Segment Anything Model (SAM) to Segment lymphocyte from Blood Smear Images
Deep learning; Lymphocyte; Segment Anything Model; Segmentation; white blood cells
Automated lymphocyte segmentation from smear images plays an important role in disease diagnosis and monitoring, aiding in the assessment of immune system function and pathology detection. This study proposes an approach for lymphocyte segmentation utilizing Segment Anything Model (SAM) which is a deep learning model. Our method leverages a pre trained SAM architecture and fine-tunes it on a custom dataset comprising smear images containing lymphocytes. The pretrained model's ability of versatile segmentation combined with fine-tuning on the specific dataset enhances its performance in accurately identifying lymphocyte boundaries. We evaluate the proposed approach on a diverse set of smear images, demonstrating its effectiveness in segmenting lymphocytes with impressive IOU score and Dice Score. SAM deep learning model, fine-tuned on custom datasets, holds promise for robust and efficient lymphocyte segmentation from blood smear images. 2024 IEEE.
2024 IEEE 3rd World Conference on Applied Intelligence and Computing, AIC 2024, pp. 1080-1084.
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.1109/AIC61668.2024.10730867 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210268951&doi=10.1109%2fAIC61668.2024.10730867&partnerID=40&md5=36cc6adea1bbf1eee90d6f1b7b2f1179
Patil A.P., Christ (Deemed to Be University), Department of Computer Science, India, Cmr Institute of Technology, Department of Computer Application, Bengaluru, India; Hiremath M., Christ (Deemed to Be University), Department of Computer Science, Bengaluru, India
Mental Health Stigma: Strategies for Destigmatization in Healthcare Settings
Destigmatization; ENACT program; IBH therapies; Mental Health Advocacy; RESHAPE program
Mental illness is one of the most common disabilities in the world. The term "mental illness stigma"describes harmful practices and misconceptions that lead to a detrimental effect on the mental health, motivation, and self-worth of those who suffer from mental illnesses. Health care services are important for treating and reducing the negative stigma of mental health, as they are areas where patients seek relief and support. The study aims to investigate the causes and how to reduce them. Explores ways to disrupt the health care environment, specifically the RESHAPE program, which focuses on the concept of "critical". This review paper looks at 8-10 papers on mental health and stigma and how stigma will be reduced. The results show that a large number of doctors and students are stigmatized, negatively affecting the lives of people affected by mental illness. RESHAPE, KAP, and IBH therapies are also effective ways to minimize mental health stigma. This intervention aims to educate public health workers, promote social cohesion, and integrate treatment into primary health care, improving treatment into primary health care, improving treatment quality and patient outcomes. The study draws attention to the importance of stigma reduction efforts in the long term in health education and practice emphasis. 2024 IEEE.
Verma A.; Panda K.T.; Sharma V.; Tripathy H.K.; Al-Khasawneh M.A.; Alkhayyat A.
2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024
Verma A., Kalinga Institute of Industrial Technology, India; Panda K.T., Kalinga Institute of Industrial Technology, India; Sharma V., CHRIST University, Computer Science Department, India; Tripathy H.K., Kalinga Institute of Industrial Technology, India; Al-Khasawneh M.A., Skyline University College, University City Sharjah, School of Computing, Sharjah, 1797, United Arab Emirates, Applied Science Private University, Applied Science Research Center, Amman, Jordan, Jadara University Research Center, Jadara University, Jordan; Alkhayyat A., The Islamic University, College of Technical Engineering, Najaf, Iraq
A Comparative Study of ML and DL Approaches for Twitter Sentiment Classification
comparative analysis; deep learning models; machine learning models; sentiment classification; text preprocessing; word embedding
This research made use of various machine learning (ML) and deep learning (DL) methods - such as support vector machines, random forests, logistic regression, naive Bayes, and XGBoost, convolutional neural networks (CNNs), and feedforward neural networks (FNNs) - for tweet analysis to investigate public sentiment towards Ola and Uber. The objective is to determine the most effective method for distinguishing between good and negative tweets. Feature engineering techniques improve the algorithms interpretation of tweet content. To balance out the disparity between positive and negative tweets. The project aims to uncover customer wants and concerns on Twitter to help Ola and Uber, in addition to improving Algorithms Accuracy. The study intends to help these ride-hailing businesses make educated modifications to boost customer happiness by closely examining tweets. Essentially, the study assesses how well various ML and DL algorithms comprehend user feedback on Uber and Ola. The overarching goal is to not only enhance computational methods but also contribute to the improvement of these ride-hailing services, ultimately fostering a more positive online environment for Ola and Uber enthusiasts. In summary, the study investigates sentiment analysis techniques on Twitter to optimize understanding of Ola and Uber-related tweets, aiming to facilitate positive changes for the ride-hailing services and their customers, promoting a friendlier Twitter community. 2024 IEEE.
Aamir M., Christ (Deemed to be University), School of Science, Lavasa, India; Lekha J., Christ (Deemed to be University), School of Science, Lavasa, India; Sweety, Christ (Deemed to be University), School of Science, Lavasa, India
Parkinsons Disease Progression Prediction using Advanced Machine Learning Techniques
CatBoost regressor; extraTrees regressor; machine learning; parkinsons' disease; proteomic data; unified parkinson's disease rating scale (UPDRS)
Parkinson's disease (PD) is a neurodegenerative condition that affects people over time and significantly lowers their quality of life. Patients with PD experience both motor and non-motor symptoms. Through clinical evaluation, the Unified Parkinson's Disease Rating Scale (UPDRS) is used to quantify the severity of Parkinson's disease. No definitive diagnostic tests for PD currently exist. Emerging machine learning techniques show potential to forecast future UPDRS scores for making informed medical decisions and enable better disease management. This paper studies research leveraging proteomic data to forecast PD prognosis, focusing on advanced machine learning techniques like CatBoost Regressor, ElasticNet, XGBoost Regressor, RandomForest Regressor, ExtraTrees Regressor and DecisionTree Regressor. 2024 IEEE.
https://doi.org/10.1109/ICEECT61758.2024.10739044 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211187364&doi=10.1109%2fICEECT61758.2024.10739044&partnerID=40&md5=770985e5c402d6feaf3e55beb81e911b
Bhattacharya T., Christ (Deemed To Be) University, Department of Data Science, Pune, India; Thomas K.T., Christ (Deemed To Be) University, Department of Data Science, Pune, India; Mathew L., Christ (Deemed To Be) University, Department of Data Science, Pune, India
Spoken Language Identification using Deep Learning
deep neural network; Language identification; major indian spoken languages; mel-frequency cepstral coefficients; spoken language
A crucial problem in natural language processing is language identification, which has applications in speech recognition, translation services, and multilingual content. The five main Indian languages that are the subject of this study are Hindi, Bengali, Tamil, English, and Gujarati. A Deep Neural Network is introduced in the paper which is specifically made to use Mel-Frequency Cepstral Coefficients (MFCCs) for sophisticated language categorization. The suggested architecture of the model, which includes batch normalisation and tightly linked layers, helps it to be adept at identifying complex linguistic patterns. Comparing the research to the source work [18], promising improvements are shown, highlighting the potential of the model in language detection. 2024 IEEE.
Julius C.A.; Vijayalakshmi S.; Palathara T.S.
Julius C.A., Christ University, Deparment of Data Science, Lavasa, Pune, India; Vijayalakshmi S., Christ University, Deparment of Data Science, Lavasa, Pune, India; Palathara T.S., Christ University, Deparment of Data Science, Lavasa, Pune, India
Importance Of Artificial Intelligence in Improving Human Resource Management For Companies To Find Suitable Candidature
AI; human resource management (HRM); jobs; recruitment
The efficient use of pertinent human resources both inside and outside the company via management structures guided by economic and humanistic principles is known as human resource management. It is a catch-all word for a set of actions that guarantee the accomplishment of group objectives and the optimization of member growth. Employers need the correct recruitment tools to fill available positions since traditional recruiting approaches are not up to par in the global talent battle. First, as the digital tool redesigns business, we look at how talent acquisition has evolved from digital 1.0 to 3.0 (AI-enabled). Artificial intelligence technology has made recruiting more efficient and made recruiters' daily tasks easier. Additionally, the analysis in the paper shows that artificial intelligence (AI) is crucial to every step of the hiring process, including promotion, application, screening, evaluation, and coordination. This study demonstrates how organizations are realizing the value of talent management in gaining a competitive edge as the need for higher-level talent grows. Even though some HR managers are using AI for talent acquisition, our research shows that there is still an opportunity for development. 2024 IEEE.
Goyal V.; Tiwari A.; Kamble D.; Rani D.S.; Patil V.C.; Jha A.
Proceedings - 2024 International Conference on Healthcare Innovations, Software and Engineering Technologies, HISET 2024, pp. 281-285.
Goyal V., Christ (Deemed to Be University), Ghaziabad, India; Tiwari A., Christ (Deemed to Be University), Ghaziabad, India; Kamble D., Jaywant Institute of Management, Karad, India; Rani D.S., Institute of Aeronautical Engineering, Department of Electrical and Electronics Engineering, Hyderabad, India; Patil V.C., Krishna Vishwa Vidyapeeth (Deemed to Be University), Department of Medicine, Karad, India; Jha A., Krishna Vishwa Vidyapeeth (Deemed to Be University), Department of Radio-Diagnosis, Karad, India
Virtual Reality in Tourism Industry within the Framework of Virtual Reality Markup Language
Augmented Reality; React VR; Virtual Reality; VR Applications; VR Tourism
Virtual Reality (VR) technology has grown and emerged in the tourism industry. It offering immersive and interactive experiences, VR has transformed how people discover and interact with the VRML and people interact with different destinations. This article explores the use of VR in tourism, and focusing on Virtual Reality Markup Language (VRML) and its role in showcasing the evolution of head-mounted displays (HMDs) and the various applications of VR. It emphasizes how VR can improve travel experiences, aid in destination planning, preserve cultural heritage, support adventure tourism, and revolutionize destination marketing. The article also gives the challenges and limitations faced by VR in tourism, as well as future trends and opportunities in the field. The article impact of VR on the tourism industry and discusses the combination of Augmented Reality (AR) and VR to create virtual art exhibitions in physical and online spaces. Additionally, it provides insights into the future of VR, AR, and Mixed Reality (MR), the use of VRML, and the development of 3D modeling for creating virtual environments that help users achieve learning objectives. 2024 IEEE.
Mallikarjuna B.; Sebastian S.; Chandra R.; Dadheech P.; Sharma M.
2024 IEEE 3rd World Conference on Applied Intelligence and Computing, AIC 2024, pp. 274-279.
Mallikarjuna B., Institute of Aeronautical Engineering, Department of Information Technology, Dundigal, 500043, India; Sebastian S., Christ University, School of Business and Management, 201003, India; Chandra R., Christ University, Poonia Department of Computer Science, 201003, India; Dadheech P., Management & Gramothan (SKIT), Swami Keshvanand Institute of Technology, Rajasthan, Jaipur, 3020, India; Sharma M., Amity University, Amity School of Architecture and Planning (ASAP), Rajasthan, Jaipur, 303002, India
An Efficient Compressive Data Collection Scheme for Wireless Sensor Networks
Compressive data collection; covariance function; Gaussian regression; margin-free estimator; maximum likelihood estimator; wireless sensor network
The Compressive Data Collection (CDC) scheme is an efficient data-acquiring method that uses compressive sensing to decrease the bulk of data transmitted. Most existing schemes are modeled as Non-Uniform Sparse Random Projection (NSRP), and an NSRP-based estimator is used. These models cannot deal with anomaly readings that deviate from their standards and norms. Therefore, we provide a new CDC strategy in this study that uses an opportunistic estimator and routing. Initially, neighbor nodes are identified using the covariance function following the Gaussian process regression, and the data transfer to the neighbor node is done using the compressive sensing technique. Compressed data are then projected by using conventional random projection. Finally, the sample required to retrieve data is estimated using margin-free and maximum likelihood estimators. Results show that the sample needed to retrieve the data is less in the proposed scheme. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Mary Anita E.A.; Jenefa J.; Vinodha D.; Lapina M.
Lecture Notes in Networks and Systems, Vol-1207 LNNS, pp. 31-47.
Springer Science and Business Media Deutschland GmbH
Mary Anita E.A., CHRIST (Deemed to be University), Bangalore, India; Jenefa J., CHRIST (Deemed to be University), Bangalore, India; Vinodha D., CHRIST (Deemed to be University), Bangalore, India; Lapina M., North Caucasus Federal University, Stavropol, Russian Federation
Effectiveness of Telemedicine in Disaster Relief Response Management
crisis management; dsaster; telemedicine; virtual healthcare
Due to climate change many parts of the worlds are prone to natural disasters. Thus, disaster management is the need of the hour. Effectiveness of Telemedicine in Disaster Relief response management shows the demand for telemedicine in the current time to tackle disasters. This paper investigates the history and evolution of telemedicine, their types, demand, challenges and its prospects. The proposed model, CrisisResponsive E-Health Recovery, places an approach on a concise way to manage disaster in the least time without giving up accuracy. The suggested model has the best response time as compared to the other existing model. Wide implementation of this model will result in better recovery rates in disasters. 2024 IEEE.
Verma P.; Alkhayyat A.; Mishra S.; Sharma V.
Verma P., KIIT Deemed to be University, School of Computer Engineering, Odisha, Bhubaneshwar, India; Alkhayyat A., Islamic University, College of Technical Engineering, Najaf, Iraq; Mishra S., Deemed to be University, Kalinga Institute of Industrial Technology, India; Sharma V., CHRIST University, Computer Science Department, India
Economic Growth, Automation and Environmental Degradation: An Empirical Evidence from Asian Countries
ardl(autoregressive distributed lag) model; automation; causality test; cross-sectional dependency test; ecological footprint; gdp per capita(gross domestic product per capita); gross capital formation
In the era of Industry 4.0 the increase in population as a result of environmental erosion is the prime concern in the global scenario, Asia as the biggest continent is very much applied to it. In this context assessment of the interrelation relationship between automation, financial development, environmental degradation, and per capita growth of 12 Asian Countries from 1995 to 2022 using the panel ARDL model, in addition to assessing the cause-effect relationship panel causality test also incorporated. As a part of ARDL PMG estimation results demonstrated that capital formation, import automation machinery, urban population growth, and ecological footprint positively impact per capita in the long term. But in this phenomenon, aggregate industrial value added negatively impacts per capita, because of automation labor displacement. Results from the causality test suggest that economic upswing, and urban population growth two-way causal relationship. However, capital formation, value-added, and ecological footprint positively impacted per capita growth. Regarding policy formulation need to formulate the necessary skill development program so that individuals can cope with the new decade of automation, in addition, ecological footprint as an indicator of environmental degradation positively impacts per capita growth, so the government needs to make a strategy at the societal level toward sustainable ecofriendly behavior. 2024 IEEE.
Ghosh S., Christ Deemed to be University, Department of Data Science, Lavasa, Pune, India; Gaine A.H., Christ Deemed to be University, Department of Data Science, Lavasa, Pune, India; Savithri M., Christ Deemed to be University, Department of Data Science, Lavasa, Pune, India
Machine Learning Algorithms for Stroke Risk Prediction Leveraging on Explainable Artificial Intelligence Techniques (XAI)
Explain Like I'm Five (ELI5); Explainable Artificial Intelligence (XAI); Local Interpretable Model Agnostic Explanation (LIME); Machine Learning
Stroke poses a significant global health challenge, contributing to widespread mortality and disability. Identifying predictors of stroke risk is crucial for enabling timely interventions, thereby reducing the increasing impact of strokes. This research addresses this imperative by employing Explainable Artificial Intelligence (XAI) techniques to pinpoint stroke risk predictors. To bridge existing gaps, six machine learning models were assessed using key performance metrics. Utilising the Synthetic Minority Over-sampling Technique (SMOTE) to minimize the impact of the imbalanced nature of the dataset used in this research, the Random Forest algorithm emerged as the most effective among the algorithms with an accuracy of 94.5%, AUC-ROC of 0.95, recall of 0.96, precision of 0.93, and an F1 score of 0.95. This study explored the interpretation of these algorithms and results using Local Interpretable Model-agnostic Explanations (LIME) and Explain Like I'm Five (ELI5). With the interpretations, healthcare providers can gain insight into patients' stroke risk predictions. Key stroke risk factors highlighted by the study include Age, Marital Status, Glucose Level, Body Mass Index, Work Type, Heart Disease, and Gender. This research significantly contributes to healthcare and healthcare informatics by providing insights that can enhance strategies for stroke prevention and management, ultimately leading to improved patient care. The identified predictors offer valuable information for healthcare professionals to develop targeted interventions, fostering a proactive approach to mitigating the impact of strokes on individuals and the healthcare system. 2024 IEEE.
Ugbomeh O.; Yiye V.; Ibeke E.; Ezenkwu C.P.; Sharma V.; Alkhayyat A.
https://doi.org/10.1109/ICEECT61758.2024.10739320 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211172203&doi=10.1109%2fICEECT61758.2024.10739320&partnerID=40&md5=2e43a5fb9306b35e3f01fa55e18d0efa
Ugbomeh O., Robert Gordon University, School of Creative and Cultural Business, Aberdeen, United Kingdom; Yiye V., Robert Gordon University, School of Creative and Cultural Business, Aberdeen, United Kingdom; Ibeke E., Robert Gordon University, School of Computing, Engrg.,and Tech, Aberdeen, United Kingdom; Ezenkwu C.P., Robert Gordon University, School of Computing, Engrg.,and Tech, Aberdeen, United Kingdom; Sharma V., Christ University, Computer Science Department, India; Alkhayyat A., Islamic University, College of Technical Engineering, Najaf, Iraq
Combatting Phishing Threats: An NLP-Based Programming Approach for Detection of Malicious Emails and Texts
AI-based expert systems and deep learning (ML); Cybersecurity; Decision trees; Email security; Feature extraction; Logistic regression; NLP-based programming (Natural Language Processing); Phishing; Text classification
Attackers are employing more advanced strategies to trick people into divulging private information or carrying out harmful deeds, and phishing is still a serious cybersecurity risk. We provide a new method in this study that combines algorithms based on AI-based expert systems and deep learning (ML) with the use of NLP-based programming (NLP) approaches to identify fraudulent emails and messages. Using a variety of datasets that include samples of both authentic and phishing messages, our approach preprocesses textual data, extracts relevant characteristics, and trains AI-based expert systems and deep learning models. Metrics including accuracy, precision, recall, and F1-score are used to assess the effectiveness of different AI-based expert systems and deep learning methods, such as logistic regression, random forests, decision trees, and neural networks, among others. To collect semantic information and increase detection accuracy, we also investigate the integration of sophisticated NLP-based techniques, such as word embeddings. The efficacy of our suggested strategy in reducing this common cybersecurity issue is highlighted by our results, which show promising performance in correctly recognizing phishing attempts. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Upreti K.; Patil H.P.; Bhende V.S.; Hundekari S.; Divakaran P.; Vats P.
Lecture Notes in Networks and Systems, Vol-1111 LNNS, pp. 83-94.
Upreti K., CHRIST(Deemed to Be University), Delhi NCR Campus, India; Patil H.P., Symbiosis Skills And Professional University, KiwalePune, India; Bhende V.S., AISSMS IOIT Pune, Pune, India; Hundekari S., School of Engineering and Technology, Pimpri Chinchwad University, Pune, India; Divakaran P., Himalayan University, Arunachal Pradesh, Itanagar, India; Vats P., Manipal University Jaipur, Rajasthan, Jaipur, India
An Analysis of Financial and Technological Factors Influencing AgriTech Acceptance in Bengaluru Division, Karnataka
Agricultural innovation; Agricultural production; Agricultural technology; AgriTech; India agriculture; Karnataka
In 2023, India surpassed China to become the world's most populated nation. This demographic surge has precipitated an escalating exigency for sustenance as populace burgeons unabatedly. To satiate this burgeoning demand there arises an imperative to augment yield of agriculture commensurately. It is pertinent to acknowledge that as per Global Hunger Index of 2019, India occupies disconcerting rank of 102 amongst consortium of 117 nations when gauged by severity of hunger quantified through Hunger Severity Scale with disquieting score of 30.3. Aspiration of attaining utopian objective of zero hunger by 2030 as promulgated by Sustainable Development Goals appears to be quixotic endeavor seemingly beyond realm of plausibility. In this milieu agricultural technology (AgriTech) enterprises within India present veritable opportunity to invigorate agricultural sector. Agrarian landscape of India has been undergoing profound metamorphosis owing to technological renaissance that has permeated nation facilitated by innovative solutions proffered by nascent corporate entities. State of Karnataka stands as an epicenter of sorts for AgriTech enterprises within India. In this study we meticulously scrutinize impact wielded by financial factors on adoption of AgriTech solutions by agrarian stakeholders and elucidate technological determinants that actuate embracement of AgriTech within this demographic. The study uses descriptive statistics and chi-square analyses to rigorously assess predefined objectives. Geographic ambit of this inquiry encompasses regions of Chikkaballapura and Doddaballapura Taluks situated within Bengaluru division of Karnataka in 2022. The empirical revelations distinctly illuminate that individuals vested with access to technological and financial resources exemplified by parameters such as annual household income, accessibility to commercial banking services, cooperative financial institutions, mobile telephony, internet connectivity and Global Positioning System (GPS) technology exhibit palpable predilection for integration of AgriTech solutions into their agrarian practices. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Lecture Notes in Networks and Systems, Vol-1168 LNNS, pp. 207-216.
Kumari S., Department of Economics, Christ (Deemed to Be University), Bangalore, India; Vineeth M., Department of Economics, Christ (Deemed to Be University), Bangalore, India; Girish G.P., Department of Finance, ICFAI Business School, Off-Campus Center, The ICFAI Foundation for Higher Education (a Deemed to-be-University under Sec 3 of UGC Act 1956), Hyderabad, India
Sentiment Analysis for Online Shopping Reviews Using Machine Learning
Accuracy; Amazon reviews; BART; BERT; GPT; Machine learning; Online shopping; Sentiment analysis
Everyday shoppers need reliable and insightful reviews of e-commerce websites to enhance their shopping experience. This research study explores sentiment analysis on Amazon reviews. It utilizes them as a diverse repository of customer opinions by unlocking their embedded sentiments, thereby recognizing their pivotal role in guiding potential buyers. Sentiment misinterpretations may result from many machine learning models that have trouble comprehending the context of Amazon reviews, particularly regarding subtle wordings, sarcasm, or irony. Additionally, these models can have biases that skew sentiment analysis results, mainly when working with a diverse set of Amazon review datasets. To overcome these, three machine learning models, namely, Bidirectional Encoder Representations from Transformers (BERT), Bidirectional and Auto-Regressive Transformers (BART), and Generative Pre-trained Transformers (GPT) are used in this study. During the experimental research, it was observed that BERT gave the highest accuracy of 90% when compared with BART (70%) and GPT (84%) models. BERTs bidirectional contextual comprehension at identifying subtleties in language provides a thorough and realistic representation of the sentiments of Amazon users, which is why the model gave the highest accuracy. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Lecture Notes in Networks and Systems, Vol-856 LNNS, pp. 453-465.
Dedeepya M., CHRIST University, Bengaluru, India; Kokatnoor S.A., CHRIST University, Bengaluru, India; Kumar S., CHRIST University, Bengaluru, India
Securing the Digital Realm: Unmasking Fraud in Online Transactions Using Supervised Machine Learning Techniques
Classification; Credit cards; Cyber security; Edited nearest neighbor; Fraud detection; Imbalanced datasets; Machine learning; Online transactions; SMOTE
A key component of contemporary banking systems and e-commerce platforms is identifying fraud in online transactions. Traditional rule-based techniques are insufficient for preventing sophisticated fraud schemes because of the increasing complexity and number of expanding online transactions. This research study examines the development of fraud detection methods, emphasizing data analytics and machine learning (ML) models. The study also focuses on the fact that developing efficient fraud detection systems requires continuous observation, data preprocessing, feature selection, and testing of models. Seven ML models, Logistic Regression (LR), Decision Trees (DT), k-Nearest Neighbors (kNN), Nae Bayes (NB), Support Vector Machine (SVM), Random Forests (RF), and Extreme Gradient Boosting (XGBoost) are considered for classifying the dataset into fraudulent or not. During the experimentation study, it was observed that XGBoost yielded the highest accuracy of 99% when compared to other models. Users can determine which features significantly influence the model's predictions by using XGBoost's feature significance insights. Additionally, XGBoost provides integrated support for managing missing values in data, negating the requirement for imputation and other preprocessing procedures. Due to these, it performed better. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Lecture Notes in Networks and Systems, Vol-1121 LNNS, pp. 477-489.
Reddy G.Y., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Bangalore, India; Kokatnoor S.A., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Bangalore, India; Kumar S., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Bangalore, India
Text Summarization Techniques for Kannada Language
Abstractive; Extractive; LSTM; ROUGE; Text processing; TF-IDF
Text Summarization is summarizing the original text document into a shorter description. This short version should retain the meaning and information content of the original text document. A concise summary can help humans quickly understand a large original document better in a short time. Summarization can be used in many text documents, such as reviews of books, movies, newspaper articles, content, and huge documents. Text summarization is broadly classified into extractive Text Summarization (ETS) and Abstractive Text Summarization (ATS). Even though more research works are carried out using extractive methods, meaningful summaries can be attained using abstractive summary techniques, which are more complex. In Indian languages, very few works are carried out in abstract summarization, and there is a high need for research in this area. The paper aims to generate extractive and abstractive summaries of the text by using deep learning and extractive summaries and comparisons between them in the Kannada language. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Yogish D.; Jalapur S.; Yogisha H.K.; Mithun B.N.
Lecture Notes in Networks and Systems, Vol-1121 LNNS, pp. 107-117.
Yogish D., CHRIST University, Karnataka, Bengaluru, India; Jalapur S., CHRIST University, Karnataka, Bengaluru, India; Yogisha H.K., Ramaiah Institute of Technology, Karnataka, Bengaluru, India; Mithun B.N., CHRIST University, Karnataka, Bengaluru, India
Predictive Analytics for Network Traffic Management
LSTM networks; machine learning; network traffic management; predictive analytics; real-time processing
It examines how this can be applied to monitoring network traffic and carrying out predictive analysis to improve the functionality and effectiveness of network management. The study uses historical data of the network traffics and uses machine learning techniques such as the Long Short Term Memory based models and the Ensemble Methods to predict the traffic patterns in the future. It includes data gathering, data pre-processing, feature selection, model choice, model training, model validation, and the architectural setup of the machine learning solution in a real-time stream processing pipeline using Apache Kafka and Apache Flink. It is evident from the results that the proposed models yield a high level of accuracy in terms of prediction and that the Ensemble method alone gives a slightly higher accuracy than LSTM in the specific metrics. Real-time values closely followed actual traffic level, thus allowing real-time adjustments in network usage. In light of this, there is a clear understanding of the significance of having reliable data preprocessing, feature engineering, and model optimization process. The study also notes the need in prediction concerning data quality and scalability issues taking into account that current and future networks are characterized as dynamic and highly complex to offer more effective solutions for intelligent and proactive networking. 2024 IEEE.
Jain N.; Husain S.O.; Goyal S.; Hariharasudhan S.; Victor M.; Manjula
2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024
https://doi.org/10.1109/IICCCS61609.2024.10763554 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214438436&doi=10.1109%2fIICCCS61609.2024.10763554&partnerID=40&md5=d44f26ed7daefa23fc7dd5486e317a68
Jain N., Ies College of Technology, Department of Electrical & Electronics Engineering, M.P., Bhopal, India; Husain S.O., The Islamic University, College Of Technical Engineering, Department Of Computers Techniques Engineering, Najaf, Iraq, The Islamic University Of Al Diwaniyah, College Of Technical Engineering, Department Of Computers Techniques Engineering, Al Diwaniyah, Iraq; Goyal S., Chandigarh Group of Colleges, Chandigarh Engineering College, Department of Computer Science Engineering, Punjab, Mohali, 140307, India; Hariharasudhan S., Prince Shri Venkateshwara Padmavathy Engineering College, Chennai, India; Victor M., Christ (Deemed to be University), School of Business and Management, India; Manjula, Saveetha Institute of Medical and Technical Sciences, Department of Mathametics, Tamilnadu, Chennai, India
A Comprehensive Review of Small Building Detection in Collapsed Images: Advancements and Applications of Machine Learning Algorithms
Collapsed images; Machine learning; Small building detection
Accurately identifying small buildings in images of collapses is essential for disaster assessment and urban planning. In the context of collapsed images, this study provides an extensive overview of the methods and approaches used for small building detection. The investigation covers developments in machine learning algorithms, their uses, and the consequences for urban development and disaster management. This work attempts to give a brief grasp of the difficulties, approaches, and potential paths in the field of small building detection from collapsed imaging through a thorough investigation of the body of existing literature. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Lecture Notes in Networks and Systems, Vol-1121 LNNS, pp. 429-438.
Sajitha I., Department of Computer Science and Engineering, CHRIST (Deemed to Be University), Karnataka, Bangalore, India; Sambandam R.K., Department of Computer Science and Engineering, CHRIST (Deemed to Be University), Karnataka, Bangalore, India; John S.P., Department of Computer Science and Engineering, Jyothi Engineering College, Cheruthuruthy, Kerala, Thrissur, India
Recent Advances in Pedestrian Identification Using LiDAR and Deep Learning Methods in Autonomous Vehicles
Autonomous vehicles; Deep learning; LiDAR; Pedestrian detection
The myriad benefits of autonomous vehicles (AVs) encompassing passenger convenience, heightened safety, fuel consumption reduction, traffic decongestion, accident mitigation, cost-efficiency and heightened dependability have underpinned their burgeoning popularity. Prior to their full-scale integration into primary road networks substantial functional impediments in AVs necessitate resolution. An indispensable feature for AVs is pedestrian detection crucial for collision avoidance. Advent of automated driving is swiftly materializing owing to consistent deployment of deep learning (DL) methodologies for obstacle identification coupled with expeditious evolution of sensor and communication technologies exemplified by LiDAR systems. This study undertakes exploration of DL-based pedestrian detection algorithms with particular focus on YOLO and R CNN for purpose of processing intricate imagery akin to LiDAR sensor outputs. Recent epochs have witnessed DL approaches emerge as potentially potent avenue for augmenting real-time obstacle recognition and avoidance capabilities of autonomous vehicles. Within this scholarly exposition we undertake exhaustive examination of latest breakthroughs in pedestrian detection leveraging synergy of LiDAR and DL systems. This discourse comprehensively catalogues most pressing unresolved issues within realm of LiDAR-DL solutions furnishing compass for prospective researchers embarking on journey to forge forthcoming generation of economically viable autonomous vehicles. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Halgatti A.; Chethana G.; Shivaprasad G.; Gayathri R.; Babu S.R.; Girish G.P.
Lecture Notes in Networks and Systems, Vol-1168 LNNS, pp. 187-196.
Halgatti A., School of Business and Management, CHRIST (Deemed to be University), Bengaluru, India; Chethana G., RV College of Engineering, Bengaluru, India; Shivaprasad G., Faculty of Management Studies, CMS Business School, JAIN (Deemed-to-be University), Bengaluru, India; Gayathri R., CMS Business School, JAIN (Deemed-to-be University), Bengaluru, India; Babu S.R., Faculty of Management Studies, CMS Business School, JAIN (Deemed-to-be University), Bengaluru, India; Girish G.P., Department of Finance, (a Deemed to-be-University Under Sec 3 of UGC Act 1956), ICFAI Business School, IFHE University, Hyderabad, India
Real-time Traffic Prediction in 5G Networks Using LSTM Networks
5G networks; deep learning; LSTM networks; network management; real-time traffic prediction
This research explores the application of Long Short-Term Memory (LSTM) networks for real-time traffic prediction within 5G networks, aiming to address the critical need for accurate prediction models in dynamic network environments. Leveraging the sequential learning capabilities of LSTM networks, the proposed methodology encompasses dataset preparation, model architecture design, training, and evaluation. Experimental results demonstrate the effectiveness of the LSTM-based prediction model in capturing temporal dependencies and providing reliable predictions across various prediction horizons. While promising, further research is warranted to enhance the model's performance and address remaining challenges. This study contributes to advancing the state-of-the-art in traffic prediction methodologies, facilitating more efficient network management and optimization in 5G environments. 2024 IEEE.
Kumar H.; Al-Jawahry H.M.; Manohar M.; Karthika K.; Victor M.; Shetty A.D.
Kumar H., Chandigarh Group of Colleges, Chandigarh Engineering College, Department of Computer Application, Punjab, Mohali, India; Al-Jawahry H.M., The Islamic University, College Of Technical Engineering, Department Of Computers Techniques Engineering, Najaf, Iraq, The Islamic University Of Al Diwaniyah, College Of Technical Engineering, Department Of Computers Techniques Engineering, Al Diwaniyah, Iraq; Manohar M., Ies College of Technology, Department of Electronics & Communication Engineering, M.P., Bhopal, India; Karthika K., Prince Shri Venkateshwara Padmavathy Engineering College, Chennai, India; Victor M., Christ (Deemed to be University), School of Business and Management, India; Shetty A.D., (Deemed to be University) Nmam Institute of Technology (NMAMIT), Department of Cse Nitte, India