Detecting Cyberbullying in Twitter: A Multi-Model Approach
With cyberbullying surging across social media, this study investigates the effectiveness of four prominent deep learning models - CNN, Bi-LSTM, GRU, and LSTM - in identifying cyberbullying within Twitter texts. Driven by the urgent need for robust tools, this research aims to enrich the field of cyberbullying detection by thoroughly evaluating these models' capabilities. A dataset of Twitter texts served as the training ground, rigorously preprocessed to ensure optimal model compatibility. Each model, CNN, Bi-LSTM, GRU, and LSTM, underwent independent training and evaluation, revealing distinct performance levels: CNN achieved the highest accuracy at 83.10%, followed by Bi-LSTM (81.90%), GRU (81.73%), and LSTM (16.07%). These differences highlight the unique strengths of each architecture in analysing and representing text data. The findings highlight the CNN model's superior performance, indicating its potential as a highly effective tool for Twitter-based cyberbullying detection. While the deep learning models explored here offer promising avenues for detecting cyberbullying on Twitter, their performance highlights the complexities inherent in this task. The limited space of tweets can often obscure the true intent behind words, making accurate identification a nuanced challenge. Despite this, the CNN model's robust performance suggests that carefully chosen architectures hold significant potential for combating online harassment. This research paves the way for further explorations in harnessing the power of AI to create a safer and more civil online experience where respectful communication can flourish even within the constraints of concision. 2024 IEEE.
Ashish Joseph V.A.; Prathap B.R.; Kumar K.P.
IEEE International Conference on Data Engineering and Communication Systems, ICDECS 2024
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.1109/ICDECS59733.2023.10502699 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192513626&doi=10.1109%2fICDECS59733.2023.10502699&partnerID=40&md5=2d1a2eb8b641699c7aaa793ff4eaf4a6
Ashish Joseph V.A., Christ (Deemed to Be University), Department of Computer Science and Engineering, Bengaluru, India; Prathap B.R., Christ (Deemed to Be University), Department of Computer Science and Engineering, Bengaluru, India; Kumar K.P., Christ (Deemed to Be University), Department of Computer Science and Engineering, Bengaluru, India
Detecting Deepfake Voices Using a Novel Method for Authenticity Verification in Voice-Based Communication
Audio Analysis; Deepfake Voice; Machine Learning; Signal Processing; Social Media; Voice Authentication System; Voice Synthesis
The widespread use of deepfake technology in recent years has given rise to grave worries about the alteration of audio-visual material. The integrity of voice-based communication is particularly vulnerable to the threat posed by deepfake voice synthesis. The development of cutting-edge methods for the identification of deepfake voices is examined in this paper, which also offers a thorough analysis of current approaches, their advantages, and disadvantages. The research presents a novel method for detecting deepfakes in voice recordings that uses signal processing, machine learning, and audio analysis to separate synthetic voices from authentic voices. By achieving superior accuracy in differentiating between real and deepfake voices, and proposed method supplies a strong barrier against the misuse of voice synthesis technology for malicious purposes, also go over the research some of the possible uses for this technology, like voice authentication system security and social media platform content moderation. The paper's insights will support continued efforts to strengthen the authenticity of voice communication in the digital age and reduce the risks associated with deepfake voice synthesis. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Lecture Notes in Networks and Systems, Vol-1088 LNNS, pp. 397-405.
Springer Science and Business Media Deutschland GmbH
Kansara A., Computer Science and Engineering (Specialization in Data Science), Christ University, Bangalore, India; Kumari P., Computer Science and Engineering (Specialization in Data Science), Christ University, Bangalore, India; Prathap B.R., Computer Science and Engineering (Specialization in Data Science), Christ University, Bangalore, India
Detecting Fake Information Dissemination using Leveraging Machine Learning and DRIMUX with B-LSTM
Accuracy; B-LSTM; DRIMUX; Fake Information Dissemination Detection; Machine Learning
Information integrity and public confidence are seriously threatened by the rapid expansion of fake news and misinformation that has resulted from the online broadcast of information. This work focuses on the detection of fraudulent information propagation utilizing machine learning techniques and the Digital Reputation and Influence Measurement Unit (DRIMUX) in order to address this problem. The use of Bidirectional Long Short-Term Memory (B-LSTM) networks into the detection process is something we really advocate. B-LSTM enables the capture of contextual dependencies from both past and future time steps, enhancing the understanding of sequential data. Additionally, DRIMUX provides reputation and influence measurements to assess the credibility of information sources. Experimental analyses on various datasets reveal the promising performance of the suggested methodology, highlighting its potential in preventing the spread of false information and protecting the veracity of digital information. 2024, Ismail Saritas. All rights reserved.
Kaneti V.R.; Venkatesan M.; Sharma H.; Bodhankar A.; Bajaj R.; Ganesh Kumar R.; Pokhariya H.S.; Deepak A.
International Journal of Intelligent Systems and Applications in Engineering, Vol-12, No. 5s, pp. 91-100.
Kaneti V.R., Department of Computer Science and Engineering, VNR Vignana Jyothi Institute of Engineering and Technology, Telangana, Hyderabad, India; Venkatesan M., Dr. D.Y. Patil Institute of Technology, Pune-18, Pimpri, India; Sharma H., Computer Engineering and Applications, GLA University, Mathura, India; Bodhankar A., Department of Decision Sciences, Dr. Ambedkar Institute of Management Studies and Research, Maharashtra, India; Bajaj R., NB Global University, Rajasthan, Bikaner, India; Ganesh Kumar R., Department of Computer Science and Engineering, CHRIST (Deemed to be University), School of Engineering and Technology, Kanminike, Bangalore, India; Pokhariya H.S., Department of Computer Science & Engineering, Graphic Era Deemed to be University, Uttarakhand, Dehradun, India; Deepak A., Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Tamilnadu, Chennai, India
Detecting Infectious Disease Based on Social Media Data Using BERT Model
BERT; Influenza; Seasonal Infectious Disease; Sentiment Analysis
Seasonal diseases are those diseases that are widespread during a particular time of the year including monsoons, winter etc. In the absence of preventative measures, the human race remains vulnerable to the hazardous effects of seasonal diseases following regular patterns of increased inci- dence and transmission which remains a global concern. Dengue, Influenza, etc. are such types of diseases where every year many people get affected globally. The primary focus of this research paper is to understand the opinion of people regarding the seasonal diseases. The research paper covers sentiment analysis on textual data from social media where people have vocalized their sentiments or thinking regarding seasonal diseases and seasonal infectious diseases. Influenza, Dengue, Malaria, Japanese Encephalitis, and Chikungunya are the seasonal diseases that have been covered in this research paper. To achieve this, the language model Bidirectional Encoder Representations from Transformers (BERT) was used to verify the sentiments about the seasonal diseases. The result of the investigation hold the potential to significantly enhance our comprehension of societal sentiments, discerning between states of tranquility and concern among individuals. The outcome of the study will help healthcare department to plan the necessary actions. 2024 IEEE.
Proceedings of InC4 2024 - 2024 IEEE International Conference on Contemporary Computing and Communications
Barman R., CHRIST University, Department of Computer Science, Bangalore, India; Rajamohan K., CHRIST University, Department of Computer Science, Bangalore, India
Detecting Student Depression Using Non-Clinical Measures with Explainable Predictive Modeling
Depression detection; Explainable AI; Machine Learning; SHAP; SMOTE; Student Mental Health
Depression among students is a serious global mental health concern, affecting academic performance, emotional wellbeing, and long-term development. While traditional diagnostic tools like self-reported questionnaires and clinical interviews are useful, they often suffer from subjectivity, recall bias, and limited scalability. This study introduces a data driven, interpretable machine learning approach to predict student depression using both academic and nonacademic factors, without relying on clinical indicators. The dataset comprises student information on demographics, academic workload, lifestyle habits, social interactions, financial stress, and emotional state. Following thorough preprocessing including handling missing values, encoding variables, correlation-based feature reduction, and SMOTE to address class imbalance, ten supervised machine learning models were trained and assessed. Among them, a SMOTE enhanced XGBoost model achieved the highest test ROC AUC score of 0.95. To maintain transparency, SHAP (Shapley Additive Explanations) was employed to interpret the model's predictions, highlighting key risk factors such as academic pressure, poor sleep quality, financial difficulties, and low social support. These findings can help guide early interventions and build trust with stakeholders. Future work may involve incorporating longitudinal and multimodal data, deploying real time solutions in educational settings, and addressing ethical considerations around privacy, fairness, and consent in AI based mental health systems. 2025 IEEE.
Kotnala, Akshat; Arora, Nidhi; Srivastava, Shilpa
2025 International Conference on Emerging Technologies and Innovation for Sustainability, EmergIN 2025;pp.648-652
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1109/EmergIN67762.2025.11450836 https://www.scopus.com/pages/publications/105036394639?origin=resultslist
Kotnala A., Amity University, Department of Statistics, Noida, India; Arora N., Kalindi College, University of Delhi, Department of Computer Science, New Delhi, India; Srivastava S., Christ University, School of Science, Bengaluru, India
Detecting the magnitude of depression in Twitter users using sentiment analysis
Depression; Emotional integrity; Sentiment analysis; Twitter data; Weighted average scoring
Today the different social networking sites have enabled everyone to easily express and share their feelings with people around the world. A lot of people use text for communicating, which can be done through different social media messaging platforms available today such as Twitter, Facebook etc, as they find it easier to express their feelings through text instead of speaking them out. Many people who also suffer from stress find it easier to express their feelings on online platform, as over there they can express themselves very easily. So if they are alerted beforehand, there are ways to overcome the mental problems and stress they are suffering from. Depression stands out to be one of the most well known mental health disorders and a major issue for medical and mental health practitioners. Legitimate checking can help in its discovery, which could be useful to anticipate and prevent depression all-together.Hence there is a need for a system, which can cater to such issues and help the user. The purpose of this paper is to propose an efficient method that can detect the level of depression in Twitter users. Sentiment scores calculated can be combined with different emotions to provide a better method to calculate depression scores. This process will help underscore various aspects of depression that have not been understood previously. The main aim is to provide a sense of understanding regarding depression levels in different users and how the scores can be correlated to the main data. 2019 Institute of Advanced Engineering and Science. All rights reserved.
International Journal of Electrical and Computer Engineering, Vol-9, No. 4, pp. 3247-3255.
Institute of Advanced Engineering and Science
All Open Access; Gold Open Access; Green Open Access
https://doi.org/10.11591/ijece.v9i4.pp3247-3255 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067230531&doi=10.11591%2fijece.v9i4.pp3247-3255&partnerID=40&md5=546cb2e7738d44d3dd92312d312041ec
Stephen J.J., Department of Computer Science, Christ (Deemed to Be University), Hosur Main Road, Bangalore, 560029, India; Prabu P., Department of Computer Science, Christ (Deemed to Be University), Hosur Main Road, Bangalore, 560029, India
Detection and analysis of android malwares using hybrid dual Path bi-LSTM Kepler dynamic graph convolutional network
Android application; Benign; Dynamic graph; Malicious; Selected features; Syntax tree
In past decade, the android malware threats have been rapidly increasing with the widespread usage of internet applications. In respect of security purpose, there are several machine learning techniques attempted to detect the malwares effectively, but failed to achieve the accurate detection due to increasing number of features, more time consumption decreases in detection efficiency. To overcome these limitations, in this research work an innovative Hybrid dual path Bidirectional long short-term memory Kepler dynamic graph Convolutional Network (HBKCN) is proposed to analyze and detect android malwares effectively. First, the augmented abstract syntax tree is applied for pre-processing and extracts the string function from each malware. Second, the adaptive aphid ant optimization is utilized to choose the most appropriate features and remove irrelevant features. Finally, the proposed HBKCN classifies benign and malware apps based on their specifications. Four benchmark datasets, namely Drebin, VirusShare, Malgenome -215, and MaMaDroid datasets, are employed to estimate the effectiveness of the technique. The result demonstrates that the HBKCN technique achieved excellent performance with respect to a few important metrics compared to existing methods. Moreover, detection accuracies of 99.2%, 99.1%,99.8% and 99.8% are achieved for the considered datasets, respectively. Also, the computation time is greatly reduced, illustrating the efficiency of the proposed model in identifying android malwares. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.
Lingayya S.; Kulkarni P.; Salins R.D.; Uppoor S.; Gurudas V.R.
International Journal of Machine Learning and Cybernetics
Lingayya S., Department of Computer Science and Engineering, Shri Madhwa Vadiraja Institute of Technology and Management, Karnataka, Bantakal, 574115, India; Kulkarni P., Department of Computer Science and Engineering, School of Engineering, Dayananda Sagar University, Ramanagara Dt., Bengaluru,, 562112, India; Salins R.D., Department of Aerospace, Honeywell Technology Solutions Lab Pvt. Ltd, Bellandur, Bangalore, 560103, India; Uppoor S., Infosys Ltd, Karnataka, Mangalore, 574153, India; Gurudas V.R., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST University, Karnataka, Bangalore, India
Lingayya, Sadananda; Kulkarni, Praveen; Salins, Rohan Don; Uppoor, Shruthi; Gurudas, V.R.
International Journal of Machine Learning and Cybernetics;Volume;16;Issue;2;Article No.;117833;pp.835-853
https://doi.org/10.1007/s13042-024-02303-3 https://www.scopus.com/pages/publications/85201560496?origin=resultslist
Detection and Behavioral Analysis of Preschoolers with Dyscalculia
Analysis; Behaviour; Behaviour analysis; Brain training; Dyscalculia
Human behaviours are influenced by various factors that might impact their thought process. The way human beings response in situations have a strong connection with genetic makeup, cultural values and experiences from the past. Behaviour Analysis discusses the effect of human response to external/internal stimuli. This study helps in understanding behaviour changes among individuals suffering from various psychological disorders. Dyscalculia is one similar type of learning disorder [LD] which is commonly found among individuals and goes undetected for years. It is a lifelong condition which causes difficulty for people to perform mathematics-related tasks. Dyscalculia is quite eminent at every age. Since the symptoms are prominent from a young age, it can be detected at the earliest. Dyscalculia has no medical treatment but can be minimized by getting involved in some brain exercises especially created for children with Learning Disabilities. The chapter deals with minor research and the behaviour analysis for the above-mentioned disorder among pre-schoolers. In this chapter, a study of the behavioural patterns of pre-schoolers with dyscalculia is performed. This chapter also attempts to propose a model that can detect and predict the possibility of a child suffering from dyscalculia. It also includes a number of brain training activities that can help them to improve and enhance their confidence in mathematics. 2020, Springer Nature Switzerland AG.
Suri S.; Tirkey A.; Delphy J.; Aurelia S.
Learning and Analytics in Intelligent Systems, Vol-6, pp. 45-67.
Suri S., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India; Tirkey A., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India; Delphy J., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India; Aurelia S., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India
Detection and Classification of Colorectal Polyp Using Deep Learning
Colorectal Cancer (CRC) is the third most dangerous cancer in the world and also increasing day by day. So, timely and accurate diagnosis is required to save the life of patients. Cancer grows from polyps which can be either cancerous or noncancerous. So, if the cancerous polyps are detected accurately and removed on time, then the dangerous consequences of cancer can be reduced to a large extent. The colonoscopy is used to detect the presence of colorectal polyps. However, manual examinations performed by experts are prone to various errors. Therefore, some researchers have utilized machine and deep learning-based models to automate the diagnosis process. However, existing models suffer from overfitting and gradient vanishing problems. To overcome these problems, a convolutional neural network- (CNN-) based deep learning model is proposed. Initially, guided image filter and dynamic histogram equalization approaches are used to filter and enhance the colonoscopy images. Thereafter, Single Shot MultiBox Detector (SSD) is used to efficiently detect and classify colorectal polyps from colonoscopy images. Finally, fully connected layers with dropouts are used to classify the polyp classes. Extensive experimental results on benchmark dataset show that the proposed model achieves significantly better results than the competitive models. The proposed model can detect and classify colorectal polyps from the colonoscopy images with 92% accuracy. 2022 Sushama Tanwar et al.
Tanwar S.; Vijayalakshmi S.; Sabharwal M.; Kaur M.; Alzubi A.A.; Lee H.-N.
All Open Access; Green Open Access; Hybrid Gold Open Access
https://doi.org/10.1155/2022/2805607 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128802566&doi=10.1155%2f2022%2f2805607&partnerID=40&md5=1a782f1a4b7dafdef8ec8b29a227fab1
Tanwar S., Galgotias University, Uttar Pradesh, 201307, India; Vijayalakshmi S., Christ University, Lavasa, 412112, India; Sabharwal M., Galgotias University, Uttar Pradesh, 201307, India; Kaur M., Gwangju Institute of Science and Technology, School of Electrical Engineering and Computer Science, Gwangju, 61005, South Korea; Alzubi A.A., Computer Science Department, Community College, King Saud University, Riyadh, Saudi Arabia; Lee H.-N., Gwangju Institute of Science and Technology, School of Electrical Engineering and Computer Science, Gwangju, 61005, South Korea
Detection and classification of lung cancer using deep neural network
DCNN; deep convolution neural network; EfficientNetB7; histopathology; lung cancer
Lung cancers hold a critical spot among the reasons for most cancer deaths among humans. The better way to maximise the survival rate is the detection of cancer at the earliest. But existing traditional techniques are time-consuming and error-prone. This study is a significant and efficient method for the detection and classification of lung cancer into large cell carcinomas, small cell, adenocarcinoma, squamous cell carcinomas, or benign respectively. In the proposed technique, a novel algorithm is implemented to generate the Image patches from whole slide histopathological images. Then, histogram normalisation is carried out to remove noise and enhance the image. Then a novel extended Mobius transformation technique is used for image augmentation. Finally, Dense EfficientNetB7 is trained to extract the features for the detection and classification of lung cancer. The performance of the proposed technique is proved more efficient and par with histologists attaining an accuracy of 98.87%. Copyright 2025 Inderscience Enterprises Ltd.
International Journal of Medical Engineering and Informatics;Volume;17;Issue;5;pp.429-443
https://doi.org/10.1504/IJMEI.2025.148638 https://www.scopus.com/pages/publications/105017018666?origin=resultslist
Kumar S.B., Department of AI, ML & DS, CHRIST University, Karnataka, Bangalore, India; Kumar M.V., Department of Information Science and Engineering, RV Institute of Technology and Management, Karnataka, Bangalore, India, Visvesvaraya Technological University, Karnataka, Belagavi, 590018, India
Detection and Classification of Potholes in Indian Roads Using Wavelet Based Energy Modules
Canny operator; Markov random; Median filter Neural Network. Wavelet Energy Field
Maintenance of roads is one the major challenge in the developed countries. The well maintained roads always indicates the economy of the whole country. The heavy use of roads, environmental conditions and maintenance is not performed regularly that leads the formation of potholes which causes the accidents and unwanted traffics. The paper discuss about the detection of potholes based on wavelet energy field. The proposed method mainly includes three phases (A)Wavelet energy filed is constructed in order to detect the image by using geometric criteria and morphological processing (B)Extracting Region of intersect by edge based segmentation technique (C)Classifying the potholes using Neural Network. 2019 IEEE.
2019 International Conference on Data Science and Communication, IconDSC 2019
https://doi.org/10.1109/IconDSC.2019.8816878 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072762234&doi=10.1109%2fIconDSC.2019.8816878&partnerID=40&md5=eb76696d1087c3cac5a03dee75846f90
Manjunatha H.T., Department of Computer Applications, Jawaharlal Nehru National College of Engineering, Shimoga, Karnataka, India; Danti A., Department of Computer Science and Engineering, Christ (Deemed to be University), Bangalore, India
Detection and Classification of Thoracic Diseases in Medical Images using Artificial Intelligence Techniques: A Systematic Review
Artificial Intelligence is at the leading edge of innovation and is developing very fast. In recent studies, it has played a progressive and vital role in Computer-Aided Diagnosis. The chest is one of the large body parts of human anatomy and contains several vital organs inside the thoracic cavity. Furthermore, chest radiographs are the most commonly ordered and globally used by physicians for diagnosis. An automated, fast, and reliable detection of diseases based on chest radiography can be a critical step in radiology workflow. This study presents the conduction and results of a systematic review investigating Artificial Intelligence Techniques to identify Thoracic Diseases in Medical Images. The systematic review was performed according to PRISMA guidelines. The research articles published in English were filtered based on defined inclusion and exclusion criteria. The Electrochemical Society
ECS Transactions, Vol-107, No. 1, pp. 307-340.
https://doi.org/10.1149/10701.0307ecst https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130551359&doi=10.1149%2f10701.0307ecst&partnerID=40&md5=d3293b8cf91c49e4b89e122ae11c2c5a
Prakash S., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, 560029, India; Ramamurthy B., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, 560029, India
Detection and identification of un-uniformed shape text from blurred video frames
Deep neural networks; Optical character recognition; Region convolution neural networks; Region of interest; Text detection; Text recognition
The identification and recognition of text from video frames have received a lot of attention recently, that makes many computer vision-based applications conceivable. In this study, we modify the picture mask and the original identification of the mask region convolution neural network and permit detection in three levels, including holistic, sequence, and at the level of pixels. To identify the texts and determine the text forms, semantics at the pixel and holistic levels can be used. With masking and detection, existences of the character and the word are separated and recognised. In addition, text detection using the results of 2-D feature space instance segmentation is done. Moreover, we explore text recognition using an attention-based optical character recognition (OCR) method with mask region convolution neural networks (R-CNN) to address and detect the problem of smaller and blurrier texts at the sequential level. Using attribute maps of the word occurrences in sequence to seq, the OCR method calculates the character sequence. At last, a fine-grained learning strategy is proposed to constructs models at word level using the annotated datasets, resulting in the training of a more precise and reliable model. The well-known benchmark datasets ICDAR 2013 and ICDAR 2015 are used to test our suggested methodology. 2024, Institute of Advanced Engineering and Science. All rights reserved.
Channegowda R.H.; Srinivasaiah R.; Jankatti S.K.; Meenakshi; Jinachandra N.S.; Hombegowda R.K.T.
IAES International Journal of Artificial Intelligence, Vol-13, No. 4, pp. 4795-4805.
https://doi.org/10.11591/ijai.v13.i4.pp4795-4805 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207517136&doi=10.11591%2fijai.v13.i4.pp4795-4805&partnerID=40&md5=50ccf68f239750e4cc9f4a5a30da46c8
Channegowda R.H., Department of Electronics and Communication Engineering, Dayananda Sagar Academy of Technology and Management, Bengaluru, India; Srinivasaiah R., Department of Computer Science and Engineering, CHRIST Deemed to be University, Bengaluru, India; Jankatti S.K., Department of Computer Science and Technology, Dayananda Sagar University, Bengaluru, India; Meenakshi, Department of Computer Science and Engineering, RNS Institute of Technology, Bengaluru, India; Jinachandra N.S., Department of Mechanical Engineering, CHRIST Deemed to be University, Bengaluru, India; Hombegowda R.K.T., Department of Electronics and Communication Engineering, Ghousia College of Engineering, Ramanagara, India
Detection and localization for watermarking technique using LSB encryption for DICOM Image
62H35; A duopoly economy; Market share; Mathematical modeling; Medical image analysis Security; Ordinary differential equations; Watermarking
Watermarking is an effective way of transferring hidden data from one place to another, or proving ownership of digital content. The hidden data can be text, audio, images GIF etc., the data is embedded in a cover object usually an image or a video sequence. Usually the watermarking system(s) rely on their hidden aspect, as their primary security measure, once this is established that the cover object is counting some hidden data, then it is generally possible to recover the hidden information. The author proposed an in-genuine technique for DICOM color image water marking by combining Multi Quadrant LSB with truly random mixed key cryptography. This system provides a high level of security by just the water marking technique, as it breaks the cover image into up to four quadrants, & does LSB replacement of two bytes each quadrant. The bit sequence as the quadrant sequence can be randomized to increase the randomness, use of truly random mixed key cryptography, by using a pre shared, variable length, truly random, private key, turns hidden data into noise, which can only be recovered by having the private key. Thus, the proposed technique truly diminishes the probability of recovering hidden data, even if it is detected that something is hidden in cover object. 2022 Taru Publications.
Gupta M.K.; Dadheech P.; Kumar A.; Dogiwal S.R.; Poonia R.C.; Raja L.; Bhatt D.P.
Journal of Discrete Mathematical Sciences and Cryptography, Vol-25, No. 1, pp. 193-204.
Gupta M.K., Department of Computer Science Engineering, Swami Keshvanand Institute of Technology Management Gramothan, Rajasthan, Jaipur, 302017, India; Dadheech P., Department of Computer Science Engineering, Swami Keshvanand Institute of Technology Management Gramothan, Rajasthan, Jaipur, 302017, India; Kumar A., Department of Computer Science Engineering, Swami Keshvanand Institute of Technology Management Gramothan, Rajasthan, Jaipur, 302017, India; Dogiwal S.R., Department of Information Technology, Swami Keshvanand Institute of Technology Management Gramothan, Rajasthan, Jaipur, 302017, India; Poonia R.C., Department of Computer Science, CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India; Raja L., Department of Computer Application, Manipal University Jaipur, Rajasthan, Jaipur, 303007, India; Bhatt D.P., Department of Computer Application, Manipal University Jaipur, Rajasthan, Jaipur, 303007, India
Detection and management of sucralose pollution: innovations in sustainable monitoring, mitigation, and energy utilization
catalyst; environmental nanotechnology; Environmental pollution; mitigation; sucralose
Sucralose, a widely utilized artificial sweetener, has emerged as a potential environmental contaminant, persisting in aquatic environments and causing ecological risks. The use of suitable methods and sustainable materials can be applied for the detection of sucralose in wastewater, sludge, and other waste matrices Using sustainable materials and novel techniques for discerning sucralose. Leveraging waste-derived components, particularly in electrochemical sensing systems with competent nanomaterials can be a promising avenue for sucralose detection. Moreover, integrating waste-derived carbon materials like activated carbon or carbon black as electrodes enhances sensor sensitivity by augmenting organic molecule adsorption capacities, thus amplifying detection capabilities. Furthermore, waste-derived substrates such as cellulose fibers or agricultural residues can be engineered to serve as substrates or immobilization matrices in enzyme-based sensors for sucralose detection. While the electrochemical systems current output may not directly serve energy applications, its potential utilization for energy generation or storage, through technologies like electrochemical capacitors or batteries, underscores the broader sustainability objectives by repurposing energy from the detection process for additional beneficial applications. The amalgamation of sustainable methodologies in sucralose pollution detection and management not only contributes to environmental preservation but also aligns with the broader trajectory toward sustainable resource utilization and conservation. 2026 Elsevier Inc. All rights reserved.
Waste Derived Catalysts: Sustainable Materials for Energy and Environment;pp.345-358
https://doi.org/10.1016/B978-0-443-32958-6.00024-8 https://www.scopus.com/pages/publications/105032956736?origin=resultslist
Manuel M., Department of Chemistry, Christ University, Karnataka, Bengaluru, India; Kanchi S., Department of Chemistry, Christ University, Karnataka, Bengaluru, India
Detection and Robust Classification of Lung Cancer Disease Using Hybrid Deep Learning Approach
computer-aided detection; hybrid deep learning; Lung cancer; memory efficient
Effective lung cancer diagnosis and treatment hinge on the early detection of lung nodules. Various techniques, such as thresholding, pattern recognition, computer-aided diagnostics, and backpropagation calculations, have been explored by scientists. Convolutional neural networks (CNNs) have emerged as powerful tools in recent times, revolutionizing many aspects of this field. However, traditional computer-aided detection systems face challenges when categorizing lung nodule detection. Excessive reliance on classifiers at every stage of the process results in diminished recognition rates and an increased occurrence of false positives. To address these issues, we present a novel approach based on deep hybrid learning for classifying lung lesions. In this study, we explore multiple memory-efficient and hybrid deep neural network (DNN) architectures for image processing. Our proposed hybrid DNN significantly outperforms the current state-of-the-art, achieving an impressive accuracy of 95.21%, all while maintaining a balanced trade-off between specificity and sensitivity. The primary focus of this research is to differentiate between CT scans of patients who have early-stage lung cancer and those who do not. This is achieved by utilizing binary classification networks, including standard CNN, SqueezeNet, and MobileNet. 2023 IEEE.
Prasanna K.R.; Kumar R.V.; Mohanbabu G.; Selvam J.J.D.; Kumarnath J.; Sellapandi S.P.
2023 International Conference on Data Science, Agents and Artificial Intelligence, ICDSAAI 2023
https://doi.org/10.1109/ICDSAAI59313.2023.10452545 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187793442&doi=10.1109%2fICDSAAI59313.2023.10452545&partnerID=40&md5=cfcd4da5f399314751eda2033b4028b0
Prasanna K.R., Bharath Institute of Higher Education and Research, Chennai, India; Kumar R.V., Sri Lakshmi Narayana Institute of Medical Science, Department of Physiology, Biher, India; Mohanbabu G., Chennai Institute of Technology, Department of Ece, Chennai, India; Selvam J.J.D., Christ(Deemed to Be University), School of Business and Management, Banglore, India; Kumarnath J., Psna College of Engineering and Technology, Dindigul, India; Sellapandi S.P., Psna College of Engineering and Technology, Dindigul, India
Detection and Sensing of Human Body Micro-Motions Using 24GHz mm-Waves: A Case Study
24 GHz mm-Wave; beam-forming; Doppler effect; human motion detection; IoT; micro-motion sensing; MIMO; signal attenuation; smart environments
The paper presents a 24 GHz millimeter-wave (mm-Wave) system for real-time human presence and distance detection, leveraging the 24 GHz band's balance of range, resolution, and power efficiency for applications in smart environments. The system uses Doppler-based reflections from signals to accurately detect presence and estimate distances with 12cm accuracy up to 5 meters with little needed infrastructure. The challenges presented by signal attenuation and multipath interference are addressed using beamforming and Massive MIMO with 5G-enabled IoT as the framework. The use of Ultra-Reliable Low Latency Communication and Massive-Machine-Type Communication allows a framework for rapid data processing and scalability. This system enables automated smart home lighting, healthcare occupancy detection, and security intrusion alert applications, with very high detection accuracies. The limits are reduced detection performance beyond a distance of 6 meters and interference from reflective surfaces. Future work includes investigating the 60 GHz bands, which will yield higher resolution in the detection, and using machine learning techniques to develop adaptive detection, as a scalable and cost effective method of real-time automation in a range of settings. 2025 IEEE.
Joy, Salna; Rajesh, Siddharth; Neethu, P.S.
2025 12th International Conference on Reliability, Infocom Technologies and Optimization ,Trends and Future Directions, ICRITO 2025;
https://doi.org/10.1109/ICRITO66076.2025.11241290 https://www.scopus.com/pages/publications/105029820572?origin=resultslist
Joy S., New Horizon College of Engineering, Department of Computer Science and Engineering, Bengaluru, India; Rajesh S., New Horizon College of Engineering, Department of Electronics and Communication Engineering, Bengaluru, India; Neethu P.S., School of Engineering and Technology, Christ University, Dept of Aiml and Data Science, Bengaluru, India
Detection andDealing ofMulticollinearity Using aNew RidgeEstimator
Generalised ridge regression; Multicollinearity; Ridge estimator; Ridge regression
The Ordinary Least Square (OLS) method is a widely used technique for estimating regression coefficients that describe the relationship between the independent variables and the dependent variable. The regression estimates obtained using this approach give a poor result in the presence of multicollinearity. Ridge regression is employed to address this problem. A ridge estimator, also called a biasing constant, plays a notable role in the parameter estimation of ridge regression. In this paper, we introduced a new ridge estimator and studied its properties. A comparative study of the proposed estimator with some of the existing ridge estimators under extreme multicollinearity has been illustrated by using a simulation technique. The estimator developed in this paper seems to perform better than all the other estimators because of the smaller ratio of average mean square error. The performance of the proposed estimator is analysed and verified using real-life data. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
Lecture Notes in Networks and Systems;Volume;1501 LNNS;pp.365-378
https://doi.org/10.1007/978-981-96-8350-5_26 https://www.scopus.com/pages/publications/105028352404?origin=resultslist
Augustine P., Department of Data Science and Statistics, CHRIST (Deemed to Be University), Bengaluru, India; John N., Department of Data Science and Statistics, CHRIST (Deemed to Be University), Bengaluru, India
Detection of a new sample of Galactic white dwarfs in the direction of the Small Magellanic Cloud
Binaries: general; Hertzsprung; Russell and C M diagrams; Techniques: photometric; Ultraviolet: stars; White dwarfs
Aims. In this study, we demonstrate the efficacy of the Ultraviolet Imaging Telescope (UVIT) in identifying and characterizing white dwarfs (WDs) within the Milky Way Galaxy. Methods. Leveraging the UVIT point-source catalogue towards the Small Magellanic Cloud and cross-matching it with Gaia DR3 data, we identified 43 single WDs (37 new detections), 13 new WD+main-sequence candidates, and 161 UV bright main-sequence stars by analysing their spectral energy distributions. Using the WD evolutionary models, we determined the masses, effective temperatures, and cooling ages of these identified WDs. Results. The masses of these WDs range from 0.2 to 1.3 M? and the effective temperatures (Teff) lie between 10 000 K to 15 000 K, with cooling ages spanning 0.1-2 Gyr. Notably, we detect WDs that are hotter than reported in the literature, which we attribute to the sensitivity of UVIT. Furthermore, we report the detection of 20 new extremely low-mass candidates from our analysis. Future spectroscopic studies of the extremely low-mass candidates will help us understand the formation scenarios of these exotic objects. Despite limitations in Gaia DR3 distance measurements for optically faint WDs, we provide a crude estimate of the WD space density within 1kpc of 1.3 10-3 pc-3, which is higher than previous estimates in the literature. Conclusions. Our results underscore the instrumental capabilities of UVIT and anticipate forthcoming UV missions such as INSIST for systematic WD discovery. Our method sets a precedent for future analyses in other UVIT fields to find more WDs and perform spectroscopic studies to verify their candidacy. The Authors 2024.
Sidharth A.V.; Shridharan B.; Mathew B.; Devaraj A.; Cysil T.B.; Stalin C.S.; Arun R.; Bhattacharyya S.; Kartha S.S.; Robin T.
Sidharth A.V., Department of Physics and Electronics, CHRIST (Deemed to be University), Bangalore, 560029, India; Shridharan B., Department of Physics and Electronics, CHRIST (Deemed to be University), Bangalore, 560029, India, Tata Institute of Fundamental Research, Homi Bhabha Road, Mumbai, 400005, India; Mathew B., Department of Physics and Electronics, CHRIST (Deemed to be University), Bangalore, 560029, India; Devaraj A., Department of Physics and Electronics, CHRIST (Deemed to be University), Bangalore, 560029, India; Cysil T.B., Department of Physics and Electronics, CHRIST (Deemed to be University), Bangalore, 560029, India; Stalin C.S., Indian Institute of Astrophysics, Sarjapur Road, Koramangala, Bangalore, 560034, India; Arun R., Indian Institute of Astrophysics, Sarjapur Road, Koramangala, Bangalore, 560034, India; Bhattacharyya S., Department of Physics and Electronics, CHRIST (Deemed to be University), Bangalore, 560029, India; Kartha S.S., Department of Physics and Electronics, CHRIST (Deemed to be University), Bangalore, 560029, India; Robin T., Department of Physics and Electronics, CHRIST (Deemed to be University), Bangalore, 560029, India