Journal Articles
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/19884
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Item A Review on Carotid Ultrasound Atherosclerotic Tissue Characterization and Stroke Risk Stratification in Machine Learning Framework(Current Medicine Group LLC 1 info@phl.cursci.com, 2015) Sharma, A.M.; Gupta, A.; Kumar, P.K.; Rajan, J.; Saba, L.; Nobutaka, I.; Laird, J.R.; Nicolades, A.; Suri, J.S.Cardiovascular diseases (including stroke and heart attack) are identified as the leading cause of death in today’s world. However, very little is understood about the arterial mechanics of plaque buildup, arterial fibrous cap rupture, and the role of abnormalities of the vasa vasorum. Recently, ultrasonic echogenicity characteristics and morphological characterization of carotid plaque types have been shown to have clinical utility in classification of stroke risks. Furthermore, this characterization supports aggressive and intensive medical therapy as well as procedures, including endarterectomy and stenting. This is the first state-of-the-art review to provide a comprehensive understanding of the field of ultrasonic vascular morphology tissue characterization. This paper presents fundamental and advanced ultrasonic tissue characterization and feature extraction methods for analyzing plaque. Additionally, the paper shows how the risk stratification is achieved using machine learning paradigms. More advanced methods need to be developed which can segment the carotid artery walls into multiple regions such as the bulb region and areas both proximal and distal to the bulb. Furthermore, multimodality imaging is needed for validation of such advanced methods for stroke and cardiovascular risk stratification. © 2015, Springer Science+Business Media New York.Item Automated microaneurysms detection for early diagnosis of diabetic retinopathy: A Comprehensive review(Elsevier B.V., 2021) Mayya, V.; Kamath S․, S.S.; Kulkarni, U.Diabetic retinopathy (DR), a chronic disease in which the retina is damaged due to small vessel damage caused by diabetes mellitus, is one of the leading causes of vision impairment in diabetic patients. Detection of the earliest clinical sign of the advent of DR is a critical requirement for intervention and effective treatment. Ophthalmologists are trained to identify DR, based on examining specific minute changes in the eye - microaneurysms, retinal haemorrhages, macular edema and changes in the retinal blood vessels. Segmentation of microaneurysms (MA) is a critical requirement for the early diagnosis of DR and has been the primary focus of the research community over the past few years. In this work, a systematic review of existing literature is carried out to examine the diagnostic use of automated MA detection and segmentation for early DR diagnosis. We mainly focus on existing early DR diagnosis techniques to understand their strengths and weaknesses. Though early diagnosis is performed using colour fundus photography, fluorescein angiography or optical coherence tomography angiography images, our study is limited to colour fundus based techniques. The early DR diagnosis methodologies reviewed in this article can be broadly classified into classical image processing, conventional machine learning (ML), and deep learning (DL) based techniques. Though significant progress has been achieved in these three classes of early DR diagnosis, several challenges and gaps still exist, underscoring a considerable scope for the development of fully automated, user-friendly early DR diagnosis and grading systems. We discuss in detail the challenges that need to be addressed in designing such effective, efficient, and robust algorithms for early DR diagnosis systems and also the ample scope for future research in this area. © 2021Item Machine learning approach for optimization and performance prediction of triangular duct solar air heater: A comprehensive review(Elsevier Ltd, 2023) Nidhul, K.; Thummar, D.; Yadav, A.K.; Anish, S.This paper presents a comprehensive review of various kinds of distinct artificial roughness employed in rectangular and triangular duct solar air heaters to aid prospective researchers in finding a critical gap in the domain of solar air heaters. A Machine Learning (ML) model is developed using 72 distinct rib combinations compiled to 454 datasets and trained using an Artificial Neural Network (ANN) to predict the performance of ribbed triangular duct Solar Air Heater (SAH). The developed ML model predicts the data with an average deviation of <3%. Owing to reasonably accurate predictions, the same could be increased when more cases (geometric or operating parameters) are added to the databases by retraining the ANN. Further, a second law analysis of the rib configurations features collector efficiency and entropy generation variation with Re for various rib parameters. For the Re range of 4000 to 18000, optimum parameters such as rib height, pitch, chamfer angle, and inclinations are obtained for triangular duct SAH. This could help design engineers obtain the performance parameters of ribbed triangular duct SAH with other artificial roughness designs, possibly with a combination of different geometrical and operating parameters, without having to perform tests. © 2023 International Solar Energy SocietyItem Computational materials discovery and development for Li and non-Li advanced battery chemistries(International Association of Physical Chemists, 2023) Sharma, H.; Nazir, A.; Kasbe, A.; Kekarjawlekar, P.; Chatterjee, K.; Motevalian, S.; Claus, A.; Prakash, V.; Acharya, S.; Sahu, K.K.Since the discovery of batteries in the 1800s, their fascinating physical and chemical properties have led to much research on their synthesis and manufacturing. Though lithium-ion batteries have been crucial for civilization, they can still not meet all the growing demands for energy storage because of the geographical distribution of lithium resources and the intrinsic limitations in the cell energy density, performance, and reliability issues. As a result, non-Li-ion batteries are becoming increasingly popular alternatives. Designing novel materials with desired properties is crucial for a quicker transition to the green energy ecosystem. Na, K, Mg, Zn, Al ion, etc. batteries are considered the most alluring and promising. This article covers all these Li, non-Li, and metal-air cell chemistries. Recently, computational screening has proven to be an effective tool to accelerate the discovery of active materials for all these cell types. First-principles methods such as density functional theory, molecular dynamics, and Monte Carlo simulations have become established techniques for the preliminary, theoretical analysis of battery systems. These computational methods generate a wealth of data that might be immensely useful in the training and validating of artificial intelligence and machine learning techniques to reduce the time and capital expenditure needed for discovering advanced materials and final product development. This review aims to summarize the application of these techniques and the recent developments in computational methods to discover and develop advanced battery chemistries. © 2023 by the authors; licensee IAPC, Zagreb, Croatia.Item Machine learning-based approaches to enhance the soil fertility—A review(Elsevier Ltd, 2024) Sujatha, M.; Jaidhar, C.D.Agriculture plays an imperative role in many countries’ economies and is a substantive source of survival. The variation in a soil nutrient decreases crop yield. An accurate soil fertility classification and application of fertilizers are essential for enhancing crop productivity. Currently, soil fertility levels are assessed through laboratory testing of soil samples, and fertilizers are applied randomly. This traditional practice increases fertilization costs and causes environmental pollution. Thus, it is necessary to develop robust and inexpensive soil fertility classification and fertilizer application. This study identifies the machine learning (ML) or deep learning-based soil fertility classifications. A comprehensive review is conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The purpose of this study is to examine different approaches that researchers use to predict or classify soil fertility. It also discusses the fertilizer recommendation developed by the researchers. The earlier research showed that ML-based approaches could accurately classify soil fertility. Furthermore, this study discusses the importance of soil nutrients and preventive measures to be taken on the imbalance of soil nutrients. This study explores research gaps and challenges in soil fertility classification and fertilizer recommendation systems. Most studies predicted the fertility levels of soil parameters, whereas a few researchers classified soil fertility. Few researchers recommended fertilizers for soil nutrient depletion. Most studies relied on expensive laboratory measurements or regional soil data collected from satellites. Based on the identified research gaps, this study suggests potential future research possibilities in soil fertility classification and the recommendation of fertilizers. It aims to develop a low-cost soil fertility classifier to prescribe fertilizers. The developed model can help farmers to enhance soil fertility with reduced fertilization costs. © 2023 Elsevier LtdItem Semantic Segmentation of Remotely Sensed Images for Land-use and Land-cover Classification: A Comprehensive Review(Taylor and Francis Ltd., 2025) Putty, A.; Annappa, B.; Pariserum Perumal, S.Remotely Sensed Images (RSI) based land-use and land-cover (LULC) mapping facilitates applications such as forest logging, biodiversity protection, and urban topographical kinetics. This process has gained more attention with the widespread availability of geospatial and remote sensing data. With recent advances in machine learning and the possibility of processing nearly real-time information on the computer, LULC mapping methods broadly fall into two categories: (i) framework-dependent algorithms, where mappings are done using the in-built algorithms in Geographical Information System (GIS) software and (ii) framework-independent algorithms, which are mainly based on deep learning techniques. Both approaches have their unique advantages and challenges. Along with the working patterns and performances of these two methodologies, this comprehensive review thoroughly analyzes deep learning architectures catering different technical capabilities like feature extraction, boundary extraction, transformer-based mechanism based mechanism, attention mechanism, pyramid pooling and lightweight models. To fine-tune these semantic segmentation processes, current technical and domain challenges and insights into future directions for analysing RSIs of varying spatial and temporal resolutions are summarized. Cross domain users with application specific requirements can make use of this study to select appropriate LULC semantic segmentation models. © 2025 IETE.Item Advanced thermal vision techniques for enhanced fault diagnosis in electrical equipment: a review(Springer, 2025) Anbalagan, A.; Persiya, J.; Mohamed Mansoor Roomi, S.; Arumuga Perumal, D.A.; Poornachari, P.; Vijayalakshmi, M.; Ebenezer, L.Ensuring the reliability and safety of electrical equipment is essential for industrial and residential applications. Traditional fault diagnosis methods involving physical inspections are time-consuming and ineffective for early fault detection. Infrared (IR) thermography offers a non-invasive and efficient solution by identifying anomalies in temperature profiles. This review explores thermal vision-based fault diagnosis techniques, including region of interest (ROI) segmentation, image pre-processing, and fault diagnosis algorithms, with a focus on deep learning approaches. The study highlights the effectiveness of machine learning models in enhancing fault detection accuracy while identifying challenges such as environmental variations, data inconsistencies, and system integration issues. The review discusses the role of real-time applications, wireless technologies, and AI-based automation in improving fault detection. Research gaps are identified, and future directions are proposed to enhance efficiency, reliability, and industrial adoption. © The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2025.Item Self-healing bacterial concrete: A bibliometric overview and state-of-the-art review on fundamentals, techniques and future perspectives(Elsevier Ltd, 2025) Ranjith, A.; Das, B.B.The augmentation of micro-cracks in concrete is unavoidable, and under varying external ambience, such cracks have the potential to cause concrete deterioration due to the ingress of deleterious substances. The utilization of bacteria enabled self-healing methods displayed promising outcomes in the sealing of such minute cracks, offering considerable benefits and reducing the need for human intervention. From this point of view, this article aims to provide comprehensive review of the existing literature on bacteria based self-healing concrete using Bibliometric analysis. The critical evaluation of the significant features that have a notable impact on the self-healing efficacy of cementitious composites incorporating bacteria is presented. These factors encompass primary aspects involving bacteria selection, healing conditions, influence of crack width, effect of pre cracking, influence of carrier compounds etc., emphasizing their significance on the healing performance. Also, the improvement in mechanical and durability properties through the utilization of bacteria-enabled cementitious composites for self-healing purposes is scrutinized. Furthermore, the performance of bacteria based self-healing concrete in aggressive environments like corrosion and carbonation exposure are critically reviewed. Additionally, this article delves into research on the application of Artificial Intelligence (AI) and Machine Learning (ML) in relation to bacteria enabled self-healing concrete. Finally, suggestions for future research directions and practical implementations in this domain are put forward. © 2025 Elsevier LtdItem A review on NLP zero-shot and few-shot learning: methods and applications(Springer Nature, 2025) Ramesh, G.; Sahil, M.; Palan, S.A.; Bhandary, D.; Ashok, T.A.; J, J.; Sowjanya, N.Zero-shot and few-shot learning techniques in natural language processing (NLP), this comprehensive review traces their evolution from traditional methods to cutting-edge approaches like transfer learning and pre-trained language models, semantic embedding, attribute-based approaches, generative models for data augmentation in zero-shot learning, and meta-learning, model-agnostic meta-learning, relationship networks, model-agnostic meta-learning (MAML), prototypical networks in few-shot learning. Real-world applications underscore the adaptability and efficacy of these techniques across various NLP tasks in both industry and academia. Acknowledging challenges inherent in zero-shot and few-shot learning, this review identifies limitations and suggests avenues for improvement. It emphasizes theoretical foundations alongside practical considerations such as accuracy and generalization across diverse NLP tasks. By consolidating key insights, this review provides researchers and practitioners with valuable guidance on the current state and future potential of zero-shot and few-shot learning techniques in addressing real-world NLP challenges. Looking ahead, this review aims to stimulate further research, fostering a deeper understanding of the complexities and applicability of zero-shot and few-shot learning techniques in NLP. By offering a roadmap for future exploration, it seeks to contribute to the ongoing advancement and practical implementation of NLP technologies across various domains. © The Author(s) 2025.Item Condition monitoring of face milling tool using K-star algorithm and histogram features of vibration signal(Elsevier B.V., 2016) Madhusudana, C.K.; Kumar, H.; Narendranath, S.This paper deals with the fault diagnosis of the face milling tool based on machine learning approach using histogram features and K-star algorithm technique. Vibration signals of the milling tool under healthy and different fault conditions are acquired during machining of steel alloy 42CrMo4. Histogram features are extracted from the acquired signals. The decision tree is used to select the salient features out of all the extracted features and these selected features are used as an input to the classifier. K-star algorithm is used as a classifier and the output of the model is utilised to study and classify the different conditions of the face milling tool. Based on the experimental results, K-star algorithm is provided a better classification accuracy in the range from 94% to 96% with histogram features and is acceptable for fault diagnosis. © 2016 Karabuk University