Recent Articles

Gloriya Glinto T, Anjana Suresh, Harinanda Mohandas  ·  International Journal of Technology and Emerging Research  ·  22 Aug 2026

Abstract [1], [2].Energy management in Higher Education Institutions (HEIs) has become increasingly important due to rising electricity consumption, escalating operational costs, and the global demand for sustainable development.Conventional Building Energy Management Systems (BEMS)primarily rely on centralized and rule-based control mechanisms, limiting their ability to adapt to the dynamic and complex environments of modern educational campuses. This paper proposes CampusGrid AI, an intelligent multi-agent framework for predictive energy management and autonomous electricity optimization in HEIs. The proposed framework integrates the Internet of Things (IoT) for real-time environmental sensing, Machine Learning (ML) for accurate energy demand forecasting, and Multi-Agent Reinforcement Learning (MARL) for autonomous and collaborative decision-making. A Digital Twin technology is incorporated to create a virtual representation of campus infrastructure, enabling safe simulation, system optimization, and predictive analysis without disrupting real-world operations. Furthermore, Explainable Artificial Intelligence (XAI) is integrated to provide transparent, interpretable, and trustworthy decision support for administrators and stakeholders. The framework employs interconnected intelligent agents to continuously monitor classrooms, laboratories, libraries, hostels, and administrative buildings, dynamically controlling electrical appliances such as lighting, air-conditioning, and laboratory equipment while maintaining occupant comfort and operational efficiency. By combining cloud computing, edge computing, and IoT technologies within a unified architecture, CampusGrid AI enables real-time monitoring, predictive analytics, autonomous energy optimization, and sustainable resource management. The proposed framework has the potential to significantly reduce electricity consumption, operational costs, and carbon emissions while improving energy efficiency and supporting the development of intelligent and sustainable higher education campuses.

Niranjana K S, Sidha P P, Anjali G, Akhila K  ·  International Journal of Technology and Emerging Research  ·  22 Aug 2026

Spatial domain image steganography has emerged as one of the most widely adopted information-hiding techniques due to its simplicity, high embedding capacity, low computational complexity, and ability to preserve the visual quality of digital images. It plays a significant role in secure communication by concealing confidential information within digital images, thereby protecting sensitive data from unauthorized access in applications such as healthcare, military communication, banking, cloud computing, digital forensics, and multimedia systems. Despite these advantages, spatial domain techniques face several challenges, including vulnerability to steganalysis, limited robustness against image processing operations, and the trade-off between embedding capacity and imperceptibility. This paper presents a comprehensive review of spatial domain image steganography by examining its historical development, fundamental concepts, classification, and major techniques, including Least Significant Bit (LSB), Adaptive LSB, Pixel Value Differencing (PVD), Pixel Indicator Technique (PIT), Optimal Pixel Adjustment Process (OPAP), edge-based methods, and other adaptive spatial approaches. The reviewed techniques are comparatively analyzed based on embedding capacity, imperceptibility, robustness, computational complexity, and practical applicability, and are further illustrated through a quantitative case study that evaluates PSNR, SSIM, and MSE for LSB substitution on a standard test image. The study identifies existing research gaps and highlights future research directions aimed at improving robustness, visual quality, and embedding capacity. By providing a structured and critical synthesis of existing literature, this review serves as a valuable reference for researchers, academicians, and practitioners working in the field of digital image security and information hiding.

Sowmya K S  ·  International Journal of Technology and Emerging Research  ·  22 Aug 2026

The rapid growth of the Internet of Things (IoT) has transformed conventional devices into intelligent interconnected systems, creating new opportunities for solving everyday challenges through smart technologies. One common problem faced by individuals is the frequent misplacement of personal belongings such as car keys, television remotes, wallets, books, pens, bags, laptops, and other valuable items, resulting in wasted time and reduced productivity. This paper proposes an IoT-Based Smart Universal Finder, an intelligent, low-cost, and energy-efficient object tracking framework that integrates Bluetooth Low Energy (BLE), cloud computing, mobile application technology, and Artificial Intelligence (AI) to provide reliable real-time object localization. The proposed system employs compact BLE-enabled smart tags attached to personal belongings, where each tag possesses a unique identification number and communicates securely with a smartphone application. When an object is misplaced, the user selects the corresponding registered device within the mobile application, which transmits a BLE command to activate the smart tag's buzzer, LED indicator, or vibration motor, thereby enabling rapid object identification. In addition to basic object tracking, the proposed framework incorporates intelligent features such as last known location storage, battery health monitoring, geofencing alerts, anti-theft notifications, family sharing, cloud synchronization, and AI-based prediction of frequently misplaced objects using historical usage patterns. A prototype-based performance evaluation demonstrates an object detection accuracy of approximately 97.2%, an average response time of 1.6 seconds, and an estimated battery life of 11 months under normal operating conditions. Comparative analysis with existing commercial solutions, including Apple AirTag, Samsung SmartTag, and Tile Tracker, indicates that the proposed Smart Universal Finder provides enhanced functionality, greater flexibility, lower implementation cost, and improved scalability. The proposed framework offers a practical solution for smart homes, educational institutions, offices, healthcare facilities, libraries, and industrial environments, while contributing to the advancement of intelligent IoT-based asset management systems and future smart-city applications.

Parvathy Krishna V, Gayathri A S, Sahala Mehrin, Arya V, Fathima Hyfa  ·  International Journal of Technology and Emerging Research  ·  21 Aug 2026

Water pollution poses a severe threat to global public health, aquatic biodiversity, and sustainable resource management. Traditional monitoring methods rely on manual sample collection and laboratory analysis, which are labor-intensive, time-consuming, and fail to provide early warning capabilities. This paper proposes an end-to-end Internet of Things (IoT) framework designed for real-time water quality monitoring and predictive pollution modeling. The framework integrates a network of low-power, multisensor edge nodes deployed across aquatic bodies to measure key parameters including pH, turbidity, dissolved oxygen (DO), total dissolved solids (TDS), and temperature. Data collected from the sensor layer is transmitted via low-power wide-area network protocols (LoRaWAN/MQTT) to a centralized cloud analytics platform. To enable proactive environmental management, a hybrid machine learning architecture—combining Long Short-Term Memory (LSTM) networks for time-series forecasting and Random Forest models for anomaly classification—is proposed to predict spatial-temporal pollution trends and identify illegal dumping events before severe contamination occurs. This paper proposes a conceptual architecture intended to guide future implementation and validation

Prapitha Gopi K  ·  International Journal of Technology and Emerging Research  ·  21 Aug 2026

Information retrieval has changed dramatically over the past decades. Early systems relied on simple keyword matching, but modern search engines must understand meaning, context, and user intent. This paper examines three major families of retrieval models that have shaped this evolution: vector space models, probabilistic retrieval, and neural retrieval. Vector space models represent documents and queries as weighted term vectors and rank them by similarity, providing a simple yet effective way to handle partial matches. Probabilistic models, such as BM25, treat relevance as a probability and rank documents according to how likely they are to satisfy a query, offering a stronger theoretical foundation for ranking. Neural retrieval goes further by learning dense semantic representations that can capture meaning beyond exact word overlap, enabling more accurate matching and reranking. We review key works including Salton et al.’s foundational vector space model, Robertson and Zaragoza’s probabilistic relevance framework, and recent neural approaches such as Dense Passage Retrieval and large language model-based retrieval surveys. The discussion shows that modern search systems rarely rely on a single model. Instead, they combine fast lexical retrieval with powerful neural rerankers to balance speed and accuracy. This hybrid approach reflects the current state of the field and points toward future research directions.

Reshmi V  ·  International Journal of Technology and Emerging Research  ·  21 Aug 2026

Abstract People across India increasingly use regional languages online to share opinions, reviews, and reactions, but understanding the sentiment behind this text is not easy. Indian regional languages are often used in informal ways, mixed with English, written in different scripts, and supported by only a small number of labeled datasets and language tools. This review paper looks at the main data mining methods used for sentiment analysis in Indian regional languages, including machine learning, lexicon-based, rule-based, deep learning, and transformer-based approaches. It compares how these methods have been used in previous studies and highlights what they do well and where they struggle. The review shows that simple machine learning methods can still be useful when data is limited, while deep learning and transformer models are more effective when better resources are available. Even so, many problems remain, especially code-mixed text, transliteration, sarcasm, negation, and the lack of reliable benchmarks across languages. The paper concludes that future progress depends on building richer datasets, improving language-specific preprocessing, and designing models that are both accurate and practical for real-world use.

VISMAYA C D, ANAMIKA V P, FATHIMA JURIYA, JAHANA SHIRIN TJ, GOURI KRISHNA P S  ·  International Journal of Technology and Emerging Research  ·  21 Aug 2026

The rapid growth of cloud computing, artificial intelligence (AI), big data analytics, and Internet of Things (IoT) applications has significantly increased the energy consumption of modern data centers. A substantial portion of this energy is converted into waste heat, requiring extensive cooling systems that consume additional power and reduce overall energy efficiency. Although several waste heat recovery technologies have been developed, most existing approaches primarily focus on recovering thermal energy and provide limited support for intelligent heat utilization and decision-making. This paper proposes an AI-Based Sustainable Data Center Heat Recovery Framework that integrates IoT sensors, cloud computing, data preprocessing, Random Forest Regression, and AI-based decision-making into a unified intelligent energy management system. The proposed framework continuously monitors operational parameters, predicts future heat generation, and recommends the most suitable heat recovery application based on operational conditions and demand. By combining predictive analytics with intelligent heat distribution, the framework improves energy efficiency, reduces cooling costs, minimizes carbon emissions, and enhances the sustainable utilization of recovered thermal energy. The proposed system provides a scalable and intelligent approach for developing energy-efficient and environmentally sustainable data center infrastructures.

Sajeevkrishnan M. A, Arifa P  ·  International Journal of Technology and Emerging Research  ·  21 Aug 2026

Abstract Quantum computing threatens the public-key cryptography on which contemporary digital infrastructure depends, and the practical question has shifted from whether that threat will materialise to how quickly organisations are obliged to respond. Two developments drove this shift: the United States National Institute of Standards and Technology published its first post-quantum cryptographic standards in August 2024, and in June 2026 an executive order attached binding dates to their adoption across federal systems and federal contractors. This review examines those developments through targeted retrieval of primary standards and policy documents, together with peer-reviewed cryptanalytic and resource-estimation literature, with bibliographic details verified against publisher records. Three principal findings emerge. First, the standardised portfolio is smaller than commonly reported: three Federal Information Processing Standards are published, a fourth remains in development, and a fifth algorithm has been selected but not yet standardised, a distinction that matters because procurement obligations attach only to published standards. Second, two candidates that reached advanced evaluation were defeated by classical rather than quantum cryptanalysis, one within a weekend on consumer hardware. Third, published estimates of the quantum resources required to break widely deployed key sizes have moved by more than an order of magnitude in a single dimension within six years, exchanging machine size for running time. The review concludes that cryptographic agility, not the selection of any particular algorithm, is the durable objective, and that migration should be prioritised by the confidentiality lifetime of data rather than by forecasts of adversary capability.

Jeslin Jaison A, Huda K, Fathima Rifa, Anagha Krishna, Malavika K S  ·  International Journal of Technology and Emerging Research  ·  21 Aug 2026

Cloud computing has become an essential part of modern IT infrastructure by delivering applications and services over the Internet. This paper presents a review of cloud computing applications and their role in transforming various industries. Applications of cloud computing include software as a service for productivity tools, platform as a service for application development, and infrastructure as a service for data centers. It is extensively used in sectors such as education, healthcare, finance, government, and entertainment. The adoption of cloud applications provides organizations with flexibility, reduced operational costs, improved collaboration, and efficient resource utilization. The study concludes that cloud computing applications are key drivers of digital transformation.

Anamika V J, Aleena K Rasheed  ·  International Journal of Technology and Emerging Research  ·  21 Aug 2026

Quantum computing has emerged as a transformative technology capable of solving complex computational problems beyond the capabilities of classical systems. However, severe hardware limitations, qubit decoherence, and high implementation costs prevent it from replacing classical cloud infrastructure. Instead, cloud and quantum computing are evolving as complementary technologies. Through Quantum-as-a-Service (QaaS) and hybrid cloud–quantum architectures, cloud platforms provide scalable infrastructure, data management, and resource orchestration, while quantum processors accelerate specialized computational tasks. This review systematically examines the relationship between cloud computing and quantum computing by analyzing their technical differences, capabilities, application areas, current challenges, limitations, and future research directions. The findings indicate that cloud computing will continue to serve as the backbone of modern digital infrastructure, while quantum computing will enhance its capabilities for solving complex computational problems. Together, these technologies are expected to drive the next generation of intelligent, scalable, and high-performance computing systems.

Jewel Johnson, Krishnapriya C S, Raniya D T, Asna P V, Niranjana V  ·  International Journal of Technology and Emerging Research  ·  21 Aug 2026

The increasing demand for heating and cooling in buildings, driven by rapid urbanization and climate change, has led to higher energy consumption and carbon emissions. Improving thermal energy efficiency has therefore become an important objective in sustainable building design. This paper proposes the conceptual design of an Artificial Intelligence (AI)-assisted Phase Change Material (PCM) thermal panel for residential and commercial buildings. The proposed system combines encapsulated PCMs with embedded temperature sensors and an AI-assisted control framework to improve indoor thermal regulation. During periods of high temperature, the PCM absorbs and stores excess heat, while during cooler conditions it releases the stored heat, thereby maintaining a more stable indoor temperature and reducing dependence on conventional heating, ventilation, and air-conditioning (HVAC) systems. The AI component continuously analyzes environmental parameters such as indoor and outdoor temperature, humidity, occupancy, and weather conditions to support intelligent thermal management and optimize panel operation under different climatic conditions. This paper presents the conceptual architecture, operating principle, and expected performance of the proposed system based on existing research in PCM technology and AI-assisted energy management. The proposed design is expected to enhance thermal comfort, improve building energy efficiency, reduce electricity consumption and carbon emissions, and support the development of sustainable and smart building infrastructure.

Mariena A A  ·  International Journal of Technology and Emerging Research  ·  21 Aug 2026

Image segmentation plays a significant role in medical image analysis by partitioning an image into meaningful regions that facilitate the identification and analysis of anatomical structures. It remains an active area of research due to the numerous challenges associated with accurately partitioning images into distinct regions. Accurate segmentation of blood smear images is essential for computer-aided diagnosis, disease detection, and hematological analysis. This study presents a comparative performance analysis of two widely used image segmentation techniques, namely Region-Based Segmentation and Watershed Segmentation, using blood smear images. The segmentation performance is quantitatively evaluated using standard metrics, including the Jaccard Coefficient, Dice Coefficient, True Positive Rate (TPR), and True Negative Rate (TNR).

Gopika K M, Archana K A, Rodha K A, Jyothika K S, Ayisha Nazwa M R  ·  International Journal of Technology and Emerging Research  ·  21 Aug 2026

Rapid Infrastructure development often has unwanted climate effects, emissions, biodiversity loss, and resource consumption due to fragmented and reactive environmental impact assessments. This research introduces Climate Decision AI, a global multi-criteria decision support system that assesses climate, environment, community, and economic impact before construction using satellite imagery, climate data, socio-economic indicators, and machine learning. The system employs Digital Twins, Predictive Analytics, and XAI to ensure projects are consistent with the UN Sustainable Development Goals (SDGs) 6, 9, 11, 13, 15, and 17. For instance, it will provide global development banks and national planning authorities with a comprehensive digital transformation platform that promotes transparency in decision-making for climate action and sustainable development.

LISNA THOMAS, SAVIYA VARGHESE  ·  International Journal of Technology and Emerging Research  ·  21 Aug 2026

The quality and freshness of fish and seafood are crucial factors for ensuring their safety, preventing seafood fraud, and sustainable consumption. The latest achievements in computer vision, Internet of Things (IoT), and artificial intelligence technologies allow for automated evaluation of fish quality. Solutions that can identify fish species and predict its freshness at the point of purchase by end-users remain rare. In this paper, we propose FishID+ – a consumer-oriented and portable solution that incorporates deep learning, multi-sensor IoT, and Explainable Artificial Intelligence (XAI) for real-time fish species identification and freshness prediction. For identifying fish species, our system uses convolutional neural network (CNN) trained on smartphone photos. Our IoT system is based on an ESP32 microcontroller and includes sensors for measuring NH₃ concentration, volatile organic compounds (VOCs), temperature, and humidity. The collected information is classified in a lightweight machine learning algorithm that categorizes the fish into Fresh, Consume Soon, or Spoiled state. Moreover, using SHAP-based explanations, the system identifies which of visual and IoT features have a higher contribution in final decision. The results of prediction, the freshness score, and recommendations are shown through a mobile application, enabling consumers to make informed purchasing decisions without requiring laboratory testing. By integrating species recognition and freshness assessment into a single intelligent platform, FishID+ addresses current limitations in existing seafood monitoring systems. The proposed framework is expected to reduce household food waste, minimize foodborne health risks, enhance consumer confidence, and support sustainable food safety through accessible, explainable, and intelligent IoT technologies.

Dr. Hitha Paulson  ·  International Journal of Technology and Emerging Research  ·  20 Aug 2026

FIFA's Football AI Pro, developed with Official Technology Partner Lenovo and deployed at the 2026 FIFA World Cup, is a generative artificial intelligence assistant built on a domain-specific Football Language Model and provided to all 48 competing national teams. This paper presents a computational evaluation of Football AI Pro based on publicly disclosed technical and operational information, since no independent access to the system or its training data is currently available. A structured evaluation framework is applied across six dimensions: data scale and coverage, architectural sophistication, explainability, accessibility, operational risk controls and data governance transparency. The evaluation suggests that the platform is credible mainly because it uses FIFA's large proprietary dataset. This dataset is larger and more consistent than those used in most football machine learning research. However, there is no independent evidence to confirm its explainability and governance claims. The paper concludes that Football AI Pro is a reliable and well-developed generative AI platform for sports analytics. However, its evaluation is based mainly on claims made by the company, not on independent technical testing.

Feyisa Lemessa  ·  International Journal of Civil and Environmental Engineering  ·  19 Aug 2026

The study was conducted in Moyale and Yabello districts, which is situated in Borana zone of the Oromia Regional State. The objectives of this study was to assess the availability of major browse species used for camel feed resources and to characterize its nutritional value/chemical composition. The study was conducted in four purposively selected kebeles focusing on 90 randomly selected camel-owning households. According to the perception of pastoralists/agro-pastoralists browses plants are the main sources of camel feed throughout of the year. Different browse plants such as trees, shrubs and forbs were collected and analyzed for chemical composition. According to the perception of pastoralists camel supplementation practise for the purpose of feed shortage and fattening. According to the perception of pastoralist 45% and 43% of camel feed resource decreasing in Moyale and Yabello district. According to the perception of pastoralists, the availability of feed resources varies across the dry and wet season. The highest feed resources during long dry season in Moyale and Yabello is tree leaves and forbs respectively. Bosciamossambicensi, Vechilla Seyal, Sarcostemmaviminale, Dalbergiamicrophylla, Vechilla etbaica, Grewia tembensis, Premnaschimperi, Phyllanthussepialis, Baleriaspinisepalo, Vechilla brevispica, Harmsiasidoides and Rhus natalensis were major browses plants collected and analyze their chemical composition. Results indicate that the dry matter content of this browses plants varies from 88.9 to 93.6.%. The highest dry matter content was recorded in Vechilla brevispica (95.6%) and the lowest in Harmsiasidoides (88.86) browses plants. Accordingly all the browses plants except Sarcostemmaviminale (5.71%), Vechilla brevispica (9.12%) and Vechilla seyal (8.8%) plants has good crude protein content among the collected and analyzed browses specie. Premnaschimper (25.6%), Rhus natalensis(19.99%) and Phyllanthussepialis(19.12%) has highest contents of the crude protein than browse plants analyzed. Premnaschimperi, Harmsiasidoides, Phyllanthussepialis and Dalbergiamicrophylla has high total invitro organic matter digestibility among the browses species compared to other browses speicies. Baleriaspinisepalo has the lowest total invitro organic matter digestibility as compared to the other browses specie. A range of 3.11 to 16.20% and 83.6 to 96.7% were recorded for Ash and Organic matter values for the twelve browse plants. Their fibre contents showed a range of 22.1 to 51.13, 19.9 to 44.9, 3.2 to 19.5.%, for NDF, ADF, ADL respectively. Baleriaspinisepalo has highest neutral detergent fiber(57.5%) and acid detergent fiber (52.5%) among other browses plants. Generally, according to the perception of community and the nutritive value(chemical composition) of this browses plants have potential for increase camel production but the productivity of browsing camels greatly depends upon accessibility of the browse and season. This study confirms that browses plants have potential for increased camel production but has shown that the magnitude of the contribution to the productivity browsers greatly depends upon accessibility of the browse.

Praveen  ·  International Journal of Communication, Media and Linguistics  ·  16 Aug 2026

Despite decades of expansion in Indian journalism, women remain structurally underrepresented as news subjects, sources, bylined writers, television panellists, and newsroom decision-makers, and this gap is increasingly compounded by unequal digital access and rising online violence that make sustained participation in the profession harder rather than easier. This paper synthesises evidence from major cross-national and India-specific monitoring studies published between 2019 and 2025 to assess whether this disparity has narrowed and whether these compounding barriers have eased. Drawing on the Global Media Monitoring Project, UN Women-Newslaundry newsroom audits, the GSMA Mobile Gender Gap Report, and UNESCO-ICFJ studies on online violence, the review finds that women accounted for just 14% of news subjects and sources in India in the most recent nationally coordinated count, down from 22% in 2010; that women wrote roughly one in four print bylines and held almost no top editorial positions at sampled newspapers; that the South Asian mobile internet gender gap stood at 32% in 2024, more than double the global low- and middle-income country average; and that 75% of women journalists surveyed globally in 2025 reported online violence, up from 73% in 2020, with reports of offline spillover more than doubling. The paper concludes that gender disparity in Indian journalism has not meaningfully narrowed and, on several indicators, is worsening, and argues that closing this gap sustainably requires media organisations to combine leadership reform with digital-safety and connectivity measures, an approach for which women-led outlets such as Khabar Lahariya offer a working model.

Maidhili Mohan, Aysha M. K.  ·  International Journal of Technology and Emerging Research  ·  16 Aug 2026

Industrial control systems (ICS) are fundamental to critical infrastructures such as electrical grids, water processing plants, and manufacturing plant, where Programmable Logic Controllers (PLCs) and Supervisory Control and Data Acquisition (SCADA) systems regulate physical processes that cannot stop operating at ease. These formerly isolated systems acquire a greater variety of challenges as they become interconnected with IT networks, including command-injection, replay, and false-data injection attacks that may actually result in serious physical harm in addition to some data loss. Two defense methods that have been evolved are discussed in this review. By integrating a private random signal on the control command, dynamic watermarking (DW) takes on an active, physics-based approach resulting in any anomaly in the sensor-actuator feedback loop becoming statistically visible. In contrast, machine learning (ML) approaches try to learn what malicious behavior appears from data. We keep track of the innovations in both directions to distinguish their supplementing powers and blind spots, as well as look at the simple but growing set of work that attempts to bring them together. Also we employed the case studies from PLC-controlled water-tank testbeds, networked control systems, and power-system automatic generation control. The paper ends by discussing the barriers that prevent these research findings from being put into practice, including the ability to scale, adversarial robustness, and real-time deployment on legacy PLC hardware.

Dr. Rakesh Kumar Yadav, Dr. Nabin Kumar Yadav, Dr. Bibha Yadav  ·  International Journal of Medical and Health Sciences  ·  16 Aug 2026

Surgical site infections (SSIs) remain one of the most common and serious postoperative complications, contributing to increased patient morbidity, prolonged hospital stays, and elevated healthcare costs. Early and accurate diagnosis, combined with timely intervention, is essential to improve outcomes. Radiology plays a pivotal role in both diagnosing and managing SSIs through various imaging modalities and interventional procedures. This review highlights the comprehensive role of radiology in detecting and treating SSIs, focusing on the use of ultrasound, computed tomography (CT), and magnetic resonance imaging (MRI) across general surgery, orthopedics, gynecology, and cardiovascular specialties. Ultrasound is valuable for assessing superficial wounds and guiding fluid aspiration; CT offers high-resolution visualization of deep collections and surgical complications; MRI excels in evaluating soft tissue and bone infections, especially around prostheses or in complex regions like the pelvis or spine. Interventional radiology enables percutaneous drainage, abscess aspiration, and catheter placement, offering a minimally invasive alternative to surgical re-intervention. Proper imaging interpretation helps distinguish normal postoperative findings from infection, aiding in accurate diagnosis and targeted management. Overall, radiology significantly enhances postoperative infection control, and its integration into multidisciplinary care is critical for improving surgical outcomes.

Nandini Mishra  ·  International Journal of Philosophy, Ethics and Humanities  ·  16 Aug 2026

The question of consciousness remains one of the most challenging issues in contemporary philosophy. David Chalmers has transformed the issue through his formulation of the "Hard Problem of Consciousness," which distinguishes subjective experience from cognitive and behavioral functions. Chalmers's philosophy of consciousness, focusing on the explanatory gap between physical processes and phenomenal experience. It analyzes reductive materialism, explores his concept of naturalistic dualism, and evaluates major criticisms from functionalist perspectives. The study continues to provide an indispensable framework for understanding consciousness and the limitations of contemporary scientific explanations. Keywords: Consciousness, Naturalistic Dualism, Philosophy of Mind, Phenomenal Experience