, or junk mail, has emerged as a significant nuisance in the modern digital landscape. This surge not only inundates user's email inboxes but also exposes them to security threats, including malicious content and phishing attempts. To tackle this escalating problem, the proposed machine learning-based strategy that employs Logistic Regression for accurate spam mail prediction. This research is creating an effective and precise spam classification model that effectively discerns between legitimate and spam emails. To achieve this, we harness a meticulously labeled dataset of emails, each classified as either spam or non-spam. This model is to apply preprocessing techniques to extract pertinent features from the email content, encompassing word frequencies, email header data, and other pertinent textual attributes. The choice of Logistic Regression as the foundational classification algorithm is rooted in its simplicity, ease of interpretation, and appropriateness for binary classification tasks. To process train the model using the annotated dataset, refining its hyper parameters to optimize its performance. By incorporating feature engineering and dimensionality reduction methodologies, bolster the model's capacity to generalize effectively to unseen data. Our evaluation methodology encompasses rigorous experiments and comprehensive performance contrasts with other well-regarded machine learning algorithms tailored for spam classification. The assessment criteria encompass accuracy, precision, recall, and the F1 score, offering a holistic appraisal of the model's efficacy. Furthermore, we scrutinize the model's resilience against diverse forms of spam emails, in addition to its capacity to generalize to new data instances. This model is to findings conclusively demonstrated that our Logistic Regression-driven spam mail prediction model achieves a competitive performance standing when juxtaposed with cutting-edge methodologies. The model adeptly identifies and sieves out spam emails, thereby cultivating a more trustworthy and secure email environment for users. The interpretability of the model lends valuable insights into the pivotal features contributing to spam detection, thereby aiding in the identification of emerging spam patterns. 2023 IEEE.
Livia Shreenithi S.A.; Yougandar S.V.; Jayapandian N.
3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023
Livia Shreenithi S.A., Christ (Deemed to Be University), Department of Cse, Bangalore, India; Yougandar S.V., Christ (Deemed to Be University), Department of Cse, Bangalore, India; Jayapandian N., Christ (Deemed to Be University), Department of Cse, Bangalore, India
A Comprehensive Research on Deep Learning Based Routing Optimization Algorithms in Software Defined Networks
Controller attacks; Deep Learning; Deterministic Policy Gradient; Optimal routing; Software defined networking
Discovering an optimal routing in Software Defined Networks (SDNs) is challenging due to several factors like scalability issues, interoperability, reliability, poor configuration of controllers and security measures. The compromised SDN controller attacks at the control plane layer, packet losses in the topology and end-to-end delay are the most security risk factors in SDNs. To overcome this, in most of the existing researches, Deep Reinforcement Learning (DRL) algorithm with various optimization techniques was implemented for optimal routing in SDN by providing link weights to balance the end-to-end delay and packet losses. DRL used Deterministic Policy Gradient (DPG) method which acts as an actor-critic reinforcement learning agent that searches for an optimal policy to minimize the expected cumulative long-term reward. However, discovering an optimal routing with efficient security measures is still a major challenge in SDNs. This research proposes a detailed review of routing optimization algorithms in SDN using Deep Learning (DL) methods which supports the researchers in accomplishing a better solution for future research. 2023 IEEE.
2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023
Kumar G., Dayananda Sagar University, School of Engineering, Department of Computer Science and Engineering, Bengaluru, India; Girisha G.S., Dayananda Sagar University, School of Engineering, Department of Computer Science and Engineering, Bengaluru, India; Shamanth N., School of Engineering and Technology Christ (Deemed to Be University), Department of Computer Science and Engineering, Bengaluru, India
Performance Analysis of Deep Learning Pretrained Image Classifiation Models
Convolutional Neural Networks (CNNs); Deep Learning; Image classification
Convolutional Neural Networks (CNNs) is revolutionized in the field of computer vision, with the high accuracy and capability to learn features from raw data. In this research work focused on a comparative analysis of two popular CNN architectures, VGG16 and VGG19. The CIFAR dataset consists of 60,000 images, each with a resolution of 32x32 and it's belong to one of the 10 classes. Experimental results are compared with VGG16 and VGG19 in terms of their accuracy and training time, and to identify any differences in their ability to learn features from the CIFAR-10 dataset. The results of this research can aid in directing the choice of appropriate architectures for image classification tasks as well as the advantages of optimisation strategies for enhancing the efficiency of deep learning models. In order to enhance the performance of these structures, more optimisation methods and datasets may be investigated in subsequent research. 2023 IEEE.
IEEE 1st International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023
Deepa, Christ University, Department of Computer Science, Bengaluru, India; Alli A., Presidency College, Department of Computer Applications, Bengaluru, India; Sheetal, Presidency College, Department of Computer Science and Engineering, Bengaluru, India; Prasad Km S., Rao Bahadur y Mahabaleswarappa Engineering College, Department of Computer Applications, Karnataka, Ballari, India
Artificial Intelligence Based Enhanced Virtual Mouse Hand Gesture Tracking Using Yolo Algorithm
Artificial Intelligence; Computer Vision; Hand Gestures; Machine Learning; Virtual Mouse; Virtual Reality; YOLO Algorithm
Virtual mouse technology has revolutionized human computer interaction, allowing users to interact with digital environments without physical peripherals. The concept traces back to the late 1970s, and over the years, it has evolved with significant advancements in computer vision, motion tracking, and gesture recognition technologies. In recent times, machine learning techniques, particularly YOLOv8, have been integrated into virtual mouse technology, enabling accurate and swift detection of virtual objects and surfaces. This advancement enhances seamless interaction, intuitive hand gestures, and personalized virtual reality experiences tailored to individual user preferences. The proposed model, EHT (Enhanced Hand Tracking), leverages the power of YOLOv8 to address the limitations of existing models, such as Mediapipe. EHT achieves higher accuracy in hand tracking, real-Time hand gesture recognition, and improved multi-user interactions. It adapts to users' unique gestures over time, delivering a more natural and immersive computing experience with accuracy rates exceeding those of Mediapipe. For instance, across multiple sample datasets, EHT consistently outperformed Mediapipe in hand tracking accuracy. In Sample Dataset 1, EHT demonstrated an accuracy of 98.3% compared to Mediapipe's 95.65%. Similarly, in Sample Dataset 2, EHT achieved an accuracy of 99.35%, surpassing Mediapipe's 94.63%. Even in Sample Dataset 3, EHT maintained its superiority with an accuracy of 98.54 %, whereas Mediapipe achieved 98.26%. The successful implementation of EHT requires a custom dataset and optimization techniques to ensure efficiency on virtual reality hardware. EHT model is anticipated redefining how users interact with digital environments, unlocking new possibilities for intuitive and immersive computing experiences. 2023 IEEE.
Karthick S.; Dinesh M.; Raj C J.D.; Jayapandian N.
2023 IEEE 2nd International Conference on Data, Decision and Systems, ICDDS 2023
Karthick S., Christ University, Department of CSE, Bangalore, India; Dinesh M., Christ University, Department of CSE, Bangalore, India; Raj C J.D., Christ University, Department of CSE, Bangalore, India; Jayapandian N., Department of CSE Christ University, India
Malicious Traffic Classification in WSN using Deep Learning Approaches
data routing; deep learning; LSTM; malicious transmission; neural network; Traffic data; transmission; WSN
Classifying malicious traffic in Wireless Sensor Networks (WSNs) is crucial for maintaining the network's security and dependability. Traditional security techniques are challenging to deploy in WSNs because they comprise tiny, resourceconstrained components with limited processing and energy capabilities. On the other hand, machine learning-based techniques, such as Deep Learning (DL) models like LSTMs, may be used to detect and categorize fraudulent traffic accurately. The classification of malicious traffic in WSNs is crucial because of security. To protect the network's integrity, data, and performance and ensure the system functions properly and securely for its intended use, hostile traffic categorization in WSNs is essential. Classifying malicious communication in a WSN using a Long Short-Term Memory (LSTM) is efficient. WSNs are susceptible to several security risks, such as malicious nodes or traffic that can impair network performance or endanger data integrity. In sequential data processing, LSTM is a Recurrent Neural Network (RNN) appropriate for identifying patterns in network traffic data. 2023 IEEE.
Gupta C.L.P.; Rajassekharan D.; Sharma D.K.; Elangovan M.; Myilsamy V.; Upreti K.
2023 International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023, pp. 426-431.
Gupta C.L.P., Bansal Institute of Engineering and Technology, Dept. of Computer Science and Engineering, Lucknow, India; Rajassekharan D., Peninsula College, Faculty of Computer Science, Peninsula, Malaysia; Sharma D.K., Jaypee University of Engineering and Technology, Department of Mathematics, Madhya Pradesh, Guna, 473226, India; Elangovan M., K. S. Rangasamy College of Technology, Department of Artificial Intelligence and Data Science, Tamil Nadu, Tiruchengode, 637215, India; Myilsamy V., V.S.B College of Engineering Technical Campus, Department of Electrical and Electronics Engineering, Tamil Nadu, Coimbatore, 642109, India; Upreti K., CHRIST(Deemed to Be University), Department of Computer Science, Delhi-NCR, Ghaziabad, India
Deep Learning Based Face Recognized Attendance Management System using Convolutional Neural Network
Convolutional Neural Network; Database Management; Deep Learning; Face Recognition; Generative Adversarial Networks
In today's digital age, manual attendance tracking is plagued by inefficiency and the potential for inaccuracies, often leading to proxy attendance. The main aim of this research work is to manage and monitor the student's attendance by using face recognition technology. This proposed model is mainly categorized four major modules. First module is database creation. Second module is face detection. Then third module is face recognition and final module is automatic attendance updating process. Student images are compiled to create a comprehensive database, ensuring inclusivity across the class roster. The system utilizes the face recognition library, which relies on deep learning based algorithms for face detection and recognition during testing. This face recognition part Convolutional Neural Network algorithm is used. The system matches detected faces with the known database and marks attendance, ensuring a streamlined and accurate attendance tracking process. This innovative approach has the potential to revolutionize attendance management in educational settings, offering a contactless and efficient solution while mitigating proxy attendance concerns. The proposed model is to compare the accuracy level of face recognition. 2023 IEEE.
Joseph A.; Nair A.; Koshy A.B.; Nallaballi M.; Jayapandian N.
Joseph A., (Deemed to Be University), Department of Cse Christ, India; Nair A., (Deemed to Be University), Department of Cse Christ, India; Koshy A.B., (Deemed to Be University), Department of Cse Christ, India; Nallaballi M., School of Engineering and Technology Christ (Deemed to Be University), India; Jayapandian N., School of Engineering and Technology Christ (Deemed to Be University), India
Vehicular Propagation Velocity Forecasting Using Open CV
Computer Vision; Convolutional Neural Network; Machine Learning; Vehicle Speed Detection
This work presents a predictive learning driven methodology for recognizing the vehicular velocity. The developed model uses machine vision models to trace and detect vehicular movement in timely manner. It further deploys a machine tested framework for estimation of its velocity on basis of the accumulated information. The technique depends upon a CNN model that is validated with a standardized instances of vehicular scans and corresponding velocity parameters. The proposed model generates good efficiency and robustness in determining velocities across test conditions which encompass various kinds of vehicles and lighting scenarios. An optimal vehicular frequency is noted with heavy-weight vehicles in place in comparison to other vehicles. A mean latency period of 1.25 seconds and an error rate of 0.05 is observed with less road traffic in place. The suggested approach can be of great help in transportation systems, traffic monitoring and enhancing road safety. 2023 IEEE.
Das U.; Sharma V.; Das M.; Mishra S.; Iwendi C.; Osamor J.
2023 4th International Conference on Computation, Automation and Knowledge Management, ICCAKM 2023
Das U., Kalinga Institute of Industrial Technology, Bhubaneswar, India; Sharma V., Christ (Deemed to Be University), Department of Computational Sciences, Delhi NCR, India; Das M., Kalinga Institute of Industrial Technology, Bhubaneswar, India; Mishra S., Kalinga Institute of Industrial Technology, Bhubaneswar, India; Iwendi C., School of Creative Technologies, University of Bolton, United Kingdom; Osamor J., Glasgow Caledonian University, Department of Cybersecurity and Networks, Glasgow, G4 0BA, United Kingdom
Machine Learning Based Recession Prediction Analysis Using Gross Domestic Product (GDP)
Covid-19; Economical Statistics; Gross Domestic Product; Machine Learning; Random Forest; Recession
This research article aims to explore the prediction and analysis of recessions, with a particular focus on Gross Domestic Product (GDP). The study examines the impact of recessions on different countries, namely India, USA, Germany, China, and Bangladesh, while also considering the influence of the COVID-19 pandemic on these nations in relation to the recessionary effects. Furthermore, the study lists many machine learning techniques that could be used to anticipate recessions. This research mainly focuses on predicting recession using different machine learning models. The research not only provides an in-depth analysis of the recessionary impacts on different economies but also serves as a foundation for future implementation of these algorithms for accurate recession prediction and proactive economic decision-making. This research study mainly focuses on machine learning algorithms like Random Forest, Support Vector Machines and Regression Model. The GDP prediction comparison is taking last twenty years data. This is mainly compared before and after COVID-19 situation. 2023 IEEE.
7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023 - Proceedings, pp. 492-498.
Haritha T.H., CHRIST (Deemed to Be University), Department of CSE, India; Joby A., CHRIST (Deemed to Be University), Department of CSE, India; Javapandian N., CHRIST (Deemed to Be University), Department of CSE, India
Identification of Cyberbullying and Finding Target User's Intention on Public Forums
bag of words; cyberbullying; feature extraction; sentiment analysis; SVM
Numerous cybercriminals are active in the online realm, carrying out cyber-crimes according to predetermined and preplanned agendas. Cyberbullying, which was formerly limited to physical limits, has now expanded online as a result of technology advancements. One type of cyberbullying is denigration or insult. The cyberbullying cases are in exponential rise in social media as per the reports of Computer Emergency Team by Sri Lanka. Insulting words are changeable in dynamic and the same terminology may have numerous meanings depending on the context. Bullying cannot be defined just because a statement comprises such a term. As a result, when classifying comments, standard keyword detecting approaches are insufficient. Other languages also may have dealt with this issue by utilizing lexical databases like WordNet, which might give synonyms as well as homonyms for words. Because no adequate lexical database mainly for the English language has been built, recognizing a word like bullying is difficult. As a result, employed rules to solve the problem. Facebook comments containing profanity were gathered, outliers were eliminated, and the remaining messages were pre-processed. Five feature extraction rules were employed to assess insult in the text. Following that, used the Support Vector Machine (SVM) technique. Using an F1-score of 85%, the findings demonstrate that when compared to existing works, SVM performs better. The focus on English language cyberbully identification, which has never been addressed earlier, distinguishes this study. 2023 IEEE.
Krishnasamy L.; Kiruthika M.; Manjunatha Swamy C.; Vigneshwaran T.; Sharon Roji Priya C.; Vijay Anand D.
Proceedings of International Conference on Contemporary Computing and Informatics, IC3I 2023, pp. 2152-2156.
Krishnasamy L., School of Engineering and Technology, Department of CSE, Christ University, Bengaluru, India; Kiruthika M., School of Computing, Department of Computational Intelligence, SRM Institute of Science and Technology, Tamilnadu, Kattankulathur, India; Manjunatha Swamy C., School of Engineering and Technology, Department of CSE, Christ University, Bengaluru, India; Vigneshwaran T., School of Engineering and Technology, Department of CSE, Christ University, Bengaluru, India; Sharon Roji Priya C., School of Engineering and Technology, Department of CSE, Christ University, Bengaluru, India; Vijay Anand D., Kongu Engineering College, Department of Information Technology, Erode, India
Predictive Analytics for Stock Market Trends using Machine Learning
Data Engineering; Data Preprocessing; Decision Trees; Deep Learning; Feature Selection; Financial Data; Linear Regression; Machine Learning; Machine Learning Algorithms; Random Forests; Recurrent Neural Networks (RNNs); Stock Market Trends
Navigating the intricacies of stock market trends demands a novel approach capable of deciphering the web of financial data and market sentiment. This research embarks on a transformative journey into the realm of machine learning, where we harness the power of data to forecast stock market trends with increased precision and accuracy. Commencing with an exploration of stock market dynamics and the inherent limitations of traditional forecasting techniques, this paper takes a bold step into the future by embracing the potential of machine learning. The study begins with an in-depth analysis of data preprocessing, unraveling the complexity of feature selection and engineering, setting the stage for a data-driven odyssey. As our exploration progresses, we dive into the deployment of diverse machine learning algorithms, including linear regression, decision trees, random forests, and the formidable deep learning models such as recurrent neural networks (RNNs) and long short-term memory networks (LSTMs). These algorithms act as our guiding lights, revealing intricate patterns concealed within historical stock price data. Our journey reaches new heights as we recognize the significance of augmenting predictive models with external data sources. Incorporating elements like news sentiment analysis and macroeconomic indicators enriches our understanding of the market landscape, enhancing the predictive capabilities of our models. We also delve into the crucial aspects of model evaluation, guarding against overfitting, and selecting appropriate performance metrics to ensure robust and reliable predictions. The research reaches its zenith with a meticulous analysis of real-world case studies, providing a comparative perspective between machine learning models and traditional forecasting methods. The results underscore the remarkable potential of machine learning in predicting stock market trends more accurately. 2023 IEEE.
Manasa N.; Praveenraj D.W.; Lakshmi S.R.
https://doi.org/10.1109/ICCAKM58659.2023.10449528 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187556499&doi=10.1109%2fICCAKM58659.2023.10449528&partnerID=40&md5=171d658a35aeb60e8ca851d7aaae7a84
Manasa N., School of Business and Management, Christ (Deemed to Be University), Bangalore, India; Praveenraj D.W., School of Business and Management, Christ (Deemed to Be University), Bangalore, India; Lakshmi S.R., School of Business and Management, Christ (Deemed to Be University), Bangalore, India
Impact of Expert Academic Teaching Quality and its Performance Based on BiLSTM-Deep CNN Network
Bidirectional Long Short-Term Memory (LSTM); Convolutional Neural Network (CNN); Information Gain (IG)
Undergraduate and postgraduate students from eight different departments at a UK institution participated in organized conversations about the impact of teachers' research activities on their education. In both samples, positive responses greatly outnumbered negative ones. There was an increase in positive feedback on professors' research when the overall quantity and quality of research in a specific field (as measured by Research Assessment Exercise [RAE] ratings) improved. Undergraduate samples with higher RAE scores were more likely to have negative feedback on research than graduate student samples. Both graduate and undergraduate students agreed that lecturers' research increased the instructor's credibility, relevance, and knowledge, as well as piqued and maintained their own interest, engagement, and drive. Data processing, feature selection, and model training are the first steps in the proposed approach. The data are changed from their raw form into a form suitable for academic use during the data pre-processing phase. They are employing Information Gain and Symmetric Uncertainty for feature selection. Following the feature selection process, the models are trained using BiLSTM-CNN. Both the BiLSTM and the CNN methods are inferior to the proposed method. 2023 IEEE.
Shukla T.D.; Acharjee P.B.; Chethan C.; Thulasimani T.; Sindhu M.; Sharma S.
7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023 - Proceedings, pp. 895-900.
https://doi.org/10.1109/ICECA58529.2023.10395690 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186121150&doi=10.1109%2fICECA58529.2023.10395690&partnerID=40&md5=1dd07530ce8c5e424dd1b87fc22ed83e
Shukla T.D., University Institute of Teachers Training & Research, Chandigarh University, Mohali, India; Acharjee P.B., CHRIST University, Pune, India; Chethan C., Sri Venkateshwara College of Engineering, Department of Information Science and Engineering, Bengaluru, India; Thulasimani T., Bannari Amman Institute of Technology, Department of Mathematics, Tamilnadu, Sathyamangalam, India; Sindhu M., Prince Shri Venkateshwara Padmavathy Engineering College, Department of Computer Science and Engineering, Chennai, India; Sharma S., Madhav Institute of Technology and Science, Department of IT, Madhya Pradesh, Gwalior, India
A Comprehensive Review on Image Restoration Methods due to Salt and Pepper Noise
Convolutional Neural Network; Digital image; linear and non-linear methods; noise; Peak Signal to Noise Ratio; visualized information
Digital images are well-use in various fields like satellite communication, mobile communication, medical and security. Visualized information helps the people to understand the things easily by seen. Improper capturing, age of camera lens, imperfect storage and transmission leads to introduce noise in the image. Gaussian noise, salt and pepper/impulse noise and speckle noise may affect the original image due to aforementioned reasons. Out of these, impulse noise/salt and pepper noise is one of the major types, degrades the image with black and white spots it results loss of required information. Hence, restoration of ground- truth image from such type of noisy image is a challenging task to provide quality and clarity visuals to users. Several linear and non-linear methods have been proposed by researchers since more than four decades. Nonlinear methods based on; median filtering approach; adaptive median filter approach; median filter with switching condition; and median filter with rank order type; are proposed from early 1980s onwards. All of these operated directly on pixels in spatial domain. Hence, they are very easy to implement and most of them are not that much robust at middle and higher noise density circumstances. Further, various researchers have been implemented linear methods such as wavelet transform methods like SWT and DWT. Majority of these are works well upto 50% noise density conditions and very few works well on higher and multiple noise density conditions also. To overcome these problems CNNs based methods have been developed tremendously by various researchers from last decade and these methods require huge database to train the network model. Most of these, achieved good accuracy rates at higher and multiple noise conditions. Hence, here a detailed review report is presented on impulse noise removal methods with their Peak Signal to Noise Ratio (PSNR). 2023 IEEE.
Kumar G.P.; Arputharaj V.J.; Kumar P.R.; Kumar D.V.; Satyanarayana B.V.V.; Budumuru P.R.
2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023 - Proceedings, pp. 562-567.
Kumar G.P., Department of ECE, Vishnu Institute of Technology, Bhimavaram, India; Arputharaj V.J., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India; Kumar P.R., Department of ECE, Sri Vishnu Engineering College for Women, Bhimavaram, India; Kumar D.V., Freshman Engineering, Department Lakireddy, Balireddy College of Engineering, Mylavaram, India; Satyanarayana B.V.V., Department of ECE, Vishnu Institute of Technology, Bhimavaram, India; Budumuru P.R., Department of ECE, Vishnu Institute of Technology, Bhimavaram, India
Impact of Machine Intelligence on Clinical Disease Outbreak Prediction
algorithmic bias; artificial intelligence; disease outbreak prediction; public health
This research paper examines the utilization of Artificial Intelligence (AI) in disease outbreak prediction and its importance in public health. It explores the hurdles associated with predicting disease outbreaks, including data quality and accessibility, ethical considerations, algorithmic bias, and integration and interpretability challenges. The paper presents an overview of AI techniques applied in healthcare and their relevance to forecasting disease outbreaks. Case studies demonstrate the efficacy of AI -based models in predicting infectious diseases, vector-borne diseases, and epidemics/pandemics, employing diverse data sources. The limitations and future prospects of AI in disease outbreak prediction are addressed, accompanied by recommendations for enhancement. In conclusion, the paper highlights AI's potential to revolutionize disease outbreak prediction, leading to proactive public health interventions and improved response strategies. 2023 IEEE.
Singhal M.; Mishra S.; Sharma V.; Anand K.; Alkhayyat A.
2023 10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering, UPCON 2023, pp. 1020-1025.
https://doi.org/10.1109/UPCON59197.2023.10434492 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187352329&doi=10.1109%2fUPCON59197.2023.10434492&partnerID=40&md5=6f6d4d2fb4eedfe0a8f8ecdd463e04b6
Singhal M., Kalinga Institute of Industrial Technology, Deemed to Be University, India; Mishra S., Kalinga Institute of Industrial Technology, Deemed to Be University, India; Sharma V., Christ (Deemed to Be University), Computer Science Department, Delhi NCR, India; Anand K., Kalinga Institute of Industrial Technology, Deemed to Be University, India; Alkhayyat A., College of Technical Engineering, The Islamic University, Najaf, Iraq
A Voting Enabled Predictive Approach for Hate Speech Detection
CountVectorizer; Hate speech detection; logistic regression; machine learning; Naive Bayes classifier; Random Forest classifier
In today's digital environment, hate speech, which is defined as disparaging and discriminating communication based on personal characteristics, presents a big difficulty. Hate crimes and the rising amount of such content on social media platforms are two examples of how it is having an impact. Large volumes of textual data require manual analysis and categorization, which is tedious and subject to prejudice. Machine learning (ML) technologies have the ability to automate hate speech identification with increased objectivity and accuracy in order to overcome these constraints. This article intends to give a comparative analysis of various ML models for the identification of hate speech. The proliferation of such content online and its negative repercussions on people and society are explored, as is the necessity for automated hate speech recognition. This paper intends to support the creation of efficient hate speech detection systems by performing a comparative analysis of ML models. Random forest records the best performance with higher accuracy and low response delay period for hate speech detection. The results will help enhance automated text classification algorithms and, in the end, promote a safer and more welcoming online environment by illuminating the benefits and drawbacks of various approaches. 2023 IEEE.
Panda P.; Mishra S.; Sharma V.; Alkhayyat A.
Proceedings of International Conference on Contemporary Computing and Informatics, IC3I 2023, pp. 450-454.
Panda P., 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; Alkhayyat A., College of Technical Engineering, The Islamic University, Najaf, Iraq
AI Sovereignty in Autonomous Driving: Exploring Needs and Possibilities for Overcoming Challenges
Autonomous car; Explainable artificial intelligence; Intelligent transport system
With the development of artificial intelligence, advancements in navigation systems for self-driving cars have become a new direction over the last decade. The inclusion of AI-driven actuators in autonomous vehicles has broken the barriers in terms of real-time high-quality data processing resources, accuracy of decisive actions and generalization of environment-action pairs. Upgradation from a car with no automation to a car with minimal to no human intervention has become a boon of AI, as it resolves most of the transportation problems on roads, including human error, lack of visibility in adverse weather conditions, tiredness of drivers in long journeys, etc. This study focuses on AI-enabled tasks, including object detection and identification, lane detection, notification for lane departure and reinforcement learning from the operational environment. However, there exist serious issues in deploying AI-empowered modules in autonomous cars, as the consumer rights to explain, trustworthiness, and reliability of the machine have not yet met the requirements. Our work explores the needs and prospects of AI sovereignty in autonomous driving by overcoming the aforementioned issues so that the healthy progress of technological society can take care of the future world. 2023 IEEE.
Sinha K.; Misra B.; Chakraborty S.; Dey N.
Conference Proceedings - 2023 IEEE Silchar Subsection Conference, SILCON 2023
Sinha K., Christ (Deemed to Be University), Department of Statistics and Data Science, Bangalore, India; Misra B., Techno International New Town, Department of Computer Science Engineering, Kolkata, India; Chakraborty S., Techno International New Town, Department of Computer Science Engineering, Kolkata, India; Dey N., Techno International New Town, Department of Computer Science Engineering, Kolkata, India
Prediction of Rainfall Using Seasonal Auto Regressive Integrated Moving Average and Transductive Long Short-Term Model
Accuracy rate; Rainfall prediction; Seasonal Auto Regressive Integrated Moving Average; Time Series Analysis; Transductive Long Short-Term Model
One of the most crucial parts of the practical application in recent years has been the analysis of time series data for forecasting. Because of the extreme climate variations, it is now harder than ever to estimate rainfall accurately. It is possible to forecast rainfall using a number of time series models that uncover hidden patterns in past meteorological data. Choosing the right Time Series Analysis Models for predicting is a challenging task. This study suggests using a Seasonal Auto Regressive Integrated Moving Average (SARIMA) to forecast values that are similar to historical values that exhibit seasonal patterns. Twelve years of historical weather data for the city of Lahore (from 2005 to 2017) and Blora Regency are taken into account for the prediction. The dataset underwent pre-processing operations like cleaning and normalisation before to the classification procedure. For classification, Transductive Long Short-Term Model (TLSTM) is employed which has learned the dependency values where the memory blocks are recurring and capable of learning long-term dependencies on this model. Further, TLSTM's goal is to increase accuracy close to the test point, where test points are selected as a validation group. The performance of the models has been assessed based on accuracy (99%), precision (98%), recall (96%) and fl-score (98%). Proposed SARIMA model showed optimistic results when compared to existing models. 2023 IEEE.
Phadke R.; Mohamad Ramadan G.; Archana Reddy R.; Kumar Pani A.; Al-Jawahry H.M.
https://doi.org/10.1109/AIKIIE60097.2023.10390346 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187257517&doi=10.1109%2fAIKIIE60097.2023.10390346&partnerID=40&md5=eefe494d87dd726dbda70f9945426acb
Phadke R., Nitte Meenakshi Institute of Technology, Department of Electronics and Communication Engineering, Bangalore, India; Mohamad Ramadan G., College of Mlt Ahl Al Bayt University, Karbala, Iraq; Archana Reddy R., School of Computer Science and Artificial Intelligence Sr University, Warangal, India; Kumar Pani A., Christ (Deemed to Be University), Department of Computer Science and Engineering, Bangalore, India; Al-Jawahry H.M., The Islamic University, Najaf, Iraq