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Enhanced Sensing Performance of an Ammonia Gas Sensor Based on Ag-Decorated ZnO Nanorods / Polyaniline Nanocomposite
The development of low-cost ammonia sensors with high sensitivity and selectivity has gained considerable interest. Though the response of these sensors at room temperature is low and needs enhancement. In the present study high sensitivity ammonia gas sensors based on nanocomposite films of polyaniline (PANI) and with varying ZnO concentrations were synthesized and investigated. With a loading of 10 at% ZnO, the gas sensing response of 59 % was obtained for 120 ppm NH3 gas. The gas response was further enhanced by decorating the ZnO nanorods with different concentrations of silver (Ag) nanoparticles. The Ag-decorated ZnO nanorods were embedded in the PANi matrix using the in-situ oxidative polymerization technique. It was shown that PANi ZnO, p-n junction, and the introduction of porosity in nanocomposite act synergistically in increasing the resistance caused by the deprotonation of PANi by NH3. Among various compositions studied, 2 % loading of Ag in ZnO embedded in PANi matrix, thin films were found to be highly selective and sensitive towards NH3 gas at room temperature with a chemiresistive response of 70 % at 120 ppm and a recovery time of less than 120 s. The selectivity of the nanocomposite was also studied towards various reducing and oxidizing gasses. 2023 Wiley-VCH GmbH. -
Deterministic, Stochastic, and Deep Learning Approaches to Understand the Economic Fluctuations in India
In the present work, a new mathematical framework is proposed for studying the interrelation among population growth rate, GDP, inflation rate, and unemployment rate within deterministic and stochastic frameworks. The values of the parameters of the proposed model are estimated using real data from India. The local and global uniqueness of solutions is established for the stochastic model. The deterministic model is solved by using the Adams-Bashforth-Moulton predictor-corrector method, and Milstein's method is used for solving the stochastic model. Numerical simulations correlated quite strongly with observed data, while projections for the 20242030 period indicate that controlled population growth bodes well for the outlook of the economy for India, supporting economic prosperity alongside reduced inflation and better employment conditions. The findings presented in this work are correlational; therefore, to find the possible cause for this phenomenon, further research is required with detailed datasets. Comparing our model's GDP predictions with that obtained using a long short-term memory recurrent neural network model returned very high values of predictive accuracy, thus reinforcing the strength and reliability of our framework. 2025 John Wiley & Sons Ltd. -
Characterization of Erd?s matrices by their zero entries
An Erd?s matrix E is a bistochastic matrix whose sum of squares of entries (Frobenius norm squared) equals its maxtrace (maximum value of the trace of ?E, as ? varies over permutation matrices). We characterize all Erd?s E by the patterns of their zero entries; showing that each such skeleton has at most one E. We present an algorithm to find all n Erd?s matrices, which finds them up to n?5 quickly and also size n=6. We further show some presently known RCDS matrices (E in which the trace of ?E remains constant across all the permutations that avoid every zero-entry position in E) to be Erd?s. 2026 Elsevier Inc. -
Deterministic, Stochastic, and Deep Learning Approaches to Understand the Economic Fluctuations in India
In the present work, a new mathematical framework is proposed for studying the interrelation among population growth rate, GDP, inflation rate, and unemployment rate within deterministic and stochastic frameworks. The values of the parameters of the proposed model are estimated using real data from India. The local and global uniqueness of solutions is established for the stochastic model. The deterministic model is solved by using the Adams-Bashforth-Moulton predictor-corrector method, and Milstein's method is used for solving the stochastic model. Numerical simulations correlated quite strongly with observed data, while projections for the 20242030 period indicate that controlled population growth bodes well for the outlook of the economy for India, supporting economic prosperity alongside reduced inflation and better employment conditions. The findings presented in this work are correlational; therefore, to find the possible cause for this phenomenon, further research is required with detailed datasets. Comparing our model's GDP predictions with that obtained using a long short-term memory recurrent neural network model returned very high values of predictive accuracy, thus reinforcing the strength and reliability of our framework. 2025 John Wiley & Sons Ltd. -
Diabetes Mellitus Classification Using Machine Learning Algorithms with Hyperparameter Tuning
Diabetes Mellitus is a prevalent condition globally, marked by elevated blood sugar levels resulting from either insufficient production of insulin or the body cells' inability to respond appropriately to released insulin. For people with diabetes to lead healthy, normal lives, early identification and treatment of the condition are essential. With the need to move away from current traditional procedures, towards a noninvasive methodology, machine learning and data mining technologies can be very useful in the classification of diabetes. Creating an effective machine learning model for the classification of diabetes mellitus was the primary goal of this research. This work is primarily carried out on combined Pima Indian diabetes dataset and German Frankfurt diabetes dataset. The class imbalance issue has been resolved using Synthetic Minority Oversampling Technique. One-hot encoding is applied to convert categorial features to numerical and various single and ensemble classifiers with the best hyperparameters obtained using GridSearchCV method were employed on the pre-processed dataset. With an AUC of 0.98 and maximum accuracy of 98.79%, the Random Forest ensemble technique outperformed the other models, according to the experimental results. As a result, the algorithm might be used to predict diabetes and alert doctors to serious cases that call for emergency care. 2024 IEEE. -
A Comprehensive Review on Heart Disease Risk Prediction using Machine Learning and Deep Learning Algorithms
Cardiovascular diseases claim approximately 17.9 million lives annually, with heart attacks and strokes accounting for over 80% of these deaths. Key risk factors, including hypertension, hyperglycemia, dyslipidemia, and obesity, are identifiable, offering opportunities for timely intervention and reduced mortality. Early detection of heart disease enables individuals to adopt lifestyle changes or seek medical treatment. However, conventional diagnostic methods, such as electrocardiogramscommonly used in clinics and hospitals to detect abnormal heart rhythmsare not effective in identifying actual heart attacks. Additionally, angiography, while more precise, is an invasive method, financial strain on patients, and high chances of incorrect diagnosis, highlighting the need for alternative approaches. The main goal of this study was to assess the accuracy of machine learning techniques, including both individual and combined classifiers, in early detection of heart diseases. Furthermore, the study aims to highlight areas where additional research is necessary. Our investigation covers a decade period from 2014 to 2024, including a thorough review of pertinent literature from international conferences and top journals from the databases like Springer, ScienceDirect, IEEEXplore, Web of Science, PubMed, MDPI, Hindawi and so on. The following keywords were used to search the articles: heart disease risk, heart disease prediction, data mining, data preprocessing, machine learning algorithms, ensemble classifiers, deep learning algorithms, feature selection, hyperparameter optimization techniques. We examine the methodologies used and evaluate their effectiveness in predicting cardiovascular conditions. Our findings reveal notable progress in applying machine learning and deep learning in cardiology. The study concludes by proposing a framework that incorporates current machine learning techniques to enhance heart disease prediction. The Author(s) under exclusive licence to International Center for Numerical Methods in Engineering (CIMNE) 2024. -
Hybrid Deep Learning Cloud Intrusion Detection
The scalability and flexibility that cloud computing provides, organisations can readily adapt their resources to meet demand without having to make significant upfront expenditures in hardware infrastructure. Three main types of computing services are provided to people worldwide via the Internet. Increased performance and resource access are two benefits that come with using cloud computing, but there is also an increased chance of attack. As a result of this research, intrusion detection systems that can process massive amounts of data packets, analyse them, and produce reports using knowledge and behaviour analysis were created. Convolution Neural Network Algorithm encrypts data as it's being transmitted end-to-end and is stored in the cloud, providing an extra degree of security. Data protection in the cloud is improved by intrusion detection. This study uses a model to show how data is encrypted and decrypted, of an algorithm and describes the defences against attacks. When assessing the performance of the suggested system, it's critical to consider the time and memory needed to encrypt and decrypt big text files. Additionally, the security of the cloud has been investigated and contrasted with various encoding techniques now in use. 2024 IEEE. -
Investigation of Brain Tumor Recognition and Classification using Deep Learning in Medical Image Processing
A brain tumour is the growth of brain cells that are abnormal, some of which may progress into cancer. Magnetic Resonance Imaging (MRI) scans are the method used most frequently to detect brain tumours. The brain's abnormal tissue growth can be seen on the MRI images, which reveal. Deep learning and machine learning techniques are employed to identify brain tumours in a number of research publications. It only takes a very short amount of time to predict a brain tumour when these algorithms are applied to MRI images, and the increased accuracy makes patient treatment simpler. Thanks to these forecasts, the radiologist can make quick decisions. The suggested approach employs deep learning, a convolution neural network (CNN), an artificial neural network (ANN), a self-defined neural network, andthe existence of brain tumor. 2022 IEEE. -
A Shortest Path Problem for Drug Delivery Using Domination and Eccentricity
The concept of domination was first introduced in by Ore in 1962. With this, the study of domination gained importance and has been vigorously studied since then. The idea about eccentricity for vertices in a graph was given by Buckley and Harary in 1990. This paper combined the ideas about domination and eccentricity and provides the observation obtained during the study. Most of the basic ideas about domination and eccentricity has been covered and also a comparative study between these two has been stated along with problem of drug transportation through networks. These ideas can be further used to solve the real-world problems which uses concepts of domination and eccentricity like for example drug delivery game theory problems, routing problem, assignment problem and many more. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Development and evaluation of the bootstrap resampling technique based statistical prediction model for Covid-19 real time data : A data driven approach
The objective of the article is to develop earlyR package based novel coronavirus disease (COVID-19) forecasting model. The reported COVID-19 serial interval data is applied for obtaining maximum likelihood value of the reproduction number (R0) using maximum likelihood approach and projections package is applied for getting trajectories of epidemic curve. The minimum, median, mean and maximum projected value of R0 with 95% confidence interval (CI) is obtained by using bootstrap resampling strategy and the predicted cumulative probable count of new cases is also presented with different quantile. To validate the results with real scenario, the past COVID-19 data is considered. The % error rate ranges from -7.91% to 21.27% for the developed model for the five Indian States. 2022 Taru Publications. -
Implementation of a Heart Disease Risk Prediction Model Using Machine Learning
Cardiovascular disease prediction aids practitioners in making more accurate health decisions for their patients. Early detection can aid people in making lifestyle changes and, if necessary, ensuring effective medical care. Machine learning (ML) is a plausible option for reducing and understanding heart symptoms of disease. The chi-square statistical test is performed to select specific attributes from the Cleveland heart disease (HD) dataset. Support vector machine (SVM), Gaussian Naive Bayes, logistic regression, LightGBM, XGBoost, and random forest algorithm have been employed for developing heart disease risk prediction model and obtained the accuracy as 80.32%, 78.68%, 80.32%, 77.04%, 73.77%, and 88.5%, respectively. The data visualization has been generated to illustrate the relationship between the features. According to the findings of the experiments, the random forest algorithm achieves 88.5% accuracy during validation for 303 data instances with 13 selected features of the Cleveland HD dataset. 2022 K. Karthick et al. -
Mechanical and Wear Behavior of Aluminium Metal Matrix Composites Reinforced Ceramics Materials for Light Structures
Aluminium Alloy based Metal Matrix Composites (AAMMCs) has widely used in defense, aircraft and automobile applications because of their enhanced engineering properties with light weight metals. Nano sized silicon nitride (80 ?m) is used as a reinforcement in this study, whereas aluminium alloy 8011 is selected as the matrix material. Using the stir casting method, metal matrix composites made of aluminium alloy 8011 with varying weight percentages of Si3N4(0, 4, 8, 12, and 16) are created. The stir casted AL 8011/Si3N4composites further heated under T6 condition. The AL 8011/Si3N4 T6 composites are further subjected to Energy Dispersive X ray Analysis (EDAX) and Scanning Electron Microscope (SEM) to identify by the presence of elements and study the microstructure characterization, respectively. The density, microhardness and wear test are conducted by employing Archimedes principle, Vickers hardness tested and pin on disc equipment, respectively. The wear test is done at different sliding distances like (500, 1000, 1500 and 2000 m), applied load like (10, 20, 30 and 40 N) and kept sliding at a speed of 1 m/s. The increasing weight percentage of silicon nitride expands the increasing of density and Vickers hardness up to 12 wt % of silicon nitride and decreasing by 16 wt % addition. The wear resistances of AL 8011/12 wt % Si3N4T6 composite exhibits higher wear resistance than other Al8011 based composites. 2024, Informatics Publishing Limited. All rights reserved. -
Static analysis tool for identification of permission misuse by android applications
Android is one of the most important and widely used mobile operating systems in the world. The Android operating system utilizes the permission-based model, which permits Android applications to get user data, framework data, gadget data and other assets of Smartphone. These permissions are affirmations declared by the developer of an application. The permissions granted varies from one application to another, depending on its functionality. During installation, permissions to access the resources of the smartphone are requested by apps. Once the client grants the permission, the apps are allowed to access the granted resources as per its requirement. Android OS is susceptible to different security issues owing to the loopholes in security. This paper mainly focuses on identifying how the permissions granted to a specific application is misused by another application using SharedUserID. The paper also proposes a security tool that identifies a list of applications which are misusing the permissions in a user's Android smartphone. The viability of the tool is tested by using a Proof-of-Concept (PoC) implementation of the security tool. Research India Publications. -
Android security issues and solutions
Android operating system uses the permission-based model which allows Android applications to access user information, system information, device information and external resources of Smartphone. The developer needs to declare the permissions for the Android application. The user needs to accept these permissions for successful installation of an Android application. These permissions are declarations. At the time of installation, if the permissions are allowed by the user, the app can access resources and information anytime. It need not re-request for permissions again. Android OS is susceptible to various security attacks due to its weakness in security. This paper tells about the misuse of app permissions using Shared User ID, how two-factor authentications fail due to inappropriate and improper usage of app permissions using spyware, data theft in Android applications, security breaches or attacks in Android and analysis of Android, iOS and Windows operating system regarding its security. 2017 IEEE. -
Artificial Intelligence Based Enhanced Virtual Mouse Hand Gesture Tracking Using Yolo Algorithm
Virtual mouse technology has revolutionized human computer interaction, allowing users to interact with digital environments without physical peripherals. The concept traces back to the late 1970s, and over the years, it has evolved with significant advancements in computer vision, motion tracking, and gesture recognition technologies. In recent times, machine learning techniques, particularly YOLOv8, have been integrated into virtual mouse technology, enabling accurate and swift detection of virtual objects and surfaces. This advancement enhances seamless interaction, intuitive hand gestures, and personalized virtual reality experiences tailored to individual user preferences. The proposed model, EHT (Enhanced Hand Tracking), leverages the power of YOLOv8 to address the limitations of existing models, such as Mediapipe. EHT achieves higher accuracy in hand tracking, real-Time hand gesture recognition, and improved multi-user interactions. It adapts to users' unique gestures over time, delivering a more natural and immersive computing experience with accuracy rates exceeding those of Mediapipe. For instance, across multiple sample datasets, EHT consistently outperformed Mediapipe in hand tracking accuracy. In Sample Dataset 1, EHT demonstrated an accuracy of 98.3% compared to Mediapipe's 95.65%. Similarly, in Sample Dataset 2, EHT achieved an accuracy of 99.35%, surpassing Mediapipe's 94.63%. Even in Sample Dataset 3, EHT maintained its superiority with an accuracy of 98.54 %, whereas Mediapipe achieved 98.26%. The successful implementation of EHT requires a custom dataset and optimization techniques to ensure efficiency on virtual reality hardware. EHT model is anticipated redefining how users interact with digital environments, unlocking new possibilities for intuitive and immersive computing experiences. 2023 IEEE. -
Professional chat application based on natural language processing
There has been an emerging trend of a vast number of chat applications which are present in the recent years to help people to connect with each other across different mediums, like Hike, WhatsApp, Telegram, etc. The proposed network-based android chat application used for chatting purpose with remote clients or users connected to the internet, and it will not let the user send inappropriate messages. This paper proposes the mechanism of creating professional chat application that will not permit the user to send inappropriate or improper messages to the participants by incorporating base level implementation of natural language processing (NLP). Before sending the messages to the user, the typed message evaluated to find any inappropriate terms in the message that may include vulgar words, etc., using natural language processing. The user can build an own dictionary which contains vulgar or irrelevant terms. After pre-processing steps of removal of punctuations, numbers, conversion of text to lower case and NLP concepts of removing stop words, stemming, tokenization, named entity recognition and parts of speech tagging, it gives keywords from the user typed message. These derived keywords compared with the terms in the dictionary to analyze the sentiment of the message. If the context of the message is negative, then the user not permitted to send the message. 2018 IEEE. -
A Physics-guided Unsupervised Learning Framework for High-impact Heavy Rainfall Prediction in Data-sparse Environments
High-Impact Weather (HIW) events, particularly high-impact heavy rainfall, pose significant risks to urban infrastructure in Australia. Traditional forecasting approaches often struggle to resolve the complex, non-linear thermodynamic interactions that drive these infrequent events, while standard supervised machine learning models are hindered by severe class imbalance. This study presents a novel, multi-disciplinary framework that integrates synoptic climatology with unsupervised anomaly detection to classify and predict high-impact heavy rainfall events in Darwin, Sydney, Brisbane, and Perth. Using daily meteorological observations (20242025), we developed a multi-phase analytical framework comprising precursor, thermodynamic, kinematic, and system evolution phases to isolate the physical signatures of storm genesis. Exploratory analysis using Danger Rose polar histograms revealed a strong anisotropic risk pattern, with heavy rainfall predominantly associated with South-South-East (SSE) and West-South-West (WSW) vectors. Bivariate Kernel Density Estimation (KDE) revealed a distinct Thermodynamic Lock-in mechanism, where severe events are confined to narrow regimes of low pressure (< 1010 hPa), high humidity (> 60%), and compressed diurnal temperature ranges. To address the limited representation of severe events data (12.1%), we benchmarked five unsupervised anomaly detection algorithms. The results indicate that DBSCAN (Density-Based Spatial Clustering) yields the optimal performance (F1-Score: 0.319; Recall: 67.5%), significantly outperforming Isolation Forest and PCA. Topological validation via t-SNE projection confirms that high-impact heavy rainfall events form dense, cohesive clusters within the phase space rather than appearing as randomly distributed stochastic outliers. These findings prove that hybridizing physical phase-space analysis with density-based machine learning offers a robust pathway for early warning systems in data-sparse environments. The Author(s), under exclusive licence to Springer Nature B.V. 2026. -
Prediction of CO and NOx Emission from Gas Turbine Using Machine Learning
In gas-turbine-based power plants, predictive emission monitoring systems (PEMS) are used to validate and back up the expensive continuous emission monitoring systems. Increasing energy consumption increased deforestation and carbon and flue gas emissions, harming the environment. The availability of relevant and ecologically sound data is crucial to their successful deployment. In this article, we adopted the Gas Turbine CO and NOx Emission Data Set Data Set from UCI machine learning repository to predict the CO And NOx emission from gas turbine using machine learning (ML). We developed the model using random forest and support vector algorithms. The random forest algorithm performs better for the data. 2025 Author(s). -
A Computing Assisted Test Method in Healthcare Industry Using Artificial Intelligence
New and improved methods of diagnosis are needed because breast cancer is still the leading cancer-related killer worldwide. Updates to the methods used to categorize breast cancer have emerged because of recent advances in DL and ML. This research goal in conducting this research is to bring together existing breast cancer diagnostic and classification methods that make use of deep learning and machine learning techniques. Early detection and accurate prediction of breast cancer are crucial for improving outcomes and reducing the impact of this disease. The creation of prediction models and tools to assess risk has become an important field of research as it can assist healthcare workers in identifying individuals who are more likely to acquire breast cancer and adapting screening and preventative programs accordingly. This introduction provides an overview of breast cancer prediction, highlighting the importance of the topic and the motivation behind predictive models. It sets the stage for a more in-depth exploration of the subject and the various technologies, methods and factors involved in breast cancer prediction. 2025 IEEE. -
Photocatalytic activity of bismuth silicate heterostructures synthesized via surfactant mediated sol-gel method
A surfactant mediated sol-gel method is employed to synthesize bismuth silicate heterostructures with tunable morphologies and properties. The synthesized nanoparticle samples were characterized by XRD, FTIR Spectroscopy, SEM-EDAX and UV-DRS. The synthesized bismuth silicates exhibit excellent photodegradation against malachite green and rhodamine B dyes in the aqueous medium. Bismuth silicates (10% SiO2-Bi2O3) show superior photocatalytic property and outstanding reusability compared to pure bismuth oxide. The kinetics of the photodegradation of the dyes shows that the reaction follows first-order kinetics with the regression coefficient of 0.99. Thus, enabling Bismuth silicates heterostructures practical application as a photocatalyst for clean water. 2019 Elsevier Ltd