Recent Articles

Mohammed Aboud Kadhim, Dr. Kamal Y. Kamal  ·  International Journal of Technology & Emerging Research  ·  01 Aug 2025

Internet of Things (IoT) systems require localization for the nodes within Wireless Sensor Networks (WSNs) for many location-based services. Since thousands of sensor nodes would exist in some networks, having GPS on each node is impractical, not merely due to the hardware cost, but also because of the poor performance indoors. Localization is now recognized as a crucial area for study. The research presented in this paper puts forward a half-measure weighted centroid DV-Hop localization algorithm. The proposed algorithm adjusts the locations of unknown nodes using redundant information obtained from localization equations. Simulation of sensor networks resulted in significant improvements in accuracy and lower error rates in localization estimation, while maintaining low hardware and computational costs.

Pooja kukreja, Avani Chopra  ·  International Journal of Technology & Emerging Research  ·  01 Aug 2025

Skin cancer is one of the most frequent and potentially fatal malignancies worldwide, emphasizing the significance of early and correct detection. Convolutional neural networks (CNNs), a recent deep learning advancement, have shown promise in automating the classification of skin lesions. This study employed EfficientNet-B0, a lightweight yet very effective CNN architecture, to train a reliable multi-class classification model on the HAM10000 dermoscopic picture dataset. To ensure compatibility with the pre-trained network, all input images were resized to 224 x 224 pixels and normalized using the ImageNet mean and standard deviation values. To rectify the class imbalance, the majority class (melanocytic nevi) was lowered to 1300 samples, while underrepresented categories (actinic keratoses, basal cell carcinoma, dermatofibroma, and vascular lesions) were oversampled with 1000 samples each. This preprocessing resulted in a balanced collection of 7512 pictures organized into seven diagnostic groups. Transfer learning was originally utilized to achieve a 77.39% accuracy by freezing the convolutional basis and training only the final classification layer. After fine-tuning the entire network, the accuracy improved to 89.36%. Test-time augmentation with flips enhanced performance to 90.16%, while combining TTA with Monte Carlo Dropout and additional augmentations increased final accuracy to 92.29%. The results highlight EfficientNet-B0's potential. By assisting medical professionals with early diagnosis, this study's improved classification model for skin lesion detection can improve patient care and reduce strain on healthcare systems.

Dr. R. Naveen  ·  International Journal of Technology & Emerging Research  ·  30 Jul 2025

The Yājñavalkya Smṛti outlines a structured legal system within its Vyavahāra Adhyāya, detailing ancient Indian civil and procedural law. This study examines key aspects such as types of disputes, pleadings, injunctions (āsedha), and the fourfold process—bhāṣā, uttara, kriyā, and phala. It reflects on the rational and organized nature of legal practice envisioned by ancient jurists and its continued relevance to modern legal thought.

Bello Bello Musa, Adamu Ahmad Bahago, Nasir Auwal Muhammad, Dr. Fakhrun Jamal  ·  International Journal of Technology & Emerging Research  ·  30 Jul 2025

Sophisticated and varied approaches including Smishing (SMS phishing), vishing (voice phishing), and quishing (QR code phishing) have emerged as a result of phishing attacks' major evolution beyond conventional email-based methods. These new attack methods circumvent traditional security measures and compromise private data by taking use of social engineering, mobile technologies, and human trust. This essay examines how phishing strategies have changed over time, looking at the methods, resources, and psychological tricks used in Smishing, vishing, and quishing. Additionally, it draws attention to real-world case studies, detection difficulties, and threat actors' growing use of automation and artificial intelligence. The study also looks at future trends in phishing, such as cross-channel phishing campaigns and deep fake-enabled voice phishing, and considers the consequences for cyber security knowledge. This study intends to educate cyber security experts and stakeholders about the pressing need to develop and adapt defense mechanisms in an increasingly complex threat landscape by examining various contemporary threats.

K. P. Sarkar, Dhanashri Panchbhai  ·  International Journal of Technology & Emerging Research  ·  29 Jul 2025

Ultrasonic velocity (U) and density (ρ) measurements have been performed for the antibiotic Cefotaxime Sodium in aqueous solution at five temperatures: 20°C, 25°C, 30°C, 35°C, and 40°C. These experimental values were employed to compute several thermodynamic and acoustic parameters, including adiabatic compressibility (βₐ), change in adiabatic compressibility (Δβ), relative adiabatic compressibility (Δβ/β°), apparent molal volume (Φᵥ), apparent molal compressibility (Φₖ), and their respective limiting values, Φ°ᵥ and Φ°ₖ. The variation of these parameters with temperature and concentration was analyzed to explore the structure-making or structure-breaking behavior of Cefotaxime Sodium in aqueous medium. The findings provide valuable insights into solute–solvent interactions and the molecular characteristics of this β-lactam antibiotic under thermal perturbation.

Dr.Ravikumar A V, Madhusudhan K V, Guruprasad G, Hareen N R, Gagan H  ·  International Journal of Technology & Emerging Research  ·  28 Jul 2025

Tracking in real time is one of the major issue or task, which can be perform for variety ofpurposes like in surveillance for monitoring any kind of dynamic scenes for safety purposes. So here,in proposed system a object tracking system is developed using the NI vision development module [1]for the purpose of surveillance. The proposed system utilizes a inbuilt-camera to capture images of theobject, which are then processed using pattern matching algorithms to identify and track the object'smovement. The system's performance can be evaluated in terms of accuracy, speed, and robustness.The results demonstrate the effectiveness of the proposed system in tracking objects in real-time, withpotential applications in robotics, surveillance, and automation. In the proposed system, we use theNational Instruments, LabVIEW programming which is the main part of the proposed system. Here asoftware related to NI i.e., Vision Acquisition is taken from the video or image processing techniquesand it performs various tasks like Set image conversion of the image data and grabbing of the video ormodifying the resolution.

Dr. Khushbu Dave, Sanchaniya Devarshi  ·  International Journal of Technology & Emerging Research  ·  28 Jul 2025

A service sector provides people with intangible products or services and completes tasks that are useful to customers, clients, businesses or the general public. Service industries, unlike, for example, manufacturing and production industries, do not relay on the sale of material goods and products to earn a profit. Instead, the individuals who work in the service sector focus on competing tasks and providing services. Service sector, commonly referred as the tertiary sector, is a critical and expansive component of the economy, encompassing a diverse array of activities that do not involve the production of tangible goods. Instead, the sector focuses on the provision of intangible services, which often involve skills, expertise, and knowledge. As economies have involved, the service sector has gained increasing prominence, contributing significantly to economic growth, employment, and overall societal development. It is one of the sectors of the economy, the order two being the primary sector (Which includes agriculture, forestry, mining, and fishing) and the secondary sector (Which includes manufacturing and construction). The service sector encompasses a wide range of sectors that provide intangible products or services rather than physical goods. Here is a list of some major sector within service sector. One of the major indicators of economic development is the shift traditional sectors like agriculture and manufacturing to the service sector. This transformation signifies a move towards higher levels of economic development and sophistication. Employment generation is a significant facet of the service sector, offering a plethora of the job opportunities across diverse fields such as retail, healthcare, education, and finance. Innovation and technological advancement are intrinsic to the service sector, as it constantly strives to enhance efficiency and create new business models.

Nammi Arun Kumar, Dr. G. Narasimha Rao  ·  International Journal of Technology & Emerging Research  ·  28 Jul 2025

The proliferation of Internet of effects bias has introduced significant cybersecurity vulnerabilities compounded by their essential interconnectedness and resource limitations. This paper proposes a robust cybersecurity frame designed to guard IoT ecosystems. Our result integrates an Autoencoder for effective point birth and anomaly discovery Deep Neural Networks(DNNs) for sophisticated deep literacy- grounded attack bracket and Decision Trees for rapid-fire, interpretable real- time trouble identification. By assaying live data from IoT bias, the system effectively detects anomalies and directly classifies different cyber pitfalls including Denial of Service(DoS) attacks and unauthorized access attempts. This multi-layered approach leverages the Autoencoder's capability to learn normal data patterns and highlight diversions while DNNs use these uprooted features to fete intricate attack autographs with high perfection. The addition of Decision Trees ensures nippy and transparent bracket critical for nimble trouble response. This intertwined system significantly improves trouble discovery capabilities and accelerates response times thereby strengthening the overall security posture of IoT networks. The proposed result offers an adaptive and visionary defense against the dynamic and evolving diapason of cyber pitfalls in the expanding IoT geography which decreasingly includes criticalcyber-physical systems(CPS) like Industrial IoT(IIoT) bias within sectors similar as heads and mileage shops integral to the dependable operation of artificial control systems(ICS) including SCADA, DCS, PLCs, and Modbus protocols.

Mayank Vij, Gagan Sharma  ·  International Journal of Technology & Emerging Research  ·  27 Jul 2025

Wireless sensor network is composed of hundreds to thousands of nodes called sensors. WSNs aresuccessfully applied to real world problems ranging from defence to civil applications. In this paper, wecompared various WSN techniques namely LEACH, ABC and PSO with proposed algorithm called Artificialhummingbird algorithm (AHA). Results show that AHA performed better than all other algorithms in termsof energy efficiency, alive nodes and Cluster head (CH) count.

Noorbhasha Karishma  ·  International Journal of Technology & Emerging Research  ·  27 Jul 2025

In today's competitive job market, creating a tailored resume for every job application is both critical and time-consuming. Many organizations use Applicant Tracking Systems (ATS) to filter resumes based on keywords and formatting, often eliminating highly qualified candidates whose resumes are not optimized. This project presents an AI powered Resume Builder that automates the process of customizing resumes based on job descriptions. The system leverages Natural Language Processing (NLP) to extract relevant keywords from job postings and matches them against user provided resumes. Using the Affinda API for resume parsing and a Python Flask backend, the application intelligently updates and formats resumes to improve keyword match rates and ATS compatibility. The resulting resumes are exportable in DOCX and PDF formats, significantly enhancing the candidate's chances of getting shortlisted while reducing manual effort.

Dr. Komal Patel, Ms. Bhanderi Frenee  ·  International Journal of Technology & Emerging Research  ·  26 Jul 2025

In simple terms, the exchange rate represents the value of one nation’s currency in relation to another. It determines how much of one currency be exchanged for another and plays a crucial role in international trade and finance. Often referred to as the foreign exchange rate or forex rate, it influences economic stability, trade competitiveness, and investment flows between countries. Exchange rates are determined in the forex market, a global marketplace where various participants engage in continuous currency trading, operating 24 hours a day except on weekends. The spot exchange rate represents the current value at which currencies are exchanged. In contrast, the forward exchange rate is an agreed-upon rate set today for a transaction that will be executed on a future date. In both developed and developing nations, various stakeholders such as foreign exchange investors, exporters, importers, banks, businesses, financial institutions, and travelers base their decisions on exchange rate fluctuations. Changes in exchange rates affect the value of international reserves, influence the competitiveness of exports and imports, determines the cost of repaying foreign debts, and impact travel expenses by altering the purchasing power of a currency. Therefore, fluctuations in exchange rates greatly influence the business cycle, trade dynamics, and capital movements within an economy. Understanding these changes is vital for analyzing financial trends and evaluating shifts in economic policy.

Shrasthi Mittal, Shrishti Singh  ·  International Journal of Technology & Emerging Research  ·  26 Jul 2025

Municipal solid waste (MSW) generation has significantly increased as a result of the fast urbanization and population growth in both developed and developing nations, creating serious problems for public health and urban administration. Inadequate infrastructure, a lack of source segregation, a lack of money, lax enforcement of policies, and public disinterest in sustainable waste management are some of the major issues related to municipal solid waste management (MSWM) in metropolitan areas that are examined in this study. Open dumping and burning are examples of improper waste disposal methods that have had detrimental effects on the environment, including soil, water, and air pollution, all of which have an immediate effect on human health.Inadequate waste management techniques in urban environments have been connected to the rise of vector-borne diseases, respiratory ailments, and waterborne infections. The study emphasizes the necessity of sustainable and integrated MSWM solutions that include decentralized waste processing systems, technological innovation, community involvement, and more stringent regulatory frameworks. Cities may lessen the negative environmental consequences of solid waste and drastically lower the hazards to public health posed by improper management of urban garbage by tackling these issues comprehensively.

Raj Pratap Singh Chauhan, Abhijeet Garg, Dr. Zareen Baksh , Dr. Lakmikant Pandey, Prof. Sardul Singh Sandhu  ·  International Journal of Technology & Emerging Research  ·  26 Jul 2025

This study focuses on using Gas Chromatography-Mass Spectrometry (GC-MS) to do a thorough metabolic profiling of Cynodon dactylon. Additionally, it assesses the cytotoxicity of the plant in the presence of many plant phytochemicals and known antibiotics. Its varied biochemical characteristics and medicinal potential make Cynodon dactylon, a commonly used grass species, promising. A complex blend of fatty acids, steroids, terpenoids, and flavonoids was revealed by using GC-MS to identify and quantify several metabolites inside the plant. Bioactive substances that may be responsible for the therapeutic effects of the substance are indicated by the metabolic profile. Once the metabolic study was completed, we evaluated the cytotoxic effects of Cynodon dactylon extracts when combined with various antibiotics and phytochemicals. Using common cell viability tests against different cancer cell lines, cytotoxicity was quantified. major cytotoxic activity was demonstrated by the results.

Tirlangi Indumathi, Dr. G. Narasimha Rao  ·  International Journal of Technology & Emerging Research  ·  26 Jul 2025

Skin cancer remains one of the most prevalent and potentially fatal forms of cancer worldwide, highlighting the urgent need for early, accurate, and scalable diagnostic methods. This project proposes a deep learning-based solution for automated skin cancer classification using Convolutional Neural Networks (CNNs) trained on the HAM10000 dataset—a benchmark collection of dermatoscopic images representing seven distinct skin lesion types, including melanoma, basal cell carcinoma, and benign nevi. The framework incorporates robust image preprocessing techniques and a customized CNN architecture designed to optimize feature extraction and classification performance across diverse lesion categories. To further enhance model generalization and address potential class imbalance, the project explores data augmentation strategies tailored for medical imagery. A user-friendly interface, developed using Streamlit, enables real-time inference and accessibility for both clinical and non-specialist use. Experimental results demonstrate high classification accuracy and strong differentiation between malignant and benign lesions, supporting the system’s utility as a reliable, cost-effective, and accessible diagnostic aid. This work underscores the significant role of AI-powered tools in augmenting dermatological decision-making, especially in resource-constrained environments where timely diagnosis can substantially impact patient outcomes.

Dr. Ruchika Dawar, Ms. Tanisha Gupta, Ms. Bakul Kolekar  ·  International Journal of Technology & Emerging Research  ·  25 Jul 2025

The Mahakumbh, one of the world's largest religious gatherings, serves as a dynamic intersection between tradition, spirituality, and modern commerce. This research examines the evolving role of marketing within the Mahakumbh, where brands strategically integrate their presence into the festival's cultural and spiritual essence. As millions of devotees assemble in pursuit of religious fulfillment, businesses leverage this unparalleled congregation to enhance brand visibility, create immersive consumer engagements, and align their marketing narratives with themes of devotion and communal experience. This study employs a qualitative research approach, relying on secondary sources to analyze brand engagement and marketing strategies at the Mahakumbh. Data has been gathered from a diverse range of digital resources, including websites, blogs, news articles, and social media posts by individuals who have documented their experiences and observations during the event. By synthesizing existing literature and online discourse, the research aims to construct a comprehensive understanding of how brands leverage cultural and religious sentiments to shape consumer perception and engagement. By analyzing marketing strategies implemented at the Mahakumbh, this paper highlights the impact of cultural values and faith on consumer perception, demonstrating that successful brand engagement requires a nuanced understanding of the event’s historical significance and emotional resonance. Through this lens, the research provides insights into how companies may harness the power of tradition and spirituality.

Bojja Manisha Ratnam, Prof. Ch Satyananda Reddy  ·  International Journal of Technology & Emerging Research  ·  25 Jul 2025

In the era of global e-commerce, understanding customer sentiment across diverse languages is vital for enhancing user experience and business intelligence. This project, titled "Multilingual Sentiment Analysis in E-commerce Platform", focuses on predicting customer sentiment—positive, negative, or neutral—based on product reviews submitted in multiple languages. The core objective is to bridge the language gap in online feedback interpretation using advanced machine learning and natural language processing techniques. To achieve this, a hybrid approach leveraging both deep learning and traditional models is implemented—specifically, BERT (Bidirectional Encoder Representations from Transformers) for robust text embeddings and contextual understanding, and Random Forest for efficient classification.

Nagireddi Jaya Sravanthi, Dr G.Sharmila Sujatha  ·  International Journal of Technology & Emerging Research  ·  25 Jul 2025

Online payment fraud has been become a significant concern in financial sector, posing challenges for real-time detection and mitigation. This study gives us a machine learning-based fraud detection system designed for identifying fraudulent transactions both before and after their execution. A large transactional dataset is processed and filtered to focus on high-risk transaction types. A Random Forest classifier is implemented for fraud detection due to its robustness and high accuracy in handling imbalanced financial data using standard evaluation metrics. The proposed approach gives high accuracy, precision, and recall, particularly with ensemble models, indicating its effectiveness in enhancing fraud detection systems. The research contributes a deployed, user-interactive solution in Streamlit web interface.

Shaik Ishrath, I. Grace Asha Roy  ·  International Journal of Technology & Emerging Research  ·  25 Jul 2025

With the improvement of the digital era comes an increased value for cybersecurity, and this needs to be addressed using advanced techniques. In this paper, we present an Intrusion Detection System (IDS) that combines the strengths of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, further optimized using Particle Swarm Optimization (PSO). The goal is to detect traffic patterns. The CNN components extract spatial features from the input data, while the LSTM captures the sequential dependencies, enhancing the detection of complex attack patterns. PSO is employed to automatically tune critical hyperparameters such as the number of LSTM units and dropout rate, improving both speed and classification accuracy. Experimental results demonstrate that the optimized model achieves an accuracy of 97.63%, outperforming traditional machine learning and non-optimized deep learning approaches. The proposed system provides a scalable and efficient solution for real-time intrusion detection in cyber environments.

Gantana Sai Madhav, Kunjam Nageswara Rao, Pappala Mohan Rao  ·  International Journal of Technology & Emerging Research  ·  25 Jul 2025

The rapid growth of solar energy adoption underscores the importance of maintaining the efficiency and reliability of photovoltaic (PV) cells. Defects in PV cells, whether caused by manufacturing inconsistencies or environmental factors, can significantly degrade performance and lead to power losses. This study proposes an automated defect identification and categorization system using state-of-the-art image classification models, particularly deep convolutional neural networks (CNNs). The system is trained on a labeled dataset of PV cell images encompassing both defective and non-defective categories, further classifying common defects such as cracks, discoloration, and hotspots. The proposed model achieved a classification accuracy of 97.44%, demonstrating robust performance in real-time defect detection. This AI-driven approach offers a scalable and non-invasive solution for quality assessment in solar panel manufacturing and maintenance, enhancing operational efficiency, reducing manual inspection costs, and supporting the sustainable deployment of solar energy systems.

Dr. Challa Narasimham , Kanipati Sowmya  ·  International Journal of Technology & Emerging Research  ·  24 Jul 2025

The Startup Funding App is a cross-platform mobile application developed using Flutter, aimed at bridging the gap between startups and investors. It uses machine learning algorithms to analyse startup data—such as industry type, location, funding history, and team size—to predict success probability and provide personalised recommendations to investors. The app features a user-friendly interface, secure authentication through Firebase, and real-time dashboards for tracking activity and engagement. By combining intelligent matchmaking with accessibility and automation, the app offers a more efficient, transparent, and data-driven approach to startup funding