Volume 13 | Issue 4

 Abstract

Pulmonary tuberculosis is a bacterial infection caused by Mycobacterium tuberculosis bacteria (MTB). It primarily affects the lungs but also affect other parts of the body. The disease spreads from one person to another person through the air. In some areas where there aren't many specialized doctors and medical resources, finding pulmonary TB in its early stages is crucial. The idea is to teach the computer to recognize TB by using various multimodal deep learning techniques like VGG16, VGG19, and Xception to help quickly and accurately identify TB in places with limited medical resources. This paper how artificial intelligence and chest X-ray images can work together to identify whether a person has TB or not.

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 Keywords

X-ray image, Deep learning, VGG16, VGG19, Xecption, Healthcare Accessibility, Medical Images.

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Industrial design has a critical role in developing technology by combining beauty with functionality and optimizing user experience. The following paper investigates its influence through legal, economic, and strategic aspects. Industrially, protection for industrial design stimulates creativity by establishing the right of creators and facilitating competitive positioning on the basis of intellectual property legislation. Prominent cases, including Apple vs. Samsung, demonstrate how design rights have an impact on market results. Economically, design leads to product differentiation, consumer preference, and value creation, particularly in design-intensive sectors such as electronics, automotive, and fashion. It is an important contributor to brand identity, customer loyalty, and business success. Strategically, firms that incorporate design thinking in their innovation processes tend to gain a competitive advantage. Industrial design is a key driver of R&D, improves usability, and reinforces branding, allowing firms to effectively meet market demands. In conclusion, the article brings to the forefront industrial design as the major driver of technological advancement and an important aspect in defining a company's long-term success and competitive edge in a fast-changing global market.

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Keywords: Industrial design, technological advancement, intellectual property, design protection, Apple vs. Samsung, product differentiation, user experience, brand identity, competitive advantage, design thinking, legal aspects, economic impact, strategic value, R&D, innovation.

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Premenstrual Syndrome (PMS) is a cyclic disorder characterized by a range of emotional, behavioral, and physical symptoms that occur during the luteal phase of the menstrual cycle. It significantly affects the daily functioning and quality of life of many women. While conventional treatments focus primarily on symptom suppression through hormonal therapy, analgesics, or antidepressants, these methods may come with side effects and do not always offer long-term relief. This study aimed to evaluate the effectiveness of individualized homeopathic treatment in managing PMS. Method: 30 cases diagnosed with PMS were selected based on specific inclusion criteria. All participants were treated exclusively with individualized homeopathic remedies tailored to their unique symptom profiles and overall constitution. The study was conducted over a defined treatment period, with regular follow-ups to assess changes in the intensity and frequency of PMS symptoms. Result: Out of the 30 cases, 27 showed marked improvement in emotional and physical symptoms, while 3 cases remained unimproved. The results demonstrated a high success rate, indicating that homeopathy may offer a safe, effective, and holistic approach to managing PMS. The individualized nature of the treatment appeared to play a key role in addressing the root causes of the condition rather than merely suppressing symptoms. These findings support the potential of homeopathy as a reliable alternative in the treatment of PMS. Conclusion: Further research with larger sample sizes and more extended follow-up periods is recommended to validate these results and explore the scope of homeopathy in women's health care.

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 Keywords

Premenstrual Syndrome (PMS), Homeopathy, Individualized Treatment, Case Series, Holistic Medicine.

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 Abstract

The classification of satellite images is vital in remote sensing for tasks such as mapping land cover, monitoring disasters, and conducting environmental assessments. Nonetheless, deep learning models frequently encounter substantial computational requirements, which hinder their use in real-time settings. To mitigate this issue, we introduce an Embedded Teacher-Student CNN framework that utilizes knowledge distillation for more efficient classification of satellite images. ResNet50 and VGG16 act as teacher models, capturing spatial features and producing soft labels to train a lightweight EfficientNet-B0 student model. Experimental results demonstrate high classification accuracy with reduced inference time, making the model suitable for real-time and resource-constrained applications.

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Satellite Image Classification, Knowledge Distillation, Teacher-Student Learning, Convolutional Neural Networks (CNN), ResNet50, VGG16, EfficientNet-B0, Hybrid Loss Function, Remote Sensing, Computational Efficiency

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1. Study Design A three armed clinical trial is a clinical research experiment in which a new proposed treatment is compared against existing standard form of treatment. It consists of 'Control group' which continues with the homoeopathy treatment. 'Experimental group' which receives allopathic treatment with homeopathic medicines. An 'Observational group' which is given the allopathic treatment. In this study we shall be comparing the effects of homoeopathic medicine. Eupatorium perfoliatum in dengue fever of adult population with a Experimental group of individual taking antiviral medications and other allopathic drugs according to symptomatology -TAMIFLU, AZTREONAM AND CEFACTIVE for viral fever 2.Operational Definitions: Dengue fever is mosquito borne infections, transmitted by Aedes aegypti,clinically manifested as High/low grade fever( pattern classically biphasic or saddleback & breaking), Rash( maculopapular) ,Thromboctopaenia, Myalgia , Arthralgia and Gastrointestinal symptoms like nausea & vomiting and lasting for 3-14 days( Incubation Period). The study will be carried out on adults suffering from Dengue fever based on Dengue Severity Scale. Out of 30 cases, 10 cases are under allopathic treatment and 10 cases will be given homoeopathic medicine with allopathic medicine to assess its effects and remaining 10 cases will receive pure homeopathic medicine Eupatorium perfoliatum. Result: Out of 30 cases, 10 are homoeopathic cases, in which 8 cases got improved and 2 cases did not improve. Next 10 cases are of allopathic, in which 6 cases get improved with allopathic treatment and 4 cases did not improve. The last 10 cases are allopathic cases intervention with homoeopathic cases, in which 5 cases get improved with both Pathy treatment and 5 cases did not get improved. Maximum patients belonged to the age group 30 -80 years of age; 7 patients belonged to the age group of 15 -30 years of age, 23 patients belonged to 30 - 80 years of age. Around 60 percent of patients were male and 40 percents were female. Conclusion: Cases in which homeopathic medicine is prescribed, the result are quick and faster in relief of complaints.

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Dengue Fever, Adult Population, Eupatorium Perfoliatum, Clinical Trail, Etc.

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 Abstract

The study examines the impact of induction programs in shaping employees' experiences as they first enter the private sector in the banking environment, as viewed through an Organizational Behavior (OB) lens. A key study was on the ability of a structured induction process to develop new employees' levels of integration, engagement, and ultimately retention. Taking into account various qualitative and quantitative data from fiction and non-fiction research and its accompanying interviews, this research presented how organization culture - exemplified by socialization and other tactics - and organization leadership can, or were, able to create new hires who had or turned into "organization insiders" or members of the organization who were committed to its objectives. The research presented opportunities for organizations to improve their employee induction programs based on OB principles to better facilitate, and transitions from outsiders to insiders.

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Induction Programs, Organizational Behavior, Organizational Culture, New Joiners, Employee Engagement

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The Traffic signs are important in communicating information to drivers. Thus, comprehension of traffic signs is essential for road safety and ignorance may result in road accidents. Traffic sign detection has been a research spotlight over the past few decades. Real- time and accurate detections are the preliminaries of robust traffic sign detection system which is yet to be achieved. This study presents a voice-assisted real-time traffic sign recognition system which can assist drivers. This system functions under two subsystems. Initially, the detection and recognition of the traffic signs are carried out using a trained Convolutional Neural Network (CNN). After recognizing the specific traffic sign, it is narrated to the driver as a voice message using a text-to-speech engine. An efficient CNN model for a benchmark dataset is developed for real-time detection and recognition using Deep Learning techniques. The advantage of this system is that even if the driver misses a traffic sign, or does not look at the traffic sign, or is unable to comprehend the sign, the system detects it and narrates it to the driver. A system of this type is also important in the development of autonomous vehicles. Initially, the detection and recognition of the traffic signs are carried out using a trained Convolutional Neural Network (CNN). After recognizing the specific traffic sign, it is narrated to the driver as a voice message using a text-to-speech engine. An efficient CNN model for a benchmark dataset is developed for real-time detection and recognition using Deep Learning techniques. The advantage of this system is that even if the driver misses a traffic sign, or does not look at the traffic sign, or is unable to comprehend the sign, the system detects it and narrates it to the driver. A system of this type is also important in the development of autonomous vehicles.

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Convolutional Neural Network (CNN), Dataset, Image Processing

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Along with the rise in technology, there is an increase in attacks involving digital platforms. It is very important to stay secure in this digital world. To respond to these attacks, we propose a method that detects phishing websites by categorizing the Internet URL and domain names of websites with the Random Forest classifier algorithm according to seventeen predetermined features. To illustrate the highest accuracy rate, the use of Random Forest algorithm is preferred. In this method, a dataset with 10,000 URLs in which 5000 URLs are non-phishing websites and 5000 URLs are phishing websites to be labelled according to seventeen predetermined features.

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Cybersecurity, Phishing detection, Phishing domains, Legitimate domains, Random Forest.

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The automated generation of realistic images from text prompts remains a challenging yet significant goal. Current AI technologies still face difficulties in fully mastering this capability. Recent advancements have led to the development of adaptable and resilient recurrent neural network architectures that effectively capture meaningful textual feature embeddings. Additionally, convolutional-based GAN frameworks have achieved notable success in synthesizing photorealistic visuals for specific applications, including human face synthesis, music cover design, and interior layout simulations. This research proposes a hybrid neural architecture that integrates adversarial training methods to combine progress in textual understanding and image synthesis. This integration enables the transformation of semantic concepts into high-resolution imagery through cross-modal alignment. The proposed system demonstrates promising capabilities in generating plausible avian and floral imagery from text-rich descriptions.

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AI generation tool, machine learning algorithms, natural language processing, human-like text content, minimal human input.

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Human trafficking, a grave violation of human rights, demands rigorous legal intervention to ensure justice for survivors and deter perpetrators. This article examines the intricate relationship between judicial outcomes and the narratives of human trafficking victims. It delves into the prosecution and conviction rates of trafficking cases, highlighting the existing gaps and challenges within the criminal justice system. Furthermore, it underscores the critical importance of incorporating the lived experiences of survivors into legal proceedings to foster a more victim-centered and ultimately more effective approach to combating human trafficking. By analyzing the disparities between legal outcomes and victim narratives, this research aims to contribute to a more nuanced understanding of the complexities inherent in addressing this heinous crime.

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Keywords: human trafficking, judicial outcomes, prosecution rates, conviction rates, victim narratives, lived experiences, criminal justice system, victim-centered approach, anti-trafficking strategies, legal frameworks, justice for survivors, deterrence, gaps in legal outcomes, survivor perspectives, trauma-informed practices.

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Women's safety is a critical concern in modern society, and leveraging machine learning (ML) technologies offers innovative solutions to enhance personal security. This paper explores the application of machine learning for real-time detection of dangerous or threatening situations, aimed at improving the safety of women. The proposed system integrates various techniques such as computer vision, audio analysis, wearable sensors, and natural language processing to detect potential risks, including physical assaults, verbal threats, and abnormal behavior. Machine learning models analyze real-time data, such as video feeds, audio signals, and motion patterns from wearable devices, to identify distress signals and trigger immediate alerts. In addition, geofencing and behavioral prediction models enable proactive monitoring of users' movements, sending notifications if dangerous situations are detected or if a user deviates from safe routines. While promising, the system faces challenges related to data privacy, accuracy, and real-time processing requirements. Despite these challenges, machine learning presents a transformative opportunity to enhance women's safety, offering efficient, scalable, and personalized protection through automated threat detection and immediate emergency response mechanisms.

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Machine Learning, Artificial Intelligence, Natural Language Processing

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The intricate interplay between economic systems and judicial mechanisms has significantly shaped the dispensation of justice in India, reflecting the socio-economic and cultural evolution of the country. This research explores the historical progression of this dynamic relationship, from the community-centric practices of ancient India to the exploitative judicial policies of colonial rule, and the judiciary's pivotal role in post-independence economic reforms and liberalization. The study highlights how ancient texts like the Dharmashastras and Kautilya's Arthashastra enshrined principles of economic equity and legal fairness, promoting community trust through localized dispute resolution. The colonial era, however, disrupted these practices, prioritizing mercantile interests over indigenous rights. In post-independence India, the judiciary emerged as a custodian of socio-economic justice, balancing constitutional mandates with economic reforms. As globalization and digitalization reshape economic paradigms, the judiciary faces new challenges, such as regulating cryptocurrencies, addressing gig economy disputes, and safeguarding intellectual property in a globalized market. This paper underscores the judiciary's adaptability and its enduring commitment to justice and equity amidst economic transitions. The findings advocate for enhanced judicial capacity-building, adoption of technology-driven solutions, and strengthened alternative dispute resolution mechanisms to uphold justice in a rapidly evolving economic landscape.

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Economy, judicial system, socio-economic justice

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Increasing population and density are making conventional garbage collection procedures inefficient. Overflowing public bins harm both people and nature. This paper presents a Smart Trash Manager System which solves these problems by using a centralized system integrated with IoT. It enables live monitoring of bin levels, their GPS location, and automated alerts before reaching capacity of bins. The developed system works with smart bins which have a Node MCU microcontroller. Smart bin uses ultrasonic sensors for filled level data, GPS module for geolocation and Twilio for SMS Alerts to respective worker. The alerts are also visible on system dashboard which is overviewed by Municipal officer, from the office. The Software Interface have multiple functional pages including but not limited to live bin statuses in an interactive map, worker data analytics, live bin monitoring list, attendance. The solution also considers municipal corporation workers and supervisors by providing a centralized system, thus streamlining the collection process, resulting in saving time and energy.

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Internet of Things (IoT), Municipal solid waste, Smart Cities, Smart waste management, Sustainability

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The basic building blocks of proteins, amino acids, have dynamic conformational flexibility that has a big impact on biological structure and function with small molecules can modulate these conformations, leading to altered biochemical behavior with implications in drug design, enzymatic regulation, and molecular recognition. This review presents a comprehensive computational investigation into the conformational changes of amino acids induced by small molecule interactions. Utilizing molecular dynamics (MD) simulations, quantum mechanical (QM) calculations, and hybrid QM/MM approaches, we explore how non-covalent interactions--such as hydrogen bonding, ?-? stacking, and van der Waals forces--affect the torsional angles and side-chain orientations of amino acids in isolated and solvated environments. Emphasis is placed on the role of solvent effects, energy landscapes, and potential energy surface (PES) mapping in elucidating these conformational shifts. Case studies involving biologically relevant ligands demonstrate the diverse effects of small molecules on the structural plasticity of amino acids. By integrating data from recent computational studies, this review highlights emerging trends, methodological advancements, and key challenges in the field. The findings provide critical insights for rational drug design, peptide engineering, and understanding protein-ligand interactions at the molecular level.

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Amino Acid Conformational Changes, Small Molecule Interaction Computational Chemistry, Molecular Dynamics Simulations, Quantum Mechanical Studies, Protein-Ligand Interactions, Conformational Flexibility, Non-Covalent Interactions, QM/MM Methods, Drug Design Insights, Structural Biology, Energy Landscape Analysis, Bioinformatics, Molecular Modeling, Theoretical Chemistry

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This paper presents the development of a Smart Bus Attendance and Transportation Management System using RFID technology and Google Firebase that streamlines student transportation management in educational institutions. Traditional manual attendance systems are prone to errors and inefficiencies; our proposed solution offers real-time attendance logging, automated E-Pass generation, and centralized data access for students and administrators. Each student carries an RFID tag scanned at boarding and deboarding, with the data instantly stored and updated in Firebase. The integration of bar-code scanning enhances identity verification, while the dashboard allows route analysis, performance monitoring, and secure data retrieval. Designed with scalability and usability in mind, the system supports both mobile and web access, ensuring better communication, reduced administrative workload, and increased transport security. This solution represents a modern, cost-effective approach to automate student transportation, enabling data-driven decisions and improving overall operational efficiency.

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RFID Technology, Google Firebase, Real-Time Monitoring, ESP32 Micro-controller, Cloud-Based Attendance System.

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Creative Commons Attribution 4.0 and The Open Definition

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Graphic novels have emerged as competent tools for documenting trauma and resistance, through unique ways of depicting marginalized histories. This paper simultaneously examines Art Spiegelman's novel Maus and Srividya Natarajan and S. Anand's Bhimayana as acts of resistance literature, exploring how they use visual storytelling to represent oppression, memory, and inherited trauma. Bhimayana uses the Tribal Pardhan Gond art form to highlight caste-based discrimination of the Untouchables in India. Whereas, Maus employs documentary-style realism to narrate the Holocaust's lasting impact on an individual. Through a comparative analysis of their artistic styles, this study argues that Maus and Bhimayana not only document historical injustices but also pose as counter-narratives that resist erasure and demand social awareness. By bridging trauma studies and visual culture, this paper contributes to the increasing discourse on marginalized voices in literature and the political significance of graphic storytelling.

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Visual narratives, Trauma, Resistance, Memory, Oppression, Counter Narratives

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This paper explores the evolving geopolitical and strategic dimensions of India-Nepal relations in the context of China's growing influence in South Asia. Rooted in deep historical, cultural, and geographical ties, India and Nepal have traditionally enjoyed close bilateral relations. However, the recent shift in Nepal's foreign policy orientation, marked by increased economic, diplomatic, and military engagement with China particularly through the Belt and Road Initiative which has introduced new complexities to this dynamic. The paper analyzes the historical foundations of India-Nepal ties, the strategic implications of China's expansive presence in Nepal, and the consequent geopolitical challenges India faces. It further examines Nepal's balancing act in leveraging both relationships to advance its national interests. Finally, the study discusses potential future scenarios, emphasizing the prospects for trilateral cooperation to mitigate regional tensions and foster sustainable development. Through a critical assessment of contemporary developments, the paper underscores the importance of recalibrating India's strategic approach to maintain its traditional influence while accommodating shifting regional dynamics.

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Belt and Road Initiative (BRI), Neighbourhood First Policy, Trilateral Cooperation, Border Disputes, Development Diplomacy, Foreign Policy Balancing,Soft Power Influence

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Understanding and being aware of our learning processes is crucial for achieving academic success. It is of utmost importance to develop metacognitive skills that allow us to improve how we learn and think, leading to even better accomplishments in our educational journey. These incredible skills provide us with a deep level of understanding regarding what works best for us individually, enabling us to adapt more effectively to different tasks. By becoming masters of metacognition, we enhance our ability to manage our learning and make intelligent decisions about which strategies to employ while considering our unique goals and preferences. Furthermore, when we possess strong metacognitive skills, we elevate our critical thinking abilities and become incredibly discerning when evaluating the vast array of information that we encounter. With the power of metacognition, we can effectively tackle complex problems, leading to improved problem-solving abilities and fostering resilience and confidence within ourselves. Moreover, the cultivation of metacognitive skills extends its benefits far beyond the realm of academics, as these skills prove to be instrumental in lifelong learning. The ability to think introspectively and tailor our learning approaches to suit our strengths and weaknesses proves incredibly advantageous as we pursue higher education and actively engage in professional endeavors. In conclusion, developing metacognitive skills plays a crucial role in achieving academic success, while also preparing us to gracefully navigate the complexities of the modern world as individuals committed to lifelong learning.

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Metacognition, metacognitive skill, academic performance, different learning environments, etc.

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Creative Commons Attribution 4.0 and The Open Definition

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Campus Collab is a web-based platform designed to streamline the process of uploading, sharing, and managing academic projects for college students. It provides a centralized and structured system where students can showcase their work, find collaborators, and receive feedback from peers and faculty. Built using modern web technologies such as React.js, Node.js, Express.js, and MongoDB, the platform ensures scalability, efficiency, and an intuitive user experience. Key features include project upload and management, interactive feedback mechanisms, user profiles, and a responsive design for seamless accessibility across devices. By fostering collaboration and engagement within the academic community, Campus Collab enhances learning experiences and simplifies project management, ultimately creating a dynamic ecosystem for students and educators to interact, innovate, and grow.

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Student Collaboration, Project Sharing Platform, User Profile Management, RESTful APIs, File Upload System, Frontend and Backend Integration, CRUD Operations, JWT Authentication, MERN Stack

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Creative Commons Attribution 4.0 and The Open Definition

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Student attendance tracking is a crucial aspect of academic institutions, ensuring discipline and monitoring student participation. Traditional attendance systems, including manual roll calls and RFID-based methods, are time consuming, prone to human error, and susceptible to fraudulent practices such as proxy attendance. To address these limitations, this research proposes an automated student attendance system utilizing Faster R-CNN (Region-based Convolutional Neural Network) for efficient and accurate face detection, combined with side-angle detection to enhance recognition when students are not directly facing the camera. The system employs a highresolution camera to capture real-time classroom footage. Faster R-CNN is leveraged for fast and precise multi-face detection, ensuring robustness even in large classrooms with multiple students present. However, traditional face recognition models struggle with side-angle or partially occluded faces, leading to misidentification or missed attendance marking. To overcome this challenge, our system integrates a side-angle detection mechanism using deep learning techniques to analyze and classify facial orientations. This mechanism compensates for varying head poses by either applying pose normalization or using angle-aware embedding's, ensuring accurate recognition of students even when they are not facing the camera directly. Once a face is successfully recognized, the system crossreferences it with an existing student database and automatically updates attendance records. The processed data is securely stored in a database, providing real-time access for faculty and administrators. The proposed method significantly improves attendance accuracy, minimizes false negatives, and ensures reliability across different environmental conditions, such as varying lighting and occlusions. Additionally, the system enhances security by preventing unauthorized attendance marking and eliminating the possibility of proxy attendance. This research demonstrates that integrating Faster R-CNN with side-angle detection improves face recognition performance in classroom environments, making it a viable and efficient solution for real-world deployment. The proposed system not only automates attendance tracking but also enhances student monitoring and management in educational institutions, paving the way for intelligent classroom automation.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Face Detection, Recognition, attendance

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Creative Commons Attribution 4.0 and The Open Definition