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Leveraging Java for Developing Privacy-Preserving and Cross-Platform Machine Learning Applications
The growing focus on data privacy and system interoperability has created a clear need for machine learning (ML) applications. These applications must be able to protect sensitive data while maintaining consistent performance in various computing environments. Java is considered as a strong choice due to its platform independence, strong static typing, and rich ecosystem of development tools and libraries. This study checks how Java supports privacy-preserving techniques such as federated learning, differential privacy, and homomorphic encryption, instilling confidence in its role for secure ML development. It also reviews Java-based libraries, including Weka, Deeplearning4j, and Apache Spark, highlighting their role in building safe, scalable, and portable ML solutions. Through architectural analysis, benchmark-based evaluation, and comparisons with other programming languages, the study demonstrates the strengths of Java to deliver secure, scalable, and interoperable privacy-sensitive machine learning applications. 2025 IEEE. -
Comprehensive Analysis of Canine Parvovirus Outbreaks: Predictive Modeling and Evaluation Metrics
This paper addresses the persistent threat of Canine Parvovirus (CPV) to canine health, exploring a spectrum of outcomes from recovery to fatalities. Employing a fusion of machine learning techniques and comprehensive evaluation metrics, we present a robust analysis of CPV outbreaks. Our methodology involves the development of a deep learning-based predictive model designed to anticipate CPV case outcomes based on symptoms and diverse contributing factors, with performance monitoring through visualization techniques. The study delves into the intricacies of a dataset featuring diverse features such as age, breed, symptoms, treatment, and geographic location. Through meticulous preprocessing and feature encoding, we establish a powerful deep learning model proficient in discerning intricate patterns within the data. Model evaluation encompasses key metrics, including accuracy, precision, recall, F1-score, confusion matrix, Cohens Kappa, and Matthews Correlation Coefficient, providing a comprehensive assessment of predictive capabilities. Our findings highlight the models proficiency in anticipating CPV outcomes, suggesting potential enhancements in decision-making within veterinary practice. Insights derived from this research contribute to the refinement of CPV diagnosis, treatment, and prevention strategies, ultimately benefiting the well-being of canine companions. The projects results demonstrate the efficacy of the proposed models in forecasting the prevalence and survival rate of the CPV virus in dogs using basic parameters. This approach eliminates the need for costly and time-consuming laboratory tests, typically requiring 1224h for results, showcasing a practical and efficient solution for CPV management. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Intelligent predictive hiring model and personality assessment
Selecting the right candidates is essential for organisational success, yet traditional hiring methods often fall short. This research introduces an advanced approach integrating natural language processing (NLP), personality assessment, and deep learning to improve candidate selection. NLP extracts key attributes from job descriptions and resumes, while personality assessments evaluate candidate suitability. A fusion of LSTM and RNN models predicts job fit using a dataset of job roles, resumes, and MBTI personality types. Pre-processing includes tokenisation, encoding, and data splitting for training and testing. The model architecture combines embedding layers with LSTM units, optimised using binary cross-entropy loss and accuracy metrics. Results show that this fusion model outperforms traditional algorithms, improving job matching accuracy. This research enhances recruitment by leveraging AI-driven insights. Future work will refine predictive models, integrate additional data, and address ethical concerns to ensure fairness and transparency, fostering a more efficient and equitable hiring process. Copyright 2026 Inderscience Enterprises Ltd. -
Spectral and Timing Properties of Selected Black Hole Binaries
X-ray binaries hosting a black hole (accretor) and a main sequence or a post-main sequence star (companion star) are called black hole X-ray binaries (BHXBs). BHXBs are gravitationally bound systems where the matter from the companion star is accreted onto the accretor either via a Roche lobe overand#64258;ow (low-mass companion star) or stellar wind (high-mass companion star). The accreted matter spirals towards the accretor, losing its angular momentum in the process. The gravitational potential energy of the in-falling matter is converted to kinetic energy which is eventually released as X-rays. X-ray spectrum of BHXB is quite complex by nature, which is contributed by various X-ray production processes. Systematic and comprehensive investigations of the X-ray production mechanisms are essential for understanding the fundamentals of accretion physics and exploring the general relativistic effects in extreme gravity environments. Launch of several dedicated X-ray missions like Uhuru, Ginga, RXTE, Chandra, XMM-Newton, NuSTAR, Swift, etc. for over half a century have led to the discovery, classifcation and fair understanding of spectro-temporal properties of BHXBs. Despite the continuous and ongoing newlineefforts, the physics of the accretion mechanism in BHXBs, accretion disk geometry, the origin of quasi periodic oscillations (QPOs), energy-dependent time lags and coherence of X-ray photons in different energies, etc., are yet to be completely understood. Hence, there is a need for newlinerevisiting these problems using the data from more sensitive instruments, that have broadband energy coverage and have better spectral and timing resolutions than RXTE. Thus, data from the latest missions like AstroSat, Swift, NuSTAR with their broadband energy coverage, especially in the lower energy regime (and#8804; 3.0 keV), and larger effective area can help fll in the gap in the newlineexisting body of knowledge and provide a holistic understanding of these sources. -
Development of Family Intervention for Management of Psychogenic Seizures : A Qualitative Study
Background: In the somatoform and dissociative spectrum, family functioning has been poorly researched based on the search in PubMed, Google Scholar, Science Direct, PROQUEST, EBSCO and Cochrane Reviews. In Psychogenic Non-Epileptic Seizures (PNES) primarily, family functioning has been understood as a comparison between Epileptic Seizures (ES) and PNES. However, an attempt to study different aspects of family functioning and its ability to influence the newlinemanifestation of the disorder is yet to be made. Methods: This study attempted to understand different family functions qualitatively by interviewing both the patients and family members of these patients. Nine patients and seven families of these patients participated in the study. Braun and Clarke s thematic model was used. Latent thematic analysis was used to analyse the data. Based on the analysis, major and sub-themes were used to develop family intervention and a family psychoeducational model. Both were developed with the help of the GUIDED Checklist to report health interventions; the TIDieR format and Delphi newlinetechnique was used to collect the expert opinion of the developed intervention. newlineResults: Two sets of results and analyses were compiled with qualitative data, that is, for patients and families. Major themes and sub-themes were developed for both, including family interaction, attachment, poor communication, structural-systemic aspects, distressful family aspects, negative newlineexpressed emotions, cultural aspects, nature of the illness, family s views of the illness and coping mechanisms. These themes indicated the importance of family functioning and its impact on the manifestation of the disorder. newlineConclusion: The researcher concludes that family dysfunction can impact the manifestation of the disorder. Hence, an additional family intervention or psychoeducation is essential for holistic newlinetreatment. -
Effect of Doping in Aluminium Nitride (AlN) Nanomaterials: A Review
Piezoelectric materials can generate electrical charges when subjected to mechanical pressure through the piezoelectric effect. In addition to generating electricity from environmental vibrations, they are also used as nano energy generators for micro electro mechanical systems (MEMS). Aluminum Nitride (AlN) with a doping element exhibits unique physical and chemical properties. It is used to manufacture many electromechanical devices. They are ideal candidates for many applications, including MEMS resonators and microwave filters, due to their large piezoelectric coefficient and low resistance. A number of material properties led to its selection, including high thermal conductivity, good mechanical strength, high resistance, corrosion resistance, and the largest piezoelectric coefficient. A piezoelectric coefficient d33 characterizes the piezoelectric response of AlN thin films. By doping this material, a wide range of applications have been explored. The Electrochemical Society -
Addressing the complexities of postoperative brain MRI cavity segmentationa comprehensive review
Postoperative brain magnetic resonance images (MRI) is pivotal for evaluating tumor resection and monitoring post-surgical changes. The segmentation of surgical cavities in these images poses challenges due to artifacts, tissue reorganization, and heterogeneous appearances. This study explores challenges and advancements in postoperative brain MRI segmentation, examining publicly accessible datasets and the efficacy of various deep learning models. The analysis focuses on different U-Net models (U-Net, V-Net, ResU-Net, attention U-Net, dense U-Net, and dilated U-Net) using the EPISURG dataset. The training dice scores are as follows: U-Net 0.8150, attention U-Net 0.8534, V-Net 0.7602, ResU-Net 0.7945, dense U-Net 0.83, dilated U-Net 0.80. The study thoroughly assesses existing postoperative cavity segmentation models and proposes a fine-tuning approach to enhance the performance further, particularly for the best-performing model, attention U-Net. This fine-tuning involves introducing dilated convolutions and residual connections to the existing attention U-Net model, resulting in improved results. These improvements underscore the necessity for ongoing research to select and adapt efficient models, retrain specific layers with a comprehensive collection of postoperative images, and fine-tune model parameters to enhance feature extraction during the encoding phase. 2024, Institute of Advanced Engineering and Science. All rights reserved. -
Enhanced Postoperative Brain MRI Segmentation with Automated Skull Removal and Resection Cavity Analysis
Brain tumors present a significant medical challenge, often necessitating surgical intervention for treatment. In the context of postoperative brain MRI, the primary focus is on the resection cavity, the void that remains in the brain following tumor removal surgery. Precise segmentation of this resection cavity is crucial for a comprehensive assessment of surgical efficacy, aiding healthcare professionals in evaluating the success of tumor removal. Automatically segmenting surgical cavities in post-operative brain MRI images is a complex task due to challenges such as image artifacts, tissue reorganization, and variations in appearance. Existing state-of-the-art techniques, mainly based on Convolutional Neural Networks (CNNs), particularly U-Net models, encounter difficulties when handling these complexities. The intricate nature of these images, coupled with limited annotated data, highlights the need for advanced automated segmentation models to accurately assess resection cavities and improve patient care. In this context, this study introduces a two-stage architecture for resection cavity segmentation, featuring two innovative models. The first is an automatic skull removal model that separates brain tissue from the skull image before input into the cavity segmentation model. The second is an automated postoperative resection cavity segmentation model customized for resected brain areas. The proposed resection cavity segmentation model is an enhanced U-Net model with a pre-trained VGG16 backbone. Trained on publicly available post-operative datasets, it undergoes preprocessing by the proposed skull removal model to enhance precision and accuracy. This segmentation model achieves a Dice coefficient value of 0.96, surpassing state-of-the-art techniques like ResUNet, Attention U-Net, U-Net++, and U-Net. (2024) Sobha Xavier P., Sathish P. K. and Raju G. -
Advancing Brain Tumor Segmentation in MRI Scans: Hybrid Attention-Residual UNET with Transformer Blocks
Accurate segmentation of brain tumors is vital for effective treatment planning, disease diagnosis, and monitoring treatment outcomes. Post-surgical monitoring, particularly for recurring tumors, relies on MRI scans, presenting challenges in segmenting small residual tumors due to surgical artifacts. This emphasizes the need for a robust model with superior feature extraction capabilities for precise segmentation in both pre-and post-operative scenarios. The study introduces the Hybrid Attention-Residual UNET with Transformer Blocks (HART-UNet), enhancing the U-Net architecture with a spatial self-attention module, deep residual connections, and RESNET50 weights. Trained on BRATS20 and validated on Kaggle LGG and BTC_ postop datasets, HART-UNet outperforms established models (UNET, Attention UNET, UNET++, and RESNET 50), achieving Dice Coefficients of 0.96, 0.97, and 0.88, respectively. These results underscore the models superior segmentation performance, marking a significant advancement in brain tumor analysis across pre-and post-operative MRI scans. 2024 by the authors of this article. -
Exploring the Adaptability of Attention U-Net for Post-operative Brain Tumor Segmentation in MRI Scans
This study explores the adaptability of a segmentation model, originally trained on pre-operative MRI data, in post-operative recurrent brain tumor segmentation. We utilized the Attention U-Net model for this study. In pre-operative training, the model achieved a Dice Coefficient of 0.92 and an IOU of 0.86 for brain tumor MRI segmentation. Due to the surgical artifacts in post-operative data, performance reduced with Dice Coefficient of 0.54 and an IOU of 0. To improve the performance, the model's architecture is fine-tuned by introducing dilated convolutions and residual connections. This refinement yielded improvements in results, with a Dice Coefficient of 0.68 and an IOU of 0.62 in the post-operative context. This improvement underscores the need for further research to select and adapt efficient models, retrain specific layers with an extensive collection of post-operative images, and fine-tune model parameters to enhance feature extraction during the encoding phase. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
A new framework for contour tracing using Euclidean distance mapping
In this paper, a new fast, efficient and accurate contour extraction method, using eight sequential Euclidean distance map and connectivity criteria based on maximal disk, is proposed. The connectivity criterion is based on a set of point pairs along the image boundary pixels. The proposed algorithm generates a contour of an image with less number of iterations compared to many of the existing methods. The performance of the proposed algorithm is tested with a database of handwritten character images. In comparison to two standard contour tracing algorithms (the Moore method and the Canny edge detection method), the proposed algorithm found to give good quality contour images and require less computing time. Further, features extracted from contours of handwritten character images, generated using the proposed algorithm, resulted in better recognition accuracy. Copyright 2021 Inderscience Enterprises Ltd. -
An Exploratory Study of Emotional Labour Among Therapists and Counsellors in India
Background: Emotional labour has been extensively investigated in the service sector, where employees manage their emotions to ensure a positive customer experience. However, there is a dearth of research into how therapists perform emotional labour during therapy sessions. Thus, the aim of this study was to explore psychotherapists' and counsellors' experiences of performing emotional labour in therapeutic settings. Method: The study used a qualitative research design with an exploratory approach. Semi-structured interviews were conducted with four clinical psychologists and four counsellors. The interviews were conducted via video call and lasted about 4560 min. Thematic analysis was used to identify emerging themes. Results: The analysis revealed that therapists experience an array of emotions during sessions. However, the expression of these emotions is guided by professional norms and emotional display rules. Participants disclosed that they use several techniques to manage their emotions both during and after sessions and that participating in emotional labour yielded both favourable and unfavourable outcomes for the therapists. Conclusion: The findings presented in this study provide insight into emotional labour and inform professionals on how this can negatively impact them if not sufficiently addressed. The study highlights the need for further investigation. In the meantime, therapists and counsellors would benefit from integrating the study's findings into their respective practices. 2025 British Association for Counselling and Psychotherapy. -
FITNESS TRAINERS PHYSICAL ATTRACTIVENESS AND GYM GOERS EXERCISE INTENTION
In line with the law of attraction, physical attractiveness has been widely used in marketing as well as advertising due to its potency in persuading consumers to take action. However, would physical attractiveness of a fitness trainer influence gym goers intention to exercise? This question motivated this research. Based on recent literature reviews, several research constructs were identified to form a research framework to investigate the physical attractiveness phenomena in the fitness industry. Hypothetically, the impact of the physical attractiveness of a fitness trainer on gym goers exercise intention is postulated to be mediated by trainers perceived expertise, trustworthiness, likeability and perceived health. Questionnaires were administered among gym-goers from 10 randomly selected fitness centres across three districts of Melaka State in Malaysia, and 192 final sample data were obtained. Data analysis reveals fitness trainers perceived expertise and likeability significantly mediates the relationship between the physical attractiveness of fitness trainers and gym goers exercise intention. Physical attractiveness of fitness trainers does impact the exercise intention of gym goers indirectly. Implications of the findings to theory and practice are also discussed in this paper, as well as suggestions for future studies. 2022, Universiti Malaysia Sarawak. All rights reserved. -
Gym-Goers Self-Identification with Physically Attractive Fitness Trainers and Intention to Exercise
Gym-goers often socially compare themselves with their trainers as they strive to look as attractive as their fitness trainers. The aim of this study was to better understand this phenomenon in the fitness industry. Relying on social comparison theory and social identity theory, self-identification with a physically attractive fitness trainer was posited to have a strong mediating effect on the relationship between appearance motive, weight management motive and gym-goers intention to exercise. The moderation effects of gym-goers age and gender in the direct relationships between appearance motive, weight management motive and exercise intention were also examined. The primary outcome of this study revealed that gym-goers who were influenced by appearance and weight management motives are more likely to identify with physically attractive fitness trainers. Additionally, gender significantly moderates the relationships between appearance motive, weight management motive and exercise intention. Appearance and weight management motives are the primary factors that influence the exercise intention of female gym-goers as compared to their male counterparts. This study sheds new insights into understanding the influence of the physical attractiveness of fitness trainers and its impact on gym-goers exercise intentions via self and social identification process. 2022 by the authors. Licensee MDPI, Basel, Switzerland. -
A Prompt Study on Recent Advances in the Development Of Colorimetric and Fluorescent Chemosensors for Nanomolar Detection of Biologically Important Analytes
Fluorescent and colorimetric chemosensors for selective detection of various biologically important analytes have been widely applied in different areas such as biology, physiology, pharmacology, and environmental sciences. The research area based on fluorescent chemosensors has been in existence for about 150years with the development of large number of fluorescent chemosensors for selective detection of cations as metal ions, anions, reactive species, neutral molecules and different gases etc. Despite the progress made in this field, several problems and challenges still exist. The most important part of sensing is limit of detection (LOD) which is the lowest concentration that can be measured (detected) with statistical significance by means of a given analytical procedure. Although there are so many reports available for detection of millimolar to micromolar range but the development of chemosensors for the detection of analytes in nanomolar range is still a challenging task. Therefore, in our current review we have focused the history and a general overview of the development in the research of fluorescent sensors for selective detection of various analytes at nanomolar level only. The basic principles involved in the design of chemosensors for specific analytes, binding mode, photophysical properties and various directions are also covered here. Summary of physiochemical properties, mechanistic view and type of different chemosensors has been demonstrated concisely in the tabular forms. Graphical Abstract: In our current review we have focused the history and a general overview of the development in the research of fluorescent sensors for selective detection of various analytes at nanomolar level only. [Figure not available: see fulltext.] 2024, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Comparison of the Results of Steady Darcy-Be ?ard Convection Problems of the Classical and the Barletta Types
The linear stability analysis of the Barletta-Darcy-Bnard convection problem in a horizontal fluid-saturated porous layer is extended to a weakly nonlinear stability analysis considering local thermal equilibrium (LTE) between the fluid and solid phases. The minimal Fourier-Galerkin expansion is used for the case of a free upper surface (Neumann boundary condition on the stream function) along with isothermal boundary condition for which heat transport is quantified in terms of the Nusselt number. The present article aims to fill the literature gap between the linear and nonlinear stability analyses of classical Darcy-Bnard convection and of Barletta-Darcy-Bnard convection. Weakly non-linear stability analysis has not been performed in the case of the non-classical Darcy-Bnard convection problem. A comparison of results of the present problem with those of the classical Darcy-Bnard convection problem is made. It is found that the cell size is larger in the case of the former problem compared to the latter. The critical Darcy-Rayleigh number, however is smaller in the former one. The Nusselt number varies inversely as the Rayleigh number, R and hence the Nusselt number increases with decrease in R which implies that more heat is transported in Barletta-Darcy-Bnard convection compared to classical Darcy-Bnard convection. 2025, Semarak Ilmu Publishing. All rights reserved. -
Evaluation of Social Media Marketing Literature in the Tourism Industry Using PRISMA
Social media is an effective communication and information-sharing tool for tourism enterprises and organisations. Tourism marketing shall tap the growing popularity of social media and internet users, embracing a technological shift by optimising the potential of social media. This research study evaluates the academic journal articles related to social media in the tourism industry published on EBSCOhost, ScienceDirect and Google Scholar academic databases from 2005 to 2022. The article adopts a content analysis approach to review the articles and to evaluate the present state of knowledge of social media marketing in academic literature. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) is used for reporting and screening the review papers. The articles were coded and categorised under six major themes: Marketing, Destination experience/image, Tourism recovery, Smart tourism, Communication and Promotion. The research analysis has identified two major areas: (a) Travellers/tourists Perspective which has a focus on their behavioural attitude and (b) Tourism Agencies Perspective which has a functional approach. Based on the review of the literature to give direction for further research, an improvised version of the definition for the term social media with the inclusion of more specific terms in it has been proposed with theoretical and practical implications. 2023 MICA-The School of Ideas. -
Evaluation of Social Media Marketing Literature in the Tourism Industry Using PRISMA
Social media is an effective communication and information-sharing tool for tourism enterprises and organisations. Tourism marketing shall tap the growing popularity of social media and internet users, embracing a technological shift by optimising the potential of social media. This research study evaluates the academic journal articles related to social media in the tourism industry published on EBSCOhost, ScienceDirect and Google Scholar academic databases from 2005 to 2022. The article adopts a content analysis approach to review the articles and to evaluate the present state of knowledge of social media marketing in academic literature. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) is used for reporting and screening the review papers. The articles were coded and categorised under six major themes: Marketing, Destination experience/image, Tourism recovery, Smart tourism, Communication and Promotion. The research analysis has identified two major areas: (a) Travellers/tourists Perspective which has a focus on their behavioural attitude and (b) Tourism Agencies Perspective which has a functional approach. Based on the review of the literature to give direction for further research, an improvised version of the definition for the term social media with the inclusion of more specific terms in it has been proposed with theoretical and practical implications. 2023 MICA-The School of Ideas. -
Regulating the Rise of Transformers in Global Healthcare: A Legal and International Law Perspective on AI Governance, Ethics, and Data Protection
With advances in healthcare, GPT, BioGPT and MedPaLM are helping doctors diagnose better, choose better treatments and engage with patients. Yet, these uses bring up significant problems related to privacy, understanding what the algorithms do, who is responsible for actions and gaining agreement. The paper examines global regulation on AI-for example, GDPR, HIPAA, India's DPDP Act, OECD AI Principles and UNESCO's AI Ethics Recommendation-and points out where gaps still exist, mainly in places with lower and middle incomes. Using both comparative studies and a focus on policies, it proposes common standards, specially designed rules for AI liability and ways to test ethical considerations. To provide secure, clear and equal use of AI in healthcare markets, the study proposes that countries come together to govern the technology. 2026, IGI Global Scientific Publishing. All rights reserved.