Browsing by Author "Shetty, S."

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A (p, q)-graph G = (V, E) is said to be (k, d)-arithmetic, where k and d are positive integers if its p vertices admits a labeling of distinct non-negative integers such that the values of the edges obtained as the sums of the labels of their end vertices form the set {k, k + d, k + 2d, ..., k + (q - 1)d}. In this paper we prove that for all odd n, the generalized web graph W (t, n) and some cycle related graphs are (k, d)-arithmetic. Also we prove that a class of trees called Tp-trees and subdivision of Tp-trees are (k + q - 1) (d, d)-arithmetic for all positive integers k and d.
(On arithmetic graphs) Hegde, S.M.; Shetty, S.
2002
A (p, q)-graph G = (V, E) is said to be super edge-magic if there exists a bijection f fromV ? E to {1, 2, 3,..., p + q } with vertices maps to {1, 2, 3,..., p} such that for all edges uv of G, f(u) + f(v) + f(uv) is a constant and bijection so denned is called a super edge- magic labeling of G, For any super edge-magic labeling of G, there is a constant c(f) such that for all edges uv of G, f(u) + f(v) + f(uv) = c(f) and its range is p + q + 3 ? c(f) ? 3p. In this paper we study super edge-magic graphs with constant c(f) = p+q +3 for at least one f and such graphs are denned as super edge least-magic(SEL-magic) graphs. We investigate the following general results on the structure of SEL-magic graphs including a result, which determines all the regular SEL-magic graphs. (1) A SEL-magic graph is either a forest with exactly one nontrivial component, which is a star or has a triangle. (2) If an eulerian (p,q)-graph G = (V, E) is SEL-magic then q ? 0, 3(mod4). (3) The minimum vertex degree ? of any SEL-monograph is at most 3. (4) There are exactly three nontrivial regular graphs K2,K3 and K2 × K3 which are SEL-magic. Also we define level joined planar grid graph L J : P m × P n and prove that it is SEL-magic. Also we give a general method of constructing new SEL-magic graphs from any given SEL-magic graph. © 2005 Elsevier Ltd. All rights reserved.
(Super Edge Least-Magic Graphs) Hegde, S.M.; Shetty, S.
2003
A (p, q)-graph G = (V,E) is said to be (k, d)-graceful, where k and d are positive integers, if its p vertices admits an assignment of a labeling of numbers 0, 1, 2, ..., k + (q - 1)d such that the values on the edges defined as the absolute difference of the labels of their end vertices form the set {k, k + d, ..., k + (q - 1)d}. In this paper we prove that a class of trees called TP-trees and subdivision of TP- trees are (k, d)-graceful for all positive integers k and d.
(On graceful trees) Hegde, S.M.; Shetty, S.
2002
A (p, q)-graph G = (V,E) is said to be magic if there exists a bijection f: V ? E ? {1, 2, 3,..., p + q} such that for all edges uv of G, f(u) + f(v) + f(uv) is a constant. The minimum of all constants say, m(G), where the minimum is taken over all such bijections of a magic graph G, is called the magic strength of G. In this paper we define the maximum of all constants say, M(G), analogous to m(G), and introduce strong magic, ideal magic, weak magic labelings, and prove that some known classes of graphs admit such labelings.
(On magic graphs) Hegde, S.M.; Shetty, S.
2003
A systematic analysis on the electrospinnability of biocompatible poly(butylene adipate-co-terephthalate)
(Institute of Physics, 2025) Das, A.; Anandhan, S.; Chethan, K.N.; Salins, S.S.; Shetty, R.; Shetty, S.
Fine-tuning electrospun nanofibers is crucial for producing high-quality fibers. Taguchi Design of Experiment (DOE), along with various other computational techniques, has been used to optimize the electrospinning parameters of different polymers. Taguchi DOE has proven effective in optimizing electrospun nanofibers because it reduces the number of trials needed. In this study, the electrospinning parameters of poly (butylene adipate-co-terephthalate) (PBAT) were optimized and quantified using the Taguchi-based Response Surface Methodology (RSM) approach. The average fiber diameters were measured from Field Emission Scanning Electron Microscopy (FESEM) images using ImageJ software. Within the tested range of parameters and levels, the Analysis of Variance (ANOVA) study identified polymer concentration and flow rate as the most significant factors that influenced the fiber diameter. Polymer concentration accounting 56.94% of the variation, while Flow Rate (FR) accounts for 20.82%. The optimal parameter levels were predicted to be 10 wt% polymer concentration, 1 ml h?1 flow rate, 18 kV voltage, and a distance from tip to target of 15 cm, which yielded fibers with an average diameter of 231 nm and an accuracy of 88.61%. Overall, the results demonstrate that Taguchi DOE, coupled with RSM, is a reliable and efficient method for identifying the optimal parameter combinations to produce uniform, fine PBAT nanofibers intended for biomedical applications. © 2025 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.
An exploratory analysis of rainfall: A case study on western ghats of India
(IEOM Society ieom-society@iieom.org, 2018) Rao, P.S.B.; Shetty, S.; Umesh, P.; Shetty, A.
In this study high resolution 0.250 Ã—0.250 (approximately 25KmÃ—25Km) gridded daily rainfall data is used to analyze the effect of changing climate on distribution of rainfall in different topographical zones of Western Ghats (WG) of India over the period 1901-2013. The non parametric two tailed Mann- Kendall with Hamed and Rao's method of autocorrelation and Sen's slope estimator for obtaining magnitude of change over time period is used. The rainfall trend in annual, monsoon and post-monsoon is increasing in state of Goa and Coastal region of Karnataka state and significantly decreasing in some part of Kerala and Maharashtra state. Winter season rainfall has seen a declined trend in southern part of the study area and in high elevated region of Kerala state. Even the mean rainfall over the study area is declining from 1951-1960 with disturbance in alternate sequence of flood and drought year from period 1990. The frequency of heavy rainfall events (65mm-124.4mm) are increasing in recent decades with 40-50% contribution from 2000-2013 in regions of Maharashtra state. The trend of heavy rainfall events are increasing in West Coast of India at 5% significance level with no trend in very heavy to extreme rainfall events (>124.5mm). Â© IEOM Society International.
Analysis and Prediction of Fantasy Cricket Contest Winners Using Machine Learning Techniques
(Springer Science and Business Media Deutschland GmbH info@springer-sbm.com, 2021) Karthik, K.; S. Krishnan, G.S.; Shetty, S.; Bankapur, S.; Kolkar, R.; Ashwin, T.S.; Vanahalli, M.K.
Cricket is one of the well-known sports across the world. The increasing interest of cricket in recent years resulted in different forms like T20, T10 from test and one day format. The craze of all these formats of cricket matches today has come into online fantasy cricket league games. Dream11 is one such app that is most popular in this context, along with many similar apps. Creating a dream team of 11 players from playing 11 of both teams involves skills, ideas and luck. Predicting a winner among all the joined contestants based on the previous historical data is a challenging task. In this paper, we used a feed-forward deep neural network (DNN) classifier for predicting the winning contestant for the top three positions in a fantasy league cricket contest. The performance of the DNN approach was compared against that of state-of-the-art machine learning approaches like k-nearest neighbours (KNN), logistic regression (LR), Naive Bayes (NB), random forest (RF), support vector machines (SVM) and in predicting the fantasy cricket contest winners. Among the methods used, DNN showed the best results for all three positions, showing its consistency in predicting the winners and outperforms the state-of-the-art machine learning classifiers by 13%, 8% and 9%, respectively, for first, second and third winning positions, respectively. Â© 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Autism Spectrum Disorder Detection Using Machine Learning Techniques
(Springer Science and Business Media Deutschland GmbH, 2025) Shetty, S.; Shetty, S.; Saranya, P.
A developmental disease called autism spectrum disorder (ASD) greatly reduces a patient's capacity for social interaction and communication in everyday situations. Using various machine learning strategies, necessitating (KNN)-K Nearest Neighbors, (LR)-Logistic Regression, (DT)-Decision Tree Classifier, (RF)-Random Forest Classifier, and (SVM)-Support Vector Machine. Using data taken from potential ASD patientsâ€™ medical records, a strong machine learning-driven strategy for autism detection is established. The ASD dataset, which is accessible to the public, is used to assess the suggested methods. There are 800 cases and 22 distinct attributes in the ASD screening dataset. The framework involves data collection, data visualization, data preprocessing, and implementation of machine learning model. A machine learning-based ASD detection system using logistic regression aims to determine if an individual has ASD or not in accordance with relevant features and behavioral patterns. Testing results are evaluated based on the performance metrics and the proposed system utilizes Logistic Regression which yields 0.85 accuracy, 0.78 precision, 0.76 recall, and 0.77 F1 score following comparison with the other models of machine learning. The suggested framework for ASD detection greatly streamlines and expedites the process of diagnosing ASDs. Â© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Climate indices and drought characteristics in the river catchments of Western Ghats of India
(Springer Science and Business Media Deutschland GmbH, 2024) Shetty, S.; Umesh, P.; Shetty, A.
The study addresses the long-term trend in rainfall, minimum and maximum temperature, and the climate indices for the river catchments located in the diverse climate of the Western Ghats of India. The dry sub-humid Chaliyar catchment and humid Kajvi catchment have shown a dramatic change in the decadal rainfall, with the decade 1950–1960 being the point of change. The monsoon rainfall has decreased in the Chaliyar and Netravati catchments and increased insignificantly in the Kajvi catchment. With the increase in mean temperature, the number of rainy days is decreasing, and intense rainfall is increasing in the pre-monsoon. The increase in minimum temperature is more severe in all three catchments, irrespective of the region’s climate. The decline in rainy days is more figurative in the humid and per-humid catchments and has seen a 16–20% decrease in R×1 day, R×3 day, and R×5 day in the past six decades with an insignificant increase in the dry sub-humid catchment. The frightful increase in warm days/nights with a decrease in cool days/nights has been alarming for the extremity of temperature in future years. The significant changes in the forest area in Chaliyar and Kajvi catchment and the increase in a built-up area in Netravati may have a decisive role in the nonseasonal variability in rainfall and temperature along with increasing greenhouse gases. In the case of meteorological drought studied using the Standardized Precipitation Index (SPI), moderate droughts have occurred over the Chaliyar and Kajvi, and extreme droughts over the Netravati catchments with no reduction in the frequency or severity of short-duration extreme rainfall events. The geographical location of the catchment has a greater impact on the characteristics of the rainfall and meteorological drought, and these changes in the hydrological regimes of the catchment have a significant bearing on the water availability in the catchments in the future years. © The Author(s) under exclusive licence to Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences 2023.
Combinatorial labelings of graphs
(2006) Hegde, S.M.; Shetty, S.
A (p, g)-graph G is said to be a permutation (combination) graph if G admits an assignment of distinct integers 1, 2, 3, ..., p to the vertices such that edge values obtained by the number of permutations (combinations) of larger vertex value taken smaller vertex value at a time are distinct. In this paper we obtain a necessary condition for combination graph and study structure of permutation and combination graphs which includes some open problems.
Comprehensive Review of Multimodal Medical Data Analysis: Open Issues and Future Research Directions
(Prague University of Economics and Business, 2022) Shetty, S.; Ananthanarayana, V.S.; Mahale, A.
Over the past few decades, the enormous expansion of medical data has led to searching for ways of data analysis in smart healthcare systems. Acquisition of data from pictures, archives, communication systems, electronic health records, online documents, radiology reports and clinical records of different styles with specific numerical information has given rise to the concept of multimodality and the need for machine learning and deep learning techniques in the analysis of the healthcare system. Medical data play a vital role in medical education and diagnosis; determining dependency between distinct modalities is essential. This paper gives a gist of current radiology medical data analysis techniques and their various approaches and frameworks for representation and classification. A brief outline of the existing medical multimodal data processing work is presented. The main objective of this study is to spot gaps in the surveyed area and list future tasks and challenges in radiology. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (or PRISMA) guidelines were incorporated in this study for effective article search and to investigate several relevant scientific publications. The systematic review was carried out on multimodal medical data analysis and highlighted advantages, limitations and strategies. The inherent benefit of multimodality in the medical domain powered with artificial intelligence has a significant impact on the performance of the disease diagnosis frameworks. © 2022 by the author(s).
Corrosion inhibiting action of Ni-Mo alloy coatings in the presence of mixed metal oxide nanocomposites
(Royal Society of Chemistry, 2018) Goveas, J.J.; Shetty, S.; Mascarenhas, N.P.; Hegde, A.; Gonsalves, R.A.
This paper is an attempt to establish the role of mixed metal oxide nanoparticles of ZnO-SnO2 (ZTO), ZnO-WO3 (ZWO) and ZnO-TiO2 (ZTiO) for enhancement of the corrosion inhibiting action of Ni-Mo alloy coatings on a copper substrate. Binary Ni-Mo alloy coatings were electrodeposited on copper plates in the presence and absence of nanoparticles from an alkaline citrate bath. The nanoparticles were previously synthesized via an electrochemical thermal technique. The corrosion resistance of these alloy coatings was evaluated by electrochemical impedance spectroscopy (EIS) and potentiodynamic polarisation techniques in 3.5% NaCl medium. The experimental results reveal that the corrosion inhibiting capacity of the coating is best enhanced in the presence of ZWO nanoparticles deposited at the optimum current density (c.d.). Changes in surface morphology, phase structure and composition were analysed using SEM, XRD, and EDX. It was observed that alloy coatings reinforced with nanoparticles possessed a much smoother surface microstructure which could result in superior corrosion resistance. © The Royal Society of Chemistry and the Centre National de la Recherche Scientifique.
Cross-modal Deep Learning-based Clinical Recommendation System for Radiology Report Generation from Chest X-rays
(Materials and Energy Research Center, 2023) Shetty, S.; Ananthanarayana, V.S.; Mahale, A.
Radiology report generation is a critical task for radiologists, and automating the process can significantly simplify their workload. However, creating accurate and reliable radiology reports requires radiologists to have sufficient experience and time to review medical images. Unfortunately, many radiology reports end with ambiguous conclusions, resulting in additional testing and diagnostic procedures for patients. To address this, we proposed an encoder-decoder-based deep learning framework that utilizes chest X-ray images to produce diagnostic radiology reports. In our study, we have introduced a novel text modelling and visual feature extraction strategy as part of our proposed encoder-decoder-based deep learning framework. Our approach aims to extract essential visual and textual information from chest X-ray images to generate more accurate and reliable radiology reports. Additionally, we have developed a dynamic web portal that accepts chest X-rays as input and generates a radiology report as output. We conducted an extensive analysis of our model and compared its performance with other state-of-the-art deep learning approaches. Our findings indicate significant improvement achieved by our proposed model compared to existing models, as evidenced by the higher BLEU scores (BLEU1 = 0.588, BLEU2 = 0.4325, BLEU3 = 0.4017, BLEU4 = 0.3860) attained on the Indiana University Dataset. These results underscore the potential of our deep learning framework to enhance the accuracy and reliability of radiology reports, leading to more efficient and effective medical treatment. © 2023 Materials and Energy Research Center. All rights reserved.
Data Augmentation vs. Synthetic Data Generation: An Empirical Evaluation for Enhancing Radiology Image Classification
(Institute of Electrical and Electronics Engineers Inc., 2023) Shetty, S.; Ananthanarayana, V.S.; Mahale, A.
Radiology is a field of medicine dealing with diagnostic images to detect diseases for further treatment. Collecting and annotating diagnostic images like Magnetic Resonance Imaging (MRI) and X-Ray is a rigorous and time-consuming process. Deep Learning methods are widely utilized for disease classification and prediction from diagnostic images, but they demand substantial amounts of training data. Additionally, certain diseases are uncommon in large patient cohorts, posing difficulties in obtaining sufficient imaging samples to construct accurate deep learning models. Data augmentation techniques are commonly used to overcome this challenge of limited data. These techniques involve applying geometric transformations such as rotation, cropping, zooming, flipping, and other similar operations to the images to enlarge the dataset artificially. Another possible way of expanding the dataset is by synthesizing data to generate artificial medical images by mimicking the original images. This study presents RAD-DCGAN: A Deep Convolutional Generative Adversarial Network to produce high-resolution synthetic radiology images from the X-ray and MRI images collected from a private medical hospital (KMC Hospital, India). We aim to determine the most effective technique for enhancing the performance of radiology image classifiers by comparing and evaluating the proposed RAD-DCGAN with the standard data augmentation strategy. Our empirical evaluation, which involved eight standard deep learning models, demonstrated that deep learning classifiers trained on synthetic radiology data outperformed those trained on standard augmented data. The utilization of the RAD-DCGAN model for training and testing deep learning models on synthetic data has demonstrated a notable improvement of 4-5% in accuracy compared to conventional augmentation techniques. This signifies the state-of-the-art performance achieved by the RAD-DCGAN model in enhancing the accuracy of deep learning models. Â© 2023 IEEE.
Dependability of rainfall to topography and micro-climate: an observation using geographically weighted regression
(Springer, 2022) Shetty, S.; Umesh, P.; Shetty, A.
The dependability of rainfall to topography and micro-climate of the region in an eco-sensitive Western Ghats of India is evaluated using the geographically weighted regression method. The correlation between rainfall and topographical features, namely, elevation, slope, Terrain Ruggedness Index, topography, and distance from the coast/ridge, varies seasonally with consistent variation across the years. The Normalized Differential Vegetation Index and rainfall have an inverse relationship due to the adverse effect of high spell rainfall on vegetation growth in the monsoon season. The rainfall negatively correlates with maximum land surface temperature and conversely with a minimum land surface temperature in the windward side of the Ghats other than monsoon season. The connection between rainfall and other variables differs significantly throughout space, with vast differences on the mountain’s windward and leeward sides, as well as in the Ghats’ southern and northern regions. The effect of the terrain is amplified in the broad, gradually sloping intermediate rough mountain that is close to the coast. The maximum rainfall depends on the mountain’s steepness on the windward side; at isolated mountains, maximum rainfall occurs at an elevation range of 500–800 m and in cascaded mountain ranges at 800–1200 m along with the influence of other driving factors. Also, the control exerted by the ridge of the mountain on the rain-bearing wind is prominent until 120 km from the mountain ridge. These results are useful in understanding the reliance of rainfall on topographic and micro-climatic parameters and can be used in hydro-geological applications. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.
Development of a new flexible nanogenerator from electrospun nanofabric based on PVDF/talc nanosheet composites
(Royal Society of Chemistry, 2020) Shetty, S.; Mahendran, A.R.; Anandhan, S.
Herein, a flexible piezoelectric nanogenerator composed of electrospun talc/PVDF [poly(vinylidene fluoride)] nanocomposite fabrics has been developed. These nanocomposite fabrics demonstrated enhanced mechanical and piezoelectric properties compared with pristine PVDF nanofabrics. In particular, nanocomposite fabrics with 0.50 wt% talc yielded 89.6% of polar ?-phase in the PVDF matrix, thereby augmenting its piezoelectric response. X-ray diffraction, Fourier transform infrared spectroscopy, and differential scanning calorimetry conclusively affirmed the promotion of polar ?-phase in the talc/PVDF nanocomposite fabrics. The 0.50 wt% talc/PVDF nanocomposite fabric based nanogenerator produced an open-circuit voltage and power density of 9.1 V and 1.12 ?W cm-2, respectively, under repetitive finger tapping mode (under a load of 3.8 N). Furthermore, the nanogenerator was also subjected to frequency modulated-shaker mode, wherein an output voltage of 8.9 V was produced. Improved flexibility, mechanical robustness, and enhanced piezoelectric responsiveness of this nanogenerator could possibly pave the way for its use in portable self-powered devices. This journal is © 2020 The Royal Society of Chemistry.
Development of multilayer Sn-Ni alloy coating by pulsed sonoelectrolysis for enhanced corrosion protection
(Royal Society of Chemistry, 2016) Shetty, S.; Mohamed, M.J.; Bhat, D.; Hegde, A.C.
Multilayer Sn-Ni alloy coating has been developed electrochemically on mild steel using an ultrasound effect, as a tool to modulate mass transfer process at electrical double layer, during deposition. Sn-Ni coatings having alternate layers of alloys of different compositions were developed on a nano/micrometric scale by pulsing sonicator ON (tON) and OFF (tOFF), periodically. The composition modulated multilayer alloy (CMMA) Sn-Ni coatings have been deposited by inducing the ultrasound field periodically at optimal current density. Corrosion performances of ultrasound-assisted multilayer Sn-Ni alloy coatings have been evaluated by electrochemical methods. Corrosion data revealed that CMMA Sn-Ni coating, developed using pulsed ultrasonic field and having 150 layers, represented as (Sn-Ni)2/2/150, is the most corrosion resistant, compared to its monolayer alloy coatings developed by both with/without ultrasound effect. Corrosion protection efficacy of multilayer coatings was found to be decreased at high degree of layering due to diffusion of layers. Improved corrosion resistance of multilayer Sn-Ni coatings is attributed to an increase in the number of layers, or interfaces separating alloys of the same metals, but of different composition, surface morphologies and phase structures, supported by energy dispersive spectroscopy, field emission scanning electron microscopy and X-ray diffraction study, respectively. The better corrosion protection of CMMA Sn-Ni coatings, compared to monolayer counterparts, is attributed to an increase in the number of layers, hence phase boundaries between layers, and experimental results are discussed. © 2018 The Royal Society of Chemistry.
Diagnostic Performance Evaluation of Deep Learning-Based Medical Text Modelling to Predict Pulmonary Diseases from Unstructured Radiology Free-Text Reports
(Prague University of Economics and Business, 2023) Shetty, S.; Ananthanarayana, V.S.; Mahale, A.
The third most common cause of death worldwide is attributed to pulmonary diseases, making it imperative to diagnose them promptly. Radiology is a medical discipline that utilizes medical imaging to guide treatment. Radiologists prepare reports interpreting details and findings analysed from medical images. Radiology free-text reports are a rich source of textual information that can be exploited to enhance the efficacy of medical prognosis, treatment and research. Radiology reports exist in an unstructured format as are not suitable by themselves for mathematical computation or machine learning operations. Therefore, natural language processing (NLP) strategies are employed to convert unstructured natural language text into a structured format that can be fed into machine learning (ML) or deep learning (DL) models for information extraction. We propose a DL-based medical text modelling framework incorporating a knowledge base to predict pulmonary diseases from unstructured radiology free-text reports. We make detailed diagnostic performance evaluations of our proposed technique by comparing it with state-of-the-art NLP techniques on radiology free-text reports extracted from two medical institutions. The comprehensive analysis shows that the proposed model achieves superior results compared to existing state-of-the-art text modelling techniques. © 2023 Prague University of Economics and Business. All Rights Reserved.
Electrochemical synthesis of ZnO-WO3 nanocomposites and their photocatalytic activity
(Springer editorial@springerplus.com, 2020) Goveas, J.J.; Shetty, S.; Mascarenhas, N.P.; D’Souza, R.M.; Gonsalves, R.A.
Abstract: Heterometal oxide nanoparticles of ZnO-WO3 (ZWO) were synthesized using a facile dual step hybrid electrochemical–thermal technique. The role of surfactant additives during synthesis was investigated by introducing three different surfactants:Cetyltrimethyl ammonium bromide (Cetrimide), sodium dodecyl sulphate (SDS), and polyethylene glycol (PEG) into the electrolytic bath. X-ray diffraction and surface morphology studies indicate that the nanoparticles are cubic with an average crystallite size of 30–40 nm. Photocatalytic behaviour of these nanomaterials was tested using Methylene Blue (MB) and Eriochrome Black-T (EBT) as sample pollutants. The best results were observed for the photocatalyst generated in the presence of SDS as an additive and calcined at 650 ?C. High degree of decolourisation of both dyes resulting in complete mineralisation is due to the photocatalytic activity of ZWO which is greater than that of commercially obtained TiO2-P25 photocatalysts. This proves that the electrochemical synthetic route with its low cost and high efficiency is an excellent technique for the bulk synthesis of heterometal oxide photocatalysts which could effectively be used in effluent water treatment. Graphic Abstract: [Figure not available: see fulltext.]. © 2020, Springer Nature B.V.
Electrodeposition and characterization of Ni-Mo alloy as an electrocatalyst for alkaline water electrolysis
(Elsevier B.V., 2017) Shetty, S.; Mohamed, M.; Bhat, D.K.; Hegde, A.C.
This work details the efficiency of Ni-Mo alloy as an electrode for water splitting application through electrodeposition method. Nano-crystalline Ni-Mo alloy coatings were deposited in the current density (c.d.) range of 1.0–4.0 A dm? 2 on a copper substrate, and were investigated for their deposit characters, and their electrocatalytic behaviours in 1.0 M KOH solution. The electrocatalytic behaviour of the coatings, in terms of their hydrogen evolution reaction (HER) and oxygen evolution reaction (OER), were evaluated by electrochemical methods, like cyclic voltammetry (CV) and chronopotentiometry (CP). Experimental results revealed that Ni-Mo alloy electrodeposited at 1.0 A dm? 2 (38.3 wt% Mo) and 4.0 A dm? 2 (33.2 wt% Mo) shows the highest electrocatalytic tendency for HER and OER, respectively. The corrosion behaviour of Ni-Mo alloy coated at 4.0 A dm? 2 is found to be the most corrosion resistant in the same alkaline medium, compared to other coatings. The highest electrocatalytic activity of Ni-Mo alloy deposit for both HER and OER, depending on deposition c.d. was attributed to their composition (in terms of Ni and Mo content), structure and surface morphology; supported by EDXA, XRD, SEM and AFM analyses. The experimental study demonstrated that Ni-Mo alloy coatings follow Volmer-Tafel type of mechanism for HER, testified by Tafel slope analyses. © 2017 Elsevier B.V.