New Horizons of Science, Technology and Culture Vol. 4

A Deep Learning System for Detecting Severe Lung Diseases including Tuberculosis, Pneumonia, and COVID-19 through Digital Chest X-rays

B. Sarada — Thu, 21 Aug 2025 00:00:00 +0000

Lung diseases are the most common and dangerous health issues worldwide. Illnesses such as Tuberculosis (TB), Pneumonia, and COVID-19 are vital to worldwide health and need accurate detection for effective treatment. In this study, a deep learning-based model was proposed for multiclass classification to automatically diagnose these illnesses using chest X-ray (CXR) images. The model employs a Convolutional Neural Network (CNN) framework that has been trained on publicly available datasets to categorise CXRs as COVID-19, Pneumonia, Tuberculosis, and No-Findings. Data pre-processing techniques were employed, including image resizing and normalisation, along with stratified data splitting. The proposed model was evaluated with an accuracy rate of 98.5%, demonstrating strong performance throughout all classes, with precision, recall, and F1-score exceeding 96%. The Pneumonia category achieved the highest recall (99.8%), while the No-Findings category showed balanced performance with 99.4% recall and a 99.2% F1-score. The findings illustrate the model's reliability for practical application in clinical decision support systems. The project's future development will focus on enhancing the model's capability to handle various types of input data, including X-rays, CT scans, and other radiological imaging formats, to boost its versatility and effectiveness in multiple diagnostic contexts.

A Comparative Review of Machine Learning and Computer Science Techniques for Optimising Healthcare Management Systems

Deepak Sharma, Jitendra Kanungo, Narendra Singh, Jitendra Raghuwanshi — Thu, 21 Aug 2025 00:00:00 +0000

The application of computer science in the management of healthcare through Information technology is changing the ways medical services are being delivered with increased efficiency, quality, and positive health impacts to the patients. This study discusses the application of concepts from computer science in healthcare management systems and the strengthening of data security measures, the effectiveness of patient observation, and the development of recommendations for clinical practice. It highlights the role of advanced algorithms—such as the Harris Hawks Optimisation Algorithm—and emerging technologies like blockchain in facilitating more secure and efficient healthcare delivery. A comprehensive literature review was conducted using four academic online databases, which are PubMed, IEEE Xplore, ScienceDirect, and Google Scholar. Four algorithms were identified as particularly relevant to integrating computer science techniques in healthcare management systems, which include the Decision Trees, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Neural Networks. The results underline the importance of the focus on interdisciplinary strategies for solving modern healthcare issues. In addition to highlighting the importance of computer science innovations in the area of healthcare, this study offers suggestions for further improvements in patient outcomes. The findings of the study indicate that it is possible for the health care industry to foster these technologies for better and more efficient, secure and responsive, to the advantage of the patient as well as the health care provider. The study also calls for future research to explore novel methodologies for further advancing the application of computer science in healthcare management.

Comparative Analysis of Amazon Web Services (AWS), Azure, and Google Cloud: Core Services, Market Strategy, and Pricing Models

Rachita, Mayank Sharma, Sahil Chopra, Bhavna Galhotra — Thu, 21 Aug 2025 00:00:00 +0000

With the advancements in the field of cloud services, companies in relation to cloud technology provide high-end services to various organisations for their best use and the functionalities of the same. For the same reason, all the cloud providers are trying to provide the best features at a limited cost for each of the services being provided to the organisation or the client. The aim of the study is to compare and check the usage and adaptability of different cloud services and cloud storage. This study presents a comprehensive comparative analysis of cloud platforms like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), focusing on key parameters such as pricing structures, service offerings, and cost-effectiveness. The primary purpose is to assist the students who are going to work with the databases, thereby enabling more informed decisions when adopting cloud-based database and storage solutions. The findings conclude that AWS remains a dominant player in the cloud computing industry, exhibiting consistent expansion. Its future potential appears robust, driven by continuous innovation, strategic partnerships, and the global shift towards digital transformation.

Ethical Challenges in Cybersecurity, Data Privacy and IP in Supply Chain Management

Deepshikha Aggarwal, Deepti Sharma, Archana B. Saxena — Thu, 21 Aug 2025 00:00:00 +0000

In today's globalised and digitally interconnected world, supply chain management (SCM) is confronted with complex ethical challenges that have far-reaching implications for businesses and their stakeholders. This study provides an in-depth exploration of three critical areas of ethical concern in SCM: data privacy, cybersecurity, and intellectual property (IP) rights. Through a comprehensive analysis that includes a review of existing literature, cross-case studies, and thematic coding of expert interviews, the study identifies and examines the multifaceted risks and challenges that organisations face in these domains. Three case studies from different industries, such as Technology, Pharmaceutical, and Automotive, were selected. Additionally, three experts were interviewed in this study. The findings highlights that third-party vendors and partners often represent significant points of vulnerability, particularly in terms of cybersecurity. The potential for data breaches and the subsequent impact on both the supply chain and customer trust necessitate rigorous third-party due diligence and proactive cybersecurity measures. Furthermore, the study underscores the importance of transparency in data privacy practices as a means of building and maintaining trust with stakeholders, especially in a regulatory environment that is increasingly focused on consumer rights and data protection. In the realm of intellectual property, the study reveals the ongoing struggle to protect proprietary information in a global supply chain context, where legal protections can vary significantly across jurisdictions. The findings emphasise the need for robust legal frameworks and vigilant enforcement to prevent IP theft, while also addressing the delicate balance between fostering innovation through collaboration and safeguarding competitive advantages. The study also identifies the development of an ethical culture within organisations as a critical factor in effectively managing these risks. Leadership commitment to ethics, coupled with regular employee training and awareness programs, is shown to be instrumental in embedding ethical considerations into daily operations. This ethical foundation not only helps in preventing ethical breaches but also supports long-term sustainability and resilience in supply chain management.

Advancing Solar Thermal Energy in Mozambique: Technologies, Barriers and Sustainable Development Pathways

Fernando Chichango — Thu, 21 Aug 2025 00:00:00 +0000

Mozambique possesses one of the highest solar irradiation potentials in Southern Africa, offering a strategic opportunity for the development of solar thermal energy technologies. This study explores the status, challenges, and prospects of solar thermal energy in Mozambique, emphasising its role in sustainable building heating. Despite favourable natural conditions, the adoption of solar thermal systems remains limited due to economic, technical, and institutional barriers. The paper highlights successful case studies, such as the SOLTRAIN initiative, and discusses innovative approaches using alternative materials to reduce costs. It concludes by recommending targeted policies, local capacity building, and technological innovation to unlock the full potential of solar thermal energy in Mozambique.

IoT-Enabled Smart Lighting Control System with Motion Detection Using Passive Infrared Sensor Technology

Thu, 21 Aug 2025 00:00:00 +0000

The energy sector is one of the major contributors to global carbon emissions. Numerous approaches have been adopted to reduce dependence on fossil-based energy by incorporating renewable energy sources. However, energy wastage at the end-user level has not been fully addressed. With recent advancements in the Internet of Things (IoT), automation and autonomous control systems now have the potential to regulate human energy consumption effectively. This study, therefore, investigates the design and implementation of an IoT-enabled smart lighting control system based on real-time occupancy detection using Passive Infrared (PIR) sensors. These sensors detect human motion, allowing the system to intelligently manage lighting by switching lights on only when necessary and adjusting brightness levels based on movement. This dynamic control minimizes energy waste in unoccupied areas and results in significant electricity savings through optimized power consumption. Beyond energy efficiency, the system also improves user convenience and comfort by providing seamless, hands-free lighting control, with a response accuracy of 98% within 5m range. This feature is particularly beneficial for individuals with mobility limitations or in scenarios that require instant lighting, such as navigating dark or cluttered spaces. Additionally, IoT connectivity enhances the system’s capabilities, enabling users to remotely monitor, adjust, and schedule lighting settings, offering greater flexibility and customization to suit individual preferences. The scalable and adaptable nature of the system makes it a practical, user-friendly solution for various smart home environments. Overall, IoT-based smart lighting systems using PIR sensors provide a dual advantage: reducing energy consumption and enhancing user interaction with lighting technologies.

Microwave Absorption and Shielding Mechanisms in Micro-cellular Foamed Conductive Composites

Isabelle Huynen — Thu, 21 Aug 2025 00:00:00 +0000

Protection against electromagnetic interference (EMI) has remained a pervasive challenge for almost two decades. For these reasons, research efforts have focused on the design and fabrication of efficient EMI shielding materials. This chapter investigates the mechanisms of microwave absorption in microcellular foamed conductive composites designed for protection or shielding against electromagnetic interference. A multi-layered electromagnetic one-dimensional (1D) model mimicking the microcellular foam structure is built and validated using previous measurements on various fabricated composite foams. This model helps to perform a parametric analysis of the absorption behaviour in a foamed composite, using the following parameters: the size of the hollow cell, the thickness of the cell’s wall, its conductivity, the overall thickness of the composite, and the frequency. The foamed composite materials that serve as a reference for the validation of the proposed model were fabricated using a supercritical CO2 process. Three different polymer matrices were considered as examples in this work, i.e., polycarbonate (PC), poly-caprolactone (PCL), and polypropylene (PP). These investigations demonstrate that multiple reflections of the microwave signal between the cellular walls are not the main mechanism responsible for absorption. However, they are often reported as a cause of enhanced absorption in the literature. On the contrary, this work demonstrates that the enhancement of the absorption observed in the foamed conductive composite compared to the unfoamed composite is mainly due to the presence of air in the micro-cells of the composite. The associated electromagnetic shielding efficiency of the foams for practical applications is also discussed. As a whole, the chapter provides a comprehensive study of the performance of composite foams that can help to protect modern communications systems, IoT devices, and living systems from spurious electromagnetic radiations that can interfere with.

Assessing OTT Platform Efficacy Using Data Science and Data Mining Techniques

Shivani Vats, Disha Grover — Thu, 21 Aug 2025 00:00:00 +0000

In the digital era, where internet connectivity shapes lifestyles and choices, traditional modes of entertainment—such as cable television and movie theatres—are gradually witnessing a decline in relevance. In their place, Over-the-Top (OTT) platforms have emerged as the dominant medium of content consumption for a vast segment of the global population. These platforms offer on-demand access to a vast array of content across genres, languages, and formats, redefining how individuals engage with entertainment. Despite their growing ubiquity, a surprising segment of the population—approximately 20%—remains unfamiliar with the term "OTT platforms." Interestingly, nearly half of this segment engages with such services unknowingly through well-known brands like Netflix, Amazon Prime Video, and Disney+, indicating a gap in conceptual understanding despite regular usage.

This research paper aims to bridge that knowledge gap and contribute to the broader understanding of OTT platforms from both a consumer and analytical standpoint. The study adopts a data-driven approach, utilising data collected through convenience sampling via structured surveys to assess the depth of public awareness, usage frequency, platform preference, and behavioural trends related to OTT services. It explores consumer sentiment, viewing patterns, subscription tendencies, and satisfaction levels.

Furthermore, the paper employs data science and data mining techniques, including machine learning algorithms, to extract meaningful insights from the data. It analyses the correlation between consumer preferences and the performance of different OTT platforms, revealing how content variety, pricing strategies, user interface, and personalisation influence user loyalty and platform success. Sentiment analysis is used to evaluate the public's perception—both positive and negative—about these platforms, underlining the dual nature of technological advancements. By examining these facets, the study not only offers actionable insights for OTT service providers and marketers but also seeks to educate uninformed users about the evolving digital entertainment landscape. This research underlines the growing necessity of digital literacy in media consumption and highlights the transformative impact of data science in decoding user behaviour and platform dynamics in the entertainment industry.

Identifying the Type of ARP Request to Introduce the MAC Address Table Instability Results in Network Sensitivity

Md. Abdullah Yusuf Imam, Prodip Kumar Biswas — Thu, 21 Aug 2025 00:00:00 +0000

In network analysis, "looping" or "network loops" refers to situations where a path in a network returns to the same node or nodes multiple times, creating a closed circuit or cycle. Looping first creates a broadcast and then a broadcast storm, and then creates network instability. It causes network jam and unavailability. In a loop, a single ARP (ARP or Address Resolution Protocol) is a networking protocol that translates Internet Protocol (IP) addresses to Media Access Control (MAC) addresses within a local area network (LAN). This translation is crucial because devices on a network use IP addresses to identify each other, but communication at the physical level relies on the MAC address. This can occur in various network contexts, including project management, computer networks, and electrical circuits. Loops occur when a path traverses the same node twice or more. Looping in Computer Programming can be stated as a "loop" is a sequence of instructions that is repeatedly executed until a certain condition is met. This study introduces the varieties of looping criteria where the ARP is infected first, and after that effect of its network smoothness, and also how it can be avoided, is tried to show in Computer technology. This study outlines steps for cancelling loops in networks, emphasising proper network design, the use of virus-free systems, and loop-free configurations to ensure smooth network operations.

Enhancing Indoor Cooling Prediction Using Empirical Models Incorporating Occupancy and Humidity

Ashish Madhukar Jadhav, Omkar Jadhav, Poonam Ranpise — Thu, 21 Aug 2025 00:00:00 +0000

Indoor air temperature is one of the key factors for maintaining the indoor air quality, energy consumption and optimum moisture. Accurate prediction of indoor temperature is crucial for optimising energy use and ensuring thermal comfort in air-conditioned environments. The study presents an empirical approach to model the cooling behaviour of a controlled room under varying conditions of air conditioner (AC) setpoint, occupancy, and humidity. While previous studies have often focused on simplified linear or Newtonian cooling models, most have neglected the combined effects of humidity, occupancy, and AC setpoint on cooling dynamics, resulting in limited real-world applicability. Three predictive models, linear, exponential (Newtonian cooling), and empirical, were developed from the experimental data collected for the time taken for every 0.5°. A drop in room temperature. Each model attempts to estimate the cooling rate and predict room temperature at different time intervals, which allows us to determine and compare Newton's coefficient of cooling under each condition. The experimental design involved controlled cooling sessions using a standard air conditioner with setpoints fixed at 15 °C, 20 °C, and 25 °C in separate trials. A quantitative comparison of the three models under each setpoint and occupancy condition was done using statistical analysis. The empirical model, which incorporates humidity, occupancy, and room volume, demonstrated superior accuracy over traditional linear and exponential models, as evidenced by lower mean error and root mean square error (RMSE), and a higher coefficient of determination (R²). The empirical model showed excellent agreement with actual observations, with a mean percentage deviation of just 10.3%, a root mean square error (RMSE) of 0.15 deg. C, and a high R² value of 0.97. It successfully predicted the cooling time within – 20 to +20 seconds and accurately captured the cooling coefficient trends with respect to temperature setpoint and occupancy. The study establishes a reliable framework for predictive climate control based on real-world thermal interactions. By accurately predicting the room cooling behaviour under varying human occupancy and humidity levels, the system can make informed decisions for optimised air conditioner operation, thereby enhancing energy efficiency.

The Role of Artificial Intelligence Techniques in Advancing Agroforestry Systems: A Review

Sameer Daniel — Thu, 21 Aug 2025 00:00:00 +0000

Agriculture plays a crucial role in human survival as a primary source of food, alongside other sources. The introduction of Artificial Intelligence is constantly transforming the present age of agroforestry. It holds significant potential to enhance the sustainability of the agricultural industry through various applications. This review explores about use of artificial intelligence techniques for agroforestry. Agroforestry is an intensive and interactive land usage strategy that maximises biotic and abiotic resources by deliberately combining trees and/or shrubs with crops and/or animals in temporal and spatial patterns on the same plot of land. Agroforestry is a self-sustaining, green and smart technology that will transform the future of Indian agriculture. Artificial intelligence is a powerful technology that encompasses computers and machines to simulate the intelligence of humans to solve specific problems on the basis of logical reasoning and fast experience. AI-powered agroforestry plays a critical role in data collecting, processing, assessment, interpretation, knowledge acquisition, and solution provision to improve overall production and efficiency. It is essential to understand the complexity of the Agroforestry system, cropping patterns, succession, stratification, productivity and biodiversity on the land. However, a larger workforce is required to increase the farm productivity which also enables employment opportunities and smart work in a reconnection with nature. Thus, AI-enabled solutions are extremely valuable in crop cultivation, risk management, crop management, crop protection, crop advice, soil and crop health monitoring and management, crop feeding, automated irrigation, autonomous crop harvesting, crop grading, and even marketing. It will transform contemporary agroforestry methods by enhancing efficiency through accurate real-time monitoring and projections of increased food yields. Thus, the combination of AI, robotics, machine learning, and ancestral knowledge is the path to a transformational technological period that will renew agriculture and agroforestry throughout the world by encompassing varied crops and livestock species. This is also known as Smart Farming, Green Farming, Modern Farming, or Technical Farming. AI systems should be developed and deployed with consideration for local communities, indigenous knowledge, and historically marginalised groups. A sound theoretical framework is a useful basis for guiding the development of specific technical applications of artificial intelligence.