Browse Items (16488 total)
Sort by:
-
Solar pv tree: Shade-free design and cost analysis considering Indian scenario
In this paper, the performance and the cost-effectiveness of a solar PV tree for supplying the energy demand of a flood lighting system at a basketball court in the School of Engineering and Technology, Christ (Deemed to be University) at Bangalore, India, are analyzed. Also, the energy demand of a flood lighting system for year 2017 is estimated (16 kWh/day), and the design of 4 individual trees of 1 kWp each is proposed, which saves around 40 sq.m area of land near to the basketball court. The experimental data was collected from June 1st, 2018 to May 31st, 2019, using a data acquisition system and processed to calculate the monthly cost of energy produced by each tree. In order to reduce the complexity in design and allow it to be shade-free, all the panels of a tree were oriented at the same azimuth angle. Based on technical and economical assessments with respect to rooftop systems, the solar PV tree presented reasonable results and could be a future adoptable technology for high population density areas, as well as for remote applications. Later, the adoptability of the proposed solar PV tree was simulated for 2 kWp, considering the climatic conditions of 2020, for different rural and urban locations of India. From the techno-economic-environmental analysis, it is highlighted that the annual energy yield is more with the solar PV tree model than with a land-mounted SPV system. The cost savings and greenhouse gas (GHG) reduction are also higher with the proposed oak tree-based solar PV tree in urban areas than in rural areas recommending it for practical applications. 2021, Walailak University. All rights reserved. -
Optimal Siting of Capacitors in Distribution Grids Considering Electric Vehicle Load Growth Using Improved Flower Pollination Algorithm
The optimal VAr compensation using capacitor banks (CBs) in radial distribution networks (RDNs) is solved in this paper while taking the growth of the load from electric vehicles (EVs) into consideration. This is accomplished by adapting an improved variant of the flower pollination algorithm (IFPA) with an enhanced local search capability. The primary objective of determining the locations and sizes of CBs is to minimize the distribution losses in the operation and control of RDNs. Additionally, the effect of CBs is shown by the increased net savings, greater voltage stability, and improved voltage profile. A voltage stability index (VSI) was used in the optimization process to determine the predefined search space for CB locations, and a double-direction learning strategy (DLS) was then considered to optimize the locations and sizes while maintaining a balance between the exploration and exploitation phases. Three IEEE RDNs were used to simulate various EV load increase scenarios as well as typical loading situations. According to a comparison with the literature, the IPFA produced global optimum results, and the proposed CBs allocation approach demonstrated enhanced performance in RDNs under all scenarios of EV load growth. 2022, University of Kragujevac, Faculty of Science. All Rights Reserved. -
Enhancing Dimensional Geometry Casting using Computer Modeling
Sand casting method is used to produce many useful products for many applications. The aim of the study is to manufacture a product with excellent dimensional geometry is achieved in sand casting process at low cost. We would expect manuscripts to show how design and/or manufacturing problems have been solved using computer modeling, simulation and analysis. In this work, the important mechanical properties of hardness and surface roughness are investigated on Aluminum 6063 cast material with and without incorporating the copper tubes as a vent hole in sand casting process. Since copper has high thermal conductivity when compared to other metals, the heat transfer rate will be varying from existing system. The copper tubes have made different diameters of holes on outer surfaces with selective distance of intervals. The specific number of copper tubes with various diameters are designed by CATIA modeling software and analyzed with Taguchi Design of Experiment. Taguchi L9 orthogonal array is used proficiently in the optimal value of hardness and surface roughness. The results are revealed that the maximum hardness value of 104 BHN is attained for 10mm distance of holes made on copper tube with an angle of 90o degree. The minimum surface roughness of 2.11 micron is achieved for 20mm distance of holes made on copper tube with 45o of angle as a vent hole in sand casting process. 2024 E3S Web of Conferences -
Epileptic Seizure Prediction from EEG Signals Using DenseNet
Epilepsy is a disorder in which the normal electrical pattern in the brain is disrupted causing seizures or loss of consciousness. Seizure is harmful during various events like swimming or driving. The electroencephalogram (EEG) is the measurement of electrical activity received from the nerve cells of the cerebral cortex. Forthcoming seizures can be predicted from scalp EEG signal to improve the quality of life. The study proposes a method of automatic epileptic seizure prediction from raw EEG signal. The raw EEG signal is converted into EEG signal image for automatic extraction of features and classification of inter-ictal and pre-ictal state using Dense Convolutional Network (DenseNet). This classification process is carried out in a manner similar to the process followed by a medical practitioner without resorting to hand-crafted features. The public CHB-MIT EEG database is used for training, validation, and testing. An EEG signal for 1 second duration is taken as one sample. The accuracy for the classification of inter-ictal and pre-ictal state is achieved up to 94% by using 5-Fold cross validation. However, the accuracy is not up to the mark for the presence of common artifacts caused by eye-blinking and muscle activities during EEG recordings. Hence, a 30 seconds pool based technique is used for decision on correct state identification. The proposed pool based technique provides an average specificity of 95.87% and a false prediction rate of 0.0413/hour. It also provide average sensitivities of 100%, 97%, and 90% for the time slots 0 - 5 minutes, 5 - 10 minutes, and 10 - 15 minutes before the seizure event. 2019 IEEE. -
Handwritten digit recognition using convolutional neural networks
Optical character recognition (OCR) systems have been used for extraction of text contained in scanned documents or images. This system consists of two steps: character detection and recognition. One classification algorithm is required for character recognition by their features. Character can be recognized using neural networks. The multilayer perceptron (MLP) provides acceptable recognition accuracy for character classification. Moreover, the convolutional neural network (CNN) and the recurrent neural network (RNN) are providing character recognition with high accuracy. MLP, RNN, and CNN may suffer from the large amount of computation in the training phase. MLP solves different types of problems with good accuracy but it takes huge amount of time due to its dense network connection. RNNs are suitable for sequence data, while CNNs are suitable for spatial data. In this chapter, a CNN is implemented for recognition of digits from MNIST database and a comparative study is established between MLP, RNN, and CNN. The CNN provides the higher accuracy for digit recognition and takes lowest amount of time for training the system with respect to MLP and RNN. The CNN gives better result with accuracy up to 98.92% as the MNIST digit dataset is used, which is spatial data. 2020 Walter de Gruyter GmbH, Berlin/Boston. All rights reserved. -
Phytochemicals as weapons against drug resistance
Phytochemicals are plant-based products with high medicinal value. These metabolites effectively target disease-causing microbes. Drug-resistant pathogens have developed mechanisms to sustain themselves even with inhibitors. Drug resistance has emerged as a global giant, causing all available treatment options to fail. The solution to this problem is in the phytochemicals of plants with antibacterial and drug resistance modulation properties. Phytochemicals might be able to get rid of efflux pumps, drug-modulating enzymes, resistance genes, quorum sensing, and biofilm, all of which cause pathogens to be resistant to drugs. Moreover, anti-obesogenic and cardioprotective properties are also observed in phytochemicals. Additionally, studies show the success of phytochemical-based nanoparticles in drug resistance regulation. This review emphasizes phytochemicals' different mechanisms of action and their derivatives in curbing drug-resistant pathogens and cancer cells. 2023 The Author(s) -
Pedestrian crossing behaviour between transport terminals
Pedestrians possess special requirements for protecting their privacy while interacting with other users of a transport network. There exists a need in order to obtain a deeper knowledge of pedestrian traffic behaviour in between transport terminals. When different transportation terminals come closer, there will be an increased pedestrian flow caused due to change in modes used. The main aim was to analyses the general pedestrian behaviour while crossing a road and to find out different human factors which affect this behaviour. The crossing patterns were observed and also the chances of conflict with vehicles. The paper brought out the fact that the pedestrians always preferred different types of crossings. These varied with the gender and age of the pedestrians and also with luggage carrying or not. There seemed to be a greater flow of pedestrians during the peak hours and then they faced difficulty in crossing due to heavy traffic. Crosswalks are locations in which pedestrians are exposed to fewer rights of accident prevention even though they may approach the roadway and be alert of approaching traffic. Pedestrian unlawful crossing attitude is a crucial factor inside area issue of safety on the road. Thus, there is a requirement to take more steps towards bringing safety. 2023 Author(s). -
SR-Mine: Adaptive Transaction Compression Method for Frequent Itemsets Mining
Extraction of frequent itemsets is a key step in association rule mining. Frequent Pattern (FP) mining from a very large dataset is still a challenging research problem. The basic frequent itemset algorithms are Apriori and FP-growth. FP-growth uses Frequent Pattern Tree (FP-tree) to store the database information in a compressed form. A large number of research papers have been proposed as an improvement of the basic frequent itemset mining algorithms. Several researchers have proposed modifications to existing data structures as well as new data structures to improve the mining process. A new method, Size Reduced Mining (SR-Mine), is proposed to speed up the FP-tree creation. The proposed work is implemented with the basic FP-growth algorithm and with the other two recent algorithms based on FP-tree. The three modified algorithms have been tested with standard datasets and compared with the original algorithms. The proposed method can be applied with the frequent itemset mining algorithms which consider each transaction one by one to construct a data structure for mining. The experimental results show that the proposed method can improve the performance of the mining. 2021, King Fahd University of Petroleum & Minerals. -
An improved frequent pattern tree: the child structured frequent pattern tree CSFP-tree
Frequent itemsets are itemsets that occur frequently in a dataset. Frequent itemset mining extracts specific itemsets with supports higher than or equal to a minimum support threshold. Many mining methods have been proposed but Apriori and FP-growth are still regarded as two prominent algorithms. The performance of the frequent itemset mining depends on many factors; one of them is searching the nodes while constructing the tree. This paper introduces a new prefix-tree structure called child structured frequent pattern tree (CSFP-tree), an FP-tree attached with a child search subtree to each node. The experimental results reveal that the CSFP-tree is superior to the FP-tree and its new variations for any kind of datasets. 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature. -
Parallelization of frequent itemset mining methods with fp-tree: An experiment with prepost+ algorithm
Parallel processing has turn to be a common programming practice because of its efficiency and thus becomes an interesting field for researchers. With the introduction of multi-core processors as well as general purpose graphics processing units, parallel programming has become affordable. This leads to the parallelization of many of the complex data processing algorithms including algorithms in data mining. In this paper, a study on parallel PrePost+ is presented. PrePost+ is an efficient frequent itemset mining algorithm. The algorithm has been modified as a parallel algorithm and the obtained result is compared with the result of sequential PrePost+ algorithm. 2021, Zarka Private University. All rights reserved. -
A perspective analysis of emotional appeal used in television advertising /
The purpose of the study is to find out whether emotional appeal is still prevailing in television advertising. The researcher focuses on the various elements used by advertisers to evoke emotional response on the audience‟s side. The advertisements decided by the researcher portray important relationships that are valued and maintained in the society. -
The Problem of Perception in Sandor Mais Embers: An Advaitic Study
This article attempts to study the problem of perception in Sandor Mais celebrated novel Embers from the standpoint of the pramana (a method of knowledge) of Advaita Vedanta. An epistemic problem, the problem of perception, concerns the overwhelming questions of life, culminating in an enigmatic amalgamation of dilemmas and paradoxes. Genuine dilemmas and paradoxes problematize human relationships, which is evident in the complex narrative of Embers. Our contention in this article is to show how, even though enacted within the periphery of the purely fictional, Embers bears testimony to the complexities of life that are quickened by the limits of human perception, which keeps one from seeing how things really are by creating a shadow or reflected consciousness. Set against the backdrop of the Austro-Hungarian Empire, the novel opens up a dialogic space at the intersection of a triangular relationship enacted on the threshold of perception and its multidimensional problems. 2024 Management Centre for Human Values. -
On the Way to Oneself : An Existential Study of the Select Plays of Sreeja K V and Sajitha Madathil
The perennial inquiries into human identity and the purpose of existence persist as enduring mysteries, often evoking a sense of introspection and existential angst. Amidst the quest for elucidation, individuals frequently find themselves entangled in the web of maya (appearance), wherein perceptions of reality become distorted, leading to emotional responses including jealousy, greed, guilt, and disappointment. However, amidst this labyrinth of existence, philosophical frameworks such as Existentialism and Advaita Vedanta offer invaluable lenses through which to perceive and engage with these existential inquiries. Existentialism prompts individuals to confront the subjective nature of their existence and assert autonomy in defining their identities and purpose. In contrast, Advaita Vedanta seeks to transcend the illusory veil of ego and perceive the ultimate reality of the Self (atman) as indistinguishable from the eternal consciousness (Brahman). Through the exploration of these philosophical paradigms, one can embark on a journey of Self- discovery, ultimately unveiling insights into the timeless questions of human existence. It is possible to identify this kind of crisis in the lives of the characters in the selected plays of Sreeja K V and Sajitha Madathil. Therefore, this thesis examines the selected plays of twenty-first-century Malayalam playwrights Sreeja K V and Sajitha Madathil through the lens of Simone de Beauvoir's existentialism and the pramana of Advaita Vedanta. It aims to explore the concept of the Self and how certain circumstances and experiences contribute to its realisation. By analysing the protagonists of these plays, the thesis seeks to uncover the notion that the Self is not merely a product of causality but rather the observer and creator of existence itself. This investigation raises further questions regarding the manifestation of the Self in one's life and the potential for misconceptions about its nature. The plays provide insights into the interaction between worldly illusions and the true essence of the Self, prompting consideration of how individuals often conflate these realms and succumb to materialistic temptations. Additionally, the thesis explores whether negative experiences are transient and whether individuals ultimately learn to overcome them. The selected plays open up the scope to understand the interplay of illusions of the world and the Self. This exploration leads to further questions like how does the Self appear in ones life? Is the Self mistaken? How often do people superimpose these two together and fall prey to the materialistic aspects? Is it true that the negative experiences are momentary, and often, one learns to survive from those experiences? Through the application of analytical frameworks of Existentialism and Advaita Vedanta into the select plays, this research endeavours to provide insights into these inquiries. -
Graphene-metal oxide composite materials for supercapacitor applications
Recently, supercapacitors have emerged as one of the potential candidates for electrochemical energy storage applications owing to their excellent capacity properties, high power density, appreciable cyclic stability, and environmentally benign nature. Graphene has paved the way as a supercapacitor electrode because of its exceptional attributes, including its conductivity, and mechanical and electrical properties. The efficiency of supercapacitors has been significantly impeded by the aggregation of graphene layers brought on by the considerable van der Waals attractions. Numerous methods have been developed to get over the limitations and make graphene a prime choice for supercapacitors. It is anticipated that combining graphene with metal oxides will improve its capacitive properties due to the mutual contribution of the individual components. In this chapter, various synthetic methods for graphene-metal oxide-based binary and ternary composites, as well as their application as supercapacitor electrodes, are explained in detail. The current research directions and future scope of graphene-metal oxide-based composites for supercapacitor application are also included. The Royal Society of Chemistry 2025. -
Cobalt oxide intercalated graphitic carbon nitride- polyaniline hybrid architecture for supercapacitors
In this study, a graphitic carbon nitride/Cobalt oxide/Polyaniline (g-C3N4/Co3O4/PANI) ternary nanocomposite was synthesized through an integrated approach combining a simple hydrothermal method with in-situ oxidative polymerization. The binary g-C3N4/Co3O4 and g-C3N4/PANI hybrid composites were also synthesized to elucidate the synergistic effects of the individual components. The structural and morphological analysis confirms the successful formation of binary and ternary composites. The porous architecture of g-C3N4/Co3O4/PANI nanocomposite synergistically combines the pseudocapacitive contributions of Co3O4, the conductive pathways of PANI, and the stabilizing role of g-C3N4, resulting in enhanced surface accessibility and improved electrolyte wettability. Strong interfacial interactions, including ?-? conjugation between g-C3N4 and PANI with Co3O4 induced electrostatic stabilization, ensuring considerable mechanical robustness. Electrochemical assessments reveal that the g-C3N4/Co3O4/PANI composite showcased a remarkable specific capacitance of 1152 F g?1 and 93 % capacitance retention over 5000 galvanostatic charge-discharge cycles. The configured asymmetric supercapacitor (g-C3N4/Co3O4/PANI//activated carbon) delivers superior energy and power densities of 59.1 Wh kg?1 and 2693 W kg?1, respectively. The developed nanocomposite represents a significant advancement in hybrid electrode materials, offering substantial potential for next-generation high-performance energy storage systems. 2025 Elsevier Ltd -
Enhancing the electrochemical performance of rGO-based ternary composite for next generation supercapacitors
This work explores the rational design and synthesis of a high-performance ternary nanocomposite rGO/CeO2/PPy, by incorporating cerium oxide and polypyrrole into the rGO matrix, through a hybrid approach of combining hydrothermal synthesis with in situ oxidative polymerization. Comprehensive structural characterization of the rGO/CeO2/PPy composite confirms the successful integration of components, revealing a hierarchically porous architecture that optimizes both charge transport and ion diffusion kinetics. The ternary composite exhibits exceptional interfacial interactions, including ?-? conjugation between rGO and PPy, coupled with electrostatic stabilization from CeO2, resulting in enhanced mechanical integrity and improved electrolyte accessibility. Electrochemical characterization reveals remarkable performance metrics, with a specific capacitance of 874 F g?1 and outstanding cyclic durability of 94% capacity retention after 5000 charge-discharge cycles at 1 A g?1. The configured rGO/CeO2/PPy//AC system exhibits exceptional energy storage performance, yielding an energy density of 39.6 Wh kg?1 while sustaining a power density of 2859 W kg?1. These outstanding characteristics underscore the material's suitability as a cutting-edge electrode for sophisticated energy storage systems, showcasing the benefits of strategic component integration in hybrid nanocomposite design. 2025 The Royal Society of Chemistry. -
Improved tomato (Solanum lycopersicum L.) growth and reduction of fungal pathogens utilising the plant growth-promoting and antifungal Bacillus albus NJ01 as a bioinoculant
Rhizobacteria that promote plant growth are crucial for improving the health, growth, and yield of plants. In this study, 14 isolates were obtained and the significance of Bacillus albus NJ01 as PGPR for the improvement of growth in tomato (Solanum lycopersicum) was assessed, as it showed plant growth-promoting traits like IAA, siderophores and ammonia production, phosphate and zinc solubilization, etc. Its role in increasing crop root and shoot length while avoiding the use of chemical pesticides and fertilizers was also studied. The root length of tomato control plants and plants treated with bioinoculant was found to be 5.58 0.15 and 7.98 0.24 cm, respectively. The shoot length of control plants and plants treated with bioinoculant was found to be 8.25 0.82 and 10.24 0.11 cm, respectively, therefore confirming the potentiality of Bacillus albus NJ01 bioinoculant as an able PGPR for improving the growth of tomato. 2025, Society for Advancement of Horticulture. All rights reserved. -
Improved tomato (Solanum lycopersicum L.) growth and reduction of fungal pathogens utilising the plant growth-promoting and antifungal Bacillus albus NJ01 as a bioinoculant
Rhizobacteria that promote plant growth are crucial for improving the health, growth, and yield of plants. In this study, 14 isolates were obtained and the significance of Bacillus albus NJ01 as PGPR for the improvement of growth in tomato (Solanum lycopersicum) was assessed, as it showed plant growth-promoting traits like IAA, siderophores and ammonia production, phosphate and zinc solubilization, etc. Its role in increasing crop root and shoot length while avoiding the use of chemical pesticides and fertilizers was also studied. The root length of tomato control plants and plants treated with bioinoculant was found to be 5.58 0.15 and 7.98 0.24 cm, respectively. The shoot length of control plants and plants treated with bioinoculant was found to be 8.25 0.82 and 10.24 0.11 cm, respectively, therefore confirming the potentiality of Bacillus albus NJ01 bioinoculant as an able PGPR for improving the growth of tomato. 2025, Society for Advancement of Horticulture. All rights reserved. -
DeepRetina: Transformer-Enhanced EfficientNet for Retinal Disease Classification
Retinal diseases are a major cause of visual impairment in India, which requires precise and automated diagnosis tools.This paper, introduce a two-phase deep learning architecture for classifying five common retinal ailments: Glaucoma, Normal Fundus, Pathological Myopia, Hypertensive Retinopathy, and Cataract. A Swin Transformer (Swin-T) was fine-tuned on augmented retinal fundus images in the first phase to extract domain-adapted feature representations. The transformer utilize such embeddings for learning a regularized EfficientNet-inspired classifier in the second phase, with mixup augmentation and label smoothing for improving generalizability. Comprehensive experiments conducted on a carefully curated dataset of 643 test images validate that our method attains a test accuracy of 93.93%, with high precision as well as recall across all categories. The suggested pipeline strikes a suitable balance between feature abundance with transformer-based adaptation and resilient classification with EfficientNet, providing a viable tool for automated diagnosis of retinal ailments in practical clinical scenarios. 2026 IEEE. -
Improving the Accuracy of Cardiovascular Disease Classification Using CardioAugmentNet Technique
Cardiovascular disease is the leading cause of death and mortality worldwide. Thus, early diagnosis of CVDs is crucial since the disease can be managed with optimal care. In the current study, we consider CardioAugmentNet, which is a CNN model augmented with data augmentation strategies for the classification of several cardiovascular pathologies in ECG images. A proposed method was designed to provide a robust algorithm for the detection of irregular heart rhythms, myocardial infarction and other cardiac diseases. The model is trained and tested on the dataset of ECG images from individuals with various prevalent cardiovascular diseases as well as normal hearts. Therefore, the CardioAugmentNet state-of-the-art model classifies different cardiac abnormalities with high accuracy, suggesting that it can be used in clinical practice. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.