Volume 13 | Issue 5 |

 Abstract

Alzheimer's Disease (AD) is an ever-growing neurodegenerative condition seriously debilitating cognitive functions and posing the requirement of early diagnosis and treatment. Use of AI-based chatbots integrating Natural Language Processing (NLP) and Machine Learning (ML) has shown to be effective in screening cognitive disorders in the last few years. This survey discusses the existing hybrid chatbot architectures specifically aimed at screening and recommending treatment of Alzheimer's disease. The baseline paper taken into consideration, "A Hybrid Transformer-Based Model for Alzheimer's Disease Prediction from Conversational Data" (Elsevier, 2025), harmoniously combines conversational AI and understanding of clinical language to provide precise diagnosis. We discuss comparative studies published in recent journals of Q1 and the limitations of existing models on computational complexity and non-personalization. We finally introduce pragmatic solutions with regards to student-level work with resource constraints. The objective is to introduce an updated, efficient, and cost-effective chatbot architecture yielding maximum diagnostic accuracy and clinical usability.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Alzheimer's Disease, Clinical NLP, Hybrid Chatbot, Conversational AI, Transformer Models, Cognitive Screening.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The increasing integration of renewable energy sources (RES) in modern power systems has introduced new challenges in maintaining frequency stability due to their intermittent and uncertain nature. Multi-source interconnected power systems (MSIPS) consist of a mix of conventional and renewable generation, requiring advanced control strategies for effective frequency regulation. This paper reviews various frequency stability control techniques, focusing on Automatic Generation Control (AGC) strategies, optimization-based controllers, and artificial intelligence (AI)-driven approaches. The effectiveness of Particle Swarm Optimization (PSO)-tuned Proportional-Integral-Derivative (PID) controllers, Fuzzy Logic Controllers (FLCs), and Neural Network-based controllers is analyzed in mitigating frequency deviations. Furthermore, the impact of energy storage systems (ESS), demand-side management (DSM), and hybrid optimization methods on frequency stability is explored. The review also highlights multi-area system considerations, robustness testing, and sensitivity analysis of various control methods. Finally, a comparative assessment of recent advancements is presented to provide insights into the future of frequency stability control in multi-source power networks.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Load frequency control, renewable energy systems, multi-area power system, optimization algorithms, artificial neural networks.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The family plays a vital role in the holistic development of a child, shaping their personality and influencing their behavioural traits. With the decline of traditional joint families and the rise of nuclear family systems in India, concerns have emerged about the impact of these structural shifts on children's personality development. This study titled "A Comparative Study of Personality of Students from Separate and Joint Families" aims to investigate the differences in personality traits among students from joint and nuclear families, with a focus on children and adolescent girls. Utilizing a survey method, data were collected from 1,000 students (500 from joint families and 500 from nuclear families) across the Amravati division, encompassing five districts. The Introversion-Extroversion Inventory (IEI) by Dr. P. F. Aziz and Dr. Rekha Gupta was employed as the assessment tool. Statistical analysis using mean scores and t-tests revealed significant differences in personality traits across all categories studied. Students from joint families demonstrated higher levels of sociability, empathy, and self-confidence compared to their counterparts from nuclear families. The findings underscore the importance of nurturing familial environments for the positive personality development of children. The study recommends strengthening parent-child relationships, promoting joint family values, and integrating personality development activities within school curricula.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Personality development, joint family system, nuclear family System, adolescent personality traits

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Abstract This study explores the impact of family structure--nuclear and joint--on the parent-child relationship and academic achievement of students. Utilizing a sample of 500 students from joint families and 500 students from separated families across five districts in Maharashtra, India, data was collected using standardized scales and academic records. The results indicate a significantly stronger parent-child relationship in joint families. However, no significant difference was observed in academic achievement across both family types. The findings emphasize the need for nurturing parent-child bonds regardless of family structure and advocate for awareness and supportive practices among parents.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Keywords: parent-child relationship, academic achievement, joint family, nuclear family, family structure, secondary education, India

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This study investigates the cash management practices of Tube Investments of India Limited over a five-year span, analyzing trends in cash flows from operating, investing, and financing activities. By using secondary data and applying financial tools such as cash flow ratios and trend analysis, the research evaluates the company's liquidity position and efficiency in cash utilization. Results indicate peak cash flow performance during 2020-2021, followed by a gradual decline in closing balances, emphasizing the need for strategic improvements in investment planning and financial control. The study provides insights for manufacturing firms seeking to optimize their cash management strategies.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Cash Management, Liquidity, Cash Flow Analysis, Operating Activities, Investing Activities, Financing Activities, Financial Performance, Trend Analysis, Working Capital, Manufacturing Industry

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This paper presents a novel approach to wheelchair control using hand gesture recognition, enabling enhanced mobility for individuals with disabilities. The system employs computer vision and sensor-based technologies to interpret predefined hand gestures, translating them into wheelchair movement commands. Using machine learning algorithms, the model ensures accurate gesture recognition with minimal latency. The proposed system enhances accessibility by reducing dependence on physical joysticks or voice commands, making it suitable for users with varying motor abilities. Experimental results demonstrate high accuracy in gesture detection and smooth wheelchair navigation. Future work includes optimizing real-time processing and integrating adaptive learning for personalized control.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Arduino Uno, Smart Wheelchair, Autonomous Navigation, Obstacle Avoidance, Controller, MPU-6050 sensor, DC Motors

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Plant diseases pose a significant threat to global agricultural productivity, leading to major economic losses and food insecurity. Early detection of plant diseases is crucial for effective management and prevention, reducing the need for pesticides and minimizing crop damage. This research explores the application of transfer learning in machine learning for the identification and classification of plant diseases. Transfer learning is a technique that leverages pre-trained models on large-scale datasets and fine-tunes them on smaller, domain-specific datasets, thus overcoming the challenges of limited labelled data in plant disease classification. In this study, we employ deep learning models, specifically Convolutional Neural Networks (CNNs), which have demonstrated high accuracy in image recognition tasks. By using transfer learning with pre-trained models like Visual Geometry Group (VGGNET), we evaluate the models based on classification accuracy, sensitivity, and specificity, aiming to achieve high accuracy in detecting multiple diseases across different plant species. The study's findings indicate that our suggested approach in contrast to conventional feature extraction methods. A reliable and effective method for early disease detection in agriculture is offered by the combination of VGGNet, which significantly increases crop disease prediction precision, responsiveness, and discrimination. The MobileNet Random Forest Classifier (RFC) model's experimental results showed the maximum accuracy of 92.39%. By establishing the foundation for automated crop disease diagnosis, this study advances precision agriculture and eventually helps farmers make prompt, well-informed decisions to reduce crop losses This dataset includes 13 classes of tomato, potato, and bell pepper diseases, ranging from common fungal infections to nutrient deficiencies

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Transfer Learning, Deep Learning Models , Image Classification ,VGG , Feature Extract.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The rapid integration of Artificial Intelligence (AI) in healthcare has revolutionized medical diagnosis, treatment, and patient care. This comprehensive review provides an in-depth examination of AI's transformative impact on healthcare, encompassing drug discovery, clinical trials, patient care, robotics-assisted surgery, and virtual nurse assistants. The applications of AI in healthcare are vast, with potential benefits including improved diagnostic accuracy, enhanced patient outcomes, and reduced healthcare costs. However, AI's limitations and challenges in healthcare are also addressed, including ethical concerns, data bias, reliability and safety issues, transparency and accountability challenges, effects on patient trust, and data privacy and security risks. The review highlights the need for interdisciplinary collaboration, regulatory frameworks, and emerging trends such as Explainable AI and Edge AI.By exploring AI's applications and challenges in healthcare, this review informs healthcare professionals, policymakers, and researchers on the future directions of AI in healthcare. The article concludes with recommendations for addressing current challenges, ensuring AI's safe and effective integration into healthcare, and optimizing its benefits for improved patient outcomes.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

artificial intelligence, ethics, governance, healthcare

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Abstract Employees in educational institutions face a variety of challenges that can impact their job satisfaction, performance, and overall well-being. Objectives: to assess the key challenges encountered by employees in selected educational institutions, focusing on factors such as workload, job-related stress, salary satisfaction, administrative support, and work-life balance. Methodology: Data was collected using a structured questionnaire and analyzed through statistical measures, including mean, median, standard deviation, and frequency distribution. Result: The findings indicate that workload and job-related stress are among the most significant concerns, with many employees experiencing high pressure and long working hours. Salary dissatisfaction and limited administrative support were also identified as moderate challenges, affecting employee motivation and retention. Conclusion: work-life balance emerged as a crucial issue, highlighting the need for better institution

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

workload, job-related stress, salary satisfaction, administrative support, and work-life balance

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Periodontics, the dental specialty focused on preventing, diagnosing, and treating periodontal disease and maintaining healthy periodontal structure, is being transformed by 3D Printing Technologies. 3D printing technology is common in dentistry. 3D Bioprinting is the utilization of 3d printing technology to combine cells, growth factors, and biomaterials to fabricate biomedical parts that maximally imitate natural tissue characteristics. It's a novel technique that brings evaluation over the years. This helps in opening several new avenues of regenerative periodontology. Different types of 3D printing are droplet-based printing, laser-assisted printing, and extrusion 3d printing. This technology facilitates the production of bio-active materials that mimic the natural bony structures, improving the success rates of bone grafts. Personalised Periodontal applications include education models, scaffolds, socket preservation, sinus and bone augmentation, and guided implant placement. It showed better alveolar ridge preservation, better regenerative capabilities, greater reduction in pocket depth and bony fill, ease of implant placement in complex cases with greater precision tailored to the unique anatomy of each patient, ensures higher accuracy during procedures, and expedites the healing process. The optimised bioprinting system is the first step towards a revolutionary approach, not only elevating the standard of periodontal care but also reducing costs and treatment times, heralding a new era in periodontal therapy.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

periodontal 3d printing, recent advances in periodontal therapy, 3d printing

  License

Creative Commons Attribution 4.0 and The Open Definition