Browse Items (14421 total)
Sort by:
-
Early bruise detection, classification and prediction in strawberry using Vis-NIR hyperspectral imaging
The most frequent kind of damage to strawberries is bruising. However, most of the bruises are so barely perceptible at an early stage on the surface, that detection of them with the human eye is quite challenging. This study proposes a method for accurately detecting and classifying the damage using reflectance imaging spectroscopy. In order to carry out the study, an experiment was devised to artificially induce bruises and a dataset was generated at different bruise intervals. A model for detecting and classifying bruises at their latent stage was developed using machine learning classifiers, including support vector machines (SVM), k-nearest neighbors (KNN), linear discriminant analysis (LDA), random forest (RF), and decision tree (DT), to investigate the changes over time after bruise occurrence on the detection performance. Regression models for the prediction of bruising time were developed using partial least square regression (PLSR), RF, gradient boosting (GB), support vector regression (SVR), and DT. Among the compared models, both SVM and LDA could achieve 99.99 % classification accuracy. RF was regarded as being the most advisable for detection and prediction jobs due to its high performance. It achieved MSE of 0.052 and R2 of 0.989 for prediction. 2024 Elsevier Ltd -
Analysis of the chemical properties and high-temperature rheological properties of MDI modified bio-asphalt
As an environmentally friendly material, bio-oil is employed to partially replace non-renewable petroleum asphalt, but its addition weakens the high-temperature non-deformability of petroleum asphalt. Therefore, the 4,4?-diphenylmethane diisocyanate (MDI) was employed as a chemical modifier of bio-asphalt to improve its high temperature rheological properties. The MDI with addition of 0.5%, 1%, 2%, 4% by weight, and the bio-oil with addition of 12% were used to obtain the MDI modified bio-asphalts. The chemical reaction mechanism between the MDI and bio-asphalt was analyzed by employing the Fourier-transform infrared spectroscopy (FTIR) and gel permeation chromatography (GPC) tests. Meanwhile, the rotational plate viscosity (RPV) test, the temperature sweep test, and the multiple stress creep and recovery (MSCR) test were employed to evaluate the high-temperature rheological properties of the MDI modified bio-asphalts. Moreover, the relationships between the chemical reaction mechanism and high-temperature rheological parameters of MDI modified bio-asphalt were established. Test results show that a nucleophilic addition reaction occurred between the MDI and the active hydrogen of bio-asphalt to form urethane chains, which increased the content of macromolecular polymers in the bio-asphalt. The MDI increased the G*/sin? (rutting factor) and the E(?) (visco-flow activation energy) of the bio-asphalt, but decreased its permanent strain and Jnr (non-recoverable creep compliance). Therefore, the MDI modifier effectively enhanced the permanent non-deformability of the bio-asphalt. Both IUrethane and LMS were positively correlated with the rutting factor, viscosity and 1/Jnr, and had significant correlations at a significance level of 0.05. Furthermore, the optimal ratio of MDI to bio-oil was determined to be 1:6 by mass. 2020 Elsevier Ltd -
AI-Driven Tutorial Code Learning System: Personalized Programming Education Through Adaptive Instruction and Gamification
Existing programming education faces critical challenges, such as lack of personalization, restricted feedback tools, and scalability limitations that hinder efficient learning outcomes. This paper presents an AI-powered tutorial code learning system to transform programming education through personalized and adaptive instruction. The system integrates advanced components, including learner modeling, intelligent content recommendation, error analysis, adaptive evaluation, gamification, learning analytics, integration frameworks, quality assurance, security, and scalability layers. To evaluate the system, the study employs a mixed-methods research approach, incorporating embedded case studies and a randomized controlled trial (RCT). Rigorous data collection methods, system measure validation, undergraduate program participant selection, quality assurance protocols, statistical analysis, and ethical considerations are utilized in this work. The architecture demonstrates potential for scalable and globally accessible programming education, addressing traditional challenges through personalized learning protocols. Unlike traditional platforms offering static content and limited feedback, this AI-powered system acts as a personalized tutor, providing active problem-solving and continuous learner engagement. The adaptive system delivers optimal learning paths based on individual student needs and has the potential to transform programming education delivery and outcomes. 2025 IEEE. -
Performance analysis of Clustering algorithms for dyslexia detection
Clustering algorithms plays vital role in analysing and evaluating vast number of high dimensional health care data ranging from medical data repositories, clinical data, electronic health records, body sensor networks, IoT devices, and so on. Dyslexia, a learning disorder is a common problem that is found in children during the initial stages of formal education, which is detected as mild to severe. It can also be one of the reasons of failure in the school. According to the literature this difficulty is commonly seen among Special Education Need children. There are few studies focussed on the application of classification algorithms for detecting the presence of dyslexia. This paper focusses one of SDG, goal 4:Quality Education, as dyslexic students can be given equal and quality education. Analyses of an online gamified test-based dataset is done by applying various clustering techniques such as K-means, Fuzzy c-means, and Bat K-means to assess their effectiveness in detecting the problem dyslexia. As the dataset is large, it is observed that usage of clustering methods gives us gain insight into the distribution of data to observe characteristics of each cluster. The clustering results are evaluated using root mean squared error (RMSE), mean absolute error (MAE), Xie-Beni index and it is found K Means outperforms FCM, Bat K Means algorithm for analysing different levels of the learning disorder. The Electrochemical Society -
Emotion Trajectory Analysis and Model Comparison for Hate Speech and Radicalization Detection in Code-Mixed Platforms
The growing presence of multilingual and codemixed content on social media creates major challenges for automated emotion recognition and mental health support. In this work, we introduce an emotion-aware computational framework that processes code-mixed Indian language comments and predicts user emotions with high accuracy, followed by context-aware support suggestions. Our dataset comes from the AI4Bharat IndicNLP corpus [14] and the Dravidian-CodeMix sentiment dataset [15], featuring a variety of multilingual user comments. To maintain linguistic consistency, we translate the raw texts into English using Google Translator and then preprocess them through normalization, tokenization, and stopword removal. We use three advanced transformer-based models, DistilBERT (six emotions), DistilRoBERTa (seven emotions), and RoBERTa GoEmotions (27+ emotions), to categorize the emotions in the comments. We compare predictions across the models and select the most reliable label for each text, which is further verified through manual checks with human annotators. This process results in a curated dataset labeled with emotions and enriched with model provenance. With this dataset, we train a Logistic Regression classifier using TF-IDF features to create an efficient, explainable prediction pipeline. The system classifies emotions and provides tailored suggestions based on emotional states, improving user support in online interactions. Experimental results show the robustness of the pipeline and its ability to adapt to various code-mixed inputs. This study offers an integrated dataset-model-suggestion framework that advances emotion recognition in multilingual contexts and supports the creation of practical emotion-aware digital systems. 2025 IEEE. -
Dynamic job sequencing of converging-diverging conveyor system for manufacturing optimization
Some sectors, such as dairy, automobile, pharmaceutical, computer and electronics, require a range of manufacturing steps to produce a component. The goods in these industries are produced in varieties and the output volume varies from low to high. Typically, these types of businesses use a conveyor system that could have a combination of a diverging and converging conveyor system due to a variety of processing phases involved in the development of the commodity. A conceptual model of the of conveyor system is described, which works manually and to illustrate the importance of the sequence using buffer the buffer layout is modeled and compared to the manual layout. The genetic algorithm is used to find the optimal buffer storage. It can be observed that by adapting various sequencing methods there will be reduction in manufacturing time and setup cost. 2022 Elsevier Ltd. All rights reserved. -
Mirabijalones S-W, rotenoids from rhizomes of white Mirabilis jalapa Linn. and their cell proliferative studies
Five undescribed (2-6) rotenoid derivatives along with three known rotenoids (1, 7 and 8) were isolated from the rhizomes of white colored variety of Mirabilis jalapa Linn. The structures of these undescribed compounds were elucidated based on UV, IR, HR-MS (ESI), 1D and 2D NMR spectroscopic techniques. Selected compounds were evaluated for their cell viability and proliferation in two cancer cell lines namely, cervical (HeLa), breast (SKBR-3) and normal lung fibroblast (WI-38). Among them, the compounds Boeravinone C (1), Mirabijalone S (2), Mirabijalone T (3) and 4, 6, 11-trihydroxy-9-methoxy-10-methylchromeno [3, 4-b] chromen-12(6H)-one (8) showed moderate cytotoxicity against HeLa cells with IC50 values in the 8.40 ? 12.9 ?M range, and compound 8 exhibited cytotoxicity against SKBR-3 cells with IC50 value of 17.6 ?M. Molecular docking studies of isolated compounds were performed with three apoptosis proteins, 3H11, 2AR9 and 1X0X. These results revealed that the isolated compounds were found to interact with Caspase 8 and 9 along with the anti-apoptotic protein Survivin. Since these compounds exhibit cytotoxic effects against SKBR3 and HeLa cells, they are expected to show apoptosis and may be further utilized for wet lab apoptotic studies. 2021 Phytochemical Society of Europe -
Modelling of critical success factors for blockchain technology adoption readiness in the context of agri-food supply chain
The agri-food supply chain is continuously facing several challenges; the most severe are food quality and safety issues. These issues debilitate the performance of the supply chain and often harm the consumer's health. Therefore, there is an urgent need to address food quality and safety assurance in the supply chain. Blockchain technology (BCT) holds the potential to resolve these issues by enhancing security and transparency. The present study explores the critical success factors (CSFs) of BCT adoption readiness in the AFSC. Initially, CSFs are identified through a literature survey and finalised by experts' opinion. The finalised factors are prioritised using the fuzzy best-worst method, followed by sensitivity analysis. The results reflect that 'food quality control', 'provenance tracking and traceability', and 'partnership and trust' as the top three success factors. The study's findings will assist policymakers, managers, and practitioners in strategising the decision-making process while BCT dissemination. Copyright 2023 Inderscience Enterprises Ltd. -
Investigating the transport flexibility measures for freight transportation: a fuzzy best-worst method approach
Unpredicted disruptions force organisations to ensure flexibility for fulfilling customer demand. Enabling flexibility along the transportation system is the most suitable solution for unpredictable disruptions. Flexibility, being a potential element, requires more attention to gain competitive advantages. In this study, an effort has been made to investigate different transport flexibility measures (TFMs) related to freight transportation. Initially, an extensive literature survey is performed to identify different TFMs linked with the supply chain and logistics domain. Further, an integrated fuzzy best-worst method (FBWM) has been adopted to prioritise the identified TFMs and sensitivity analysis is performed to ensure robustness of the model. The findings of the study reflect mode, fleet, vehicle and speed flexibility as the significant flexibility measures for freight transportation. This study will help practitioners, managers and decision-makers associated with freight transportation to make better decisions to ensure flexibility in the freight transportation system. Copyright 2022 Inderscience Enterprises Ltd. -
Breaking down the barrier: exploring queer young adults experience with counselling
This study explores the impact of counselling on the mental health of queer young adults in India. The research uses qualitative methods, including semi-structured interviews, to understand how counselling impacts their identities and well-being. The study involved 12 participants aged 1825, representing diverse genders and sexual orientations across different regions of India. Thematic analysis revealed three main themes: Queer Identity: Recognized, Misrecognized, and Negotiated in Therapy, Queer recommendations for inclusive care, and Empowering queer mental health journey. Each main theme revealed three sub-themes. The study emphasises the importance of therapists competence, empathy, and affirmative practices in creating a supportive therapeutic environment for queer young adults. The participant narratives highlight the complex character of the therapeutic process, going beyond professional interventions to include concepts like belonging, empowerment, and validation. 2026 College of Sexual and Relationship Therapists. -
Security and privacy issues in existing biometric systems and solutions
[No abstract available] -
Promoting Net-Zero Economy for Sustainable Development: Practice-Based View
The present research investigates the utilization of various resources, including tangible assets, human expertise, and intangible assets, in a cohesive set of established procedures, which impact the development and implementation of net-zero practices. It also explores the effect on the environmental performance of SME enterprises operating in business markets. Additionally, the study explores whether digitalization plays a moderating role in this relationship. The samples of 291 were used in the study. Data were analyzed using partial least square structural equation modeling. For a sustainable net-zero economy (SNZE), it is essential for managers to acknowledge the importance of resource and capabilities management. While the management of tangible assets and human skills is vital, greater emphasis should be placed on intangible resources like organizational culture and learning. Furthermore, the capacity of small-sized enterprises (SMEs) to process and implement knowledge could prove to be instrumental in accomplishing net-zero targets. Consequently, managers should leverage Industry-4.0-based technological solutions to enhance resource and capabilities management effectively. This research pioneers an exploration into the influence of human capital and various assets (tangible and intangible), on the development and implementation of a SNZE in organizations, underpinned by empirical data. The study broadens the understanding of the practice-based view (PBV) framework in realizing SNZE, particularly within SME B2B enterprises. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Face Detection-Based Border Security System Using Haar-Cascade and LBPH Algorithm
Border security is a process which measures the border management ideas by a country or set of countries to wage against unspecific and unauthorized travel or trade across the country borders, to bound non-legal deport, various crimes combat, and foreclose dangerous criminals from entering in the country. A system will help in keeping a check on those personnels who forge with the legal document with an intension to cross the border. This article discusses about border security of whole Indian context, and there are various such systems which have been built since 2010 as wireless sensor network system named Panchendriya. Remote and instruction manual switch mode arm system using ultrasonic sensor for security of border. This article we have made use of Haar-Cascadian along with LBPH algorithm with their functioning. The result and discussion section we compare most recent face recognition techniques that have been used in the last ten years. The proposed prototype is discussed and shown through simulation model, it provide better result compare to existing model. The proposed Haar-Cascade and LBPH algorithm provide 10% better performance. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Inventory model for deteriorating items with ramp type demand under permissible delay in payment
Permissible delay in payment is a common method of payment often used by the suppliers and it generally leads to higher sales and ultimately higher revenue. This method is significant in the case of deteriorating products. In this paper, an inventory model for the deteriorating items with price and time-dependent ramp type demand is presented with shortages allowed and partially backlogged. The solution procedure is illustrated by numerical examples. The concavity of the profit function with respect to the decision variable is discussed analytically. Numerical analysis shows that the profit per unit time increases with the delay payment facility. Copyright 2021 Inderscience Enterprises Ltd. -
Subsume Pediatric Headaches in Psychiatric Disorders? Critiques on Delphic Nosology, Diagnostic Conundrums, and Variability in the Interventions
Purpose of Review: Tension-type headache (TTH) continues to be the most prevalent type of headache across all age groups worldwide, and the global burden of migraine and TTH together account for 7% of all-cause years lived with disability (YLDs). TTH has been shown to have a prevalence of up to 80% in several studies and presents a wide range and high variability in clinical settings. The aim of this review is to identify gaps in diagnostics, nosology, and variability in the treatment of children and adolescents who present with headaches without an identifiable etiology. Recent Findings: Migraine and TTH have been debated to have more similarities than distinctions, increasing chances of misdiagnosis and leading to significant cases diagnosed as probable TTH or probable migraine. The lack of specificity and sensitivity for TTH classification often leads to the diagnosis being made by negating associated migraine symptoms. Although pathology is not well understood, some studies have suggested a neurological basis for TTH, in need of further validation. Some research indicates that nitric oxide signaling plays an integral part in the pain mechanisms related to TTH. Analgesics and non-steroidal anti-inflammatories are usually the first lines of treatment for children with recurring headaches, and additional treatment options include medication and behavioral therapies. Summary: With high prevalence and socioeconomic burden among children and adolescents, its essential to further study Tension-type headaches and secondary headaches without known cause and potential interventions. Treatment studies involving randomized controlled trials are also needed to test the efficacy of various treatments further. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Consolidation of Cloud Computing in Smart and Sustainable Environment
Cloud computing has revolutionized IoT device data collection, administration, and analysis by offering a scalable and sustainable solution for managing vast amounts of data. The paper highlights cloud computing's benefits in data processing, device management, cost efficiency and scalability. However, challenges related to security, data ownership, and vendor lock-in require attention. A novel sustainable cloud-IoT model is presented by integrating smart computing with cloud infrastructure. It is observed that the model records promising performance. The mean response delay is 1.9 seconds and the 89.5% is the generated mean computational storage accuracy rate. In conclusion, the cloud computing empowered sustainable model can be used in organizations to gain insights from IoT data and make informed decisions, shaping future research in this rapidly evolving field. 2023 IEEE. -
Deep Learning Character Recognition of Handwritten Devanagari Script: A Complete Survey
Recognition of handwritten characters is a concept in which the single characters are classified, it is a facility of an electronic device to scan and decipher the handwritten input from a variety of sources, including written texts, images, and other digital touch-screen devices. This concept is being used in distinctive sectors such as the processing of bank checks, form data entry, and parcel posting and nowadays it is becoming a very important issue in the pattern recognition domain and a very challenging task to resolve it. Since deep learning is a crucial strategy in solving detection and pattern recognition problems, several algorithms are available to classify the characters with better prediction rates on different datasets, and ultimately, whichever algorithm gives the optimized results will be considered the best solution for the character recognition problem. As a result, various solutions proposed by the existing researchers are discussed using deep learning algorithms in this survey article. 2023 IEEE. -
A Comparative Investigation on the use of Machine Learning Techniques for Currency Authentication
In the present banking sector, identifying the real and the fake note is a very challenging task because if we do it manually, it takes a long time to check which is real and which is fake. This research study article aims to authenticate the money between real and fake by using different machine algorithms facilitating learning, such as K-means Clustering, Random Forest Classification, Support Vector Machines, and logistics Regression. Specifically, we consider the banknote dataset. The data of money is extracted from various banknote images by using the wavelet transform tool, which is primarily used to remove elements from the images. However, we are mainly concerned with the different machine learning algorithms, so we take the two variables, where the first variable indicates image variance and the second indicates image skewness. We use these two variables to train our machine learning algorithms. So, majorly, by applying the different machine learning algorithms, which are supervised and unsupervised, we find the accuracy for the respective machine learning algorithms and then visualize and classify the real and fake notes separately. Finally, the prediction is based on integrity, which means the efficiency value is based on how much the mechanism system can uncover the fake notes. Then, after calculating the accuracy of currency authentication, there is a high possibility that the accuracy of the particular algorithm is the best algorithm, so the application of currency authentication will be very useful for the bank to easily find duplicate notes. 2022 IEEE. -
Two inventory models for growing items under different payment policies with deterioration
Industries of growing items show an upward trend in the production as well as in consumption. Poultry and livestock are good examples of growing items which are both deteriorating and ameliorating in nature. In this study apart from these specific features of growing items, one of the real-world business policies, permission of delay in payment is also considered. Present paper proposed two inventory models, one with the permission of delay in payment and another without it. Concavity of the profit functions with respect to decision variables are discussed analytically for both the models. Solution procedure and numerical examples are provided in order to get the managerial insights. The numerical analysis growth in weight is approximated by Richard's growth function. The numerical analysis predicts that net profit and the initial purchase quantity both increases under the permissible delay payment policy compared to without it. Sensitivity analysis provides important managerial insights. Copyright 2022 Inderscience Enterprises Ltd. -
Optimal procurement and pricing policy for deteriorating items with price and time dependent seasonal demand and permissible delay in payment
In practice, items like food, nursery plants, medicines, etc. are seasonal and deteriorating in nature. For this type of products, permissible delay in payment is a common business policy, which is used to increase in the sell volume and to develop trust in buyer-seller relationship. In this paper, we developed an inventory model for time dependent deteriorating seasonal items with the permission of delay in payment. Shortages are permitted and partially back ordered. Our aim is to find optimal selling price and ordering quantity simultaneously. Concavity of profit function with respect to decision variables has been discussed analytically. A solution procedure followed by a numerical example and sensitivity analysis along with managerial insights are provided. Numerical analysis predicts that delay in payment profit policy is a better decision in order to maximise the profit or in order to get more profit. 2022 Inderscience Enterprises Ltd.