Recent Articles

Harikrushna B Rathod, Jitendra M Shah  ·  International Journal of Technology and Emerging Research  ·  19 Sep 2025

Massive multiple input multiple output (MIMO) is an important technology to 5G and beyond wireless communication systems because it is capable of improving the spectral efficiency, energy efficiency, and link reliability. However, precise channel state information (CSI) acquisition is a key requirement for obtaining these benefits. Channel estimation in Massive MIMO is a challenging task especially because of the problem of pilot contamination in which pilot signals from neighboring cells interfere and degrades estimation quality. This paper explores channel estimation techniques and pilot contamination mitigation strategies in Massive MIMO networks from both foundational and emerging perspectives. We describe how different estimation methods are implemented, including least squares (LS), minimum mean square error (MMSE), and compressed sensing (CS) based ones. Moreover, we investigate the effect of the pilot contamination and discuss mitigation approaches, including optimization of pilot reuse, advanced precoding, and deep learning-based approaches. Finally, we highlight open research challenges and future directions.

Jitendra M Shah, Harikrushna B Rathod  ·  International Journal of Technology and Emerging Research  ·  19 Sep 2025

This paper presents a comparative study of various hybrid multilevel inverter (HMLI) configurations with respect to their output voltage levels and overall performance. The focus is on harmonic elimination in HMLIs to reduce the total harmonic distortion (THD) of the voltage applied to the load, thereby improving power quality [1,2]. Different HMLI topologies are analyzed and compared, emphasizing their effectiveness in minimizing harmonics. By dividing the switching process into high- and low-frequency components, the proposed approach aims to enhance converter efficiency while simultaneously reducing size and cost [3]. Furthermore, this study introduces the performance analysis of a novel hybrid multilevel inverter topology, referred to as SHMLI, employing various pulse width modulation (PWM) strategies. The PWM techniques investigated include multicarrier phase disposition (PD) and phase shift modulation methods, which have been demonstrated to improve switching performance and harmonic profile [4,5]. All simulations and analyses are conducted using MATLAB-SIMULINK to validate the theoretical findings.

Veeramuthu P, Rajesh D  ·  International Journal of Technology and Emerging Research  ·  19 Sep 2025

Cancer remains one of the leading causes of mortality worldwide, and timely diagnosis plays a critical role in improving patient survival rates. Traditional diagnostic methods often face challenges such as complexity, cost, and human error, necessitating the development of intelligent computational systems. This study proposes a machine learning–based framework for the intelligent prediction of cancer diseases, aiming to improve accuracy, reduce misdiagnosis, and support clinical decision-making. The proposed approach integrates feature selection, optimized model training, and performance evaluation to construct a scalable predictive model applicable to various types of cancer.

ILKNUR YUCEL, Nadiye CAMCI  ·  International Journal of Technology and Emerging Research  ·  19 Sep 2025

Cancer screening is a crucial public health intervention aimed at early detection and reducing mortality. Health professionals, especially First and Emergency Aid students, play a vital role in promoting awareness and guiding individuals on screening. Understanding their knowledge level is important for shaping effective education programs. This cross-sectional descriptive study was conducted in the 2024–2025 academic year with 1st and 2nd year students enrolled in the First and Emergency Aid Program at a foundation university. Data were collected using a demographic questionnaire and the "Knowledge Scale for Cancer Screenings." A total of 73 volunteer students participated. The reliability of the scale was confirmed with Cronbach’s Alpha = 0.796. 63% of the students had prior knowledge about cancer screenings, with breast cancer being the most recognized. The overall knowledge level was moderate. Significant associations were found between knowledge level and variables such as gender, grade level, and health insurance status. However, smoking, alcohol use, employment status, and family history of cancer showed no significant effect. The findings indicate a need for restructuring cancer screening education in the curriculum of emergency aid students. Enhancing knowledge through targeted training may improve their role as future healthcare providers.

Lavi Devi  ·  International Journal of Technology and Emerging Research  ·  17 Sep 2025

The global shift towards plant based diets,driven by health, environmental, and ethical considerations, encounters significant cultural challenges. This paper explores how traditional food practices , cultural identities, and societal norms influence the acceptance and adoption of plant based diets. Through a comparative analysis of various cultural contexts, the study examines the interplay between tradition and innovation, highlighting the barriers and opportunities in transitioning towards plant based eating.

Dr. Herendra Kumar, Harsh Chhangani  ·  International Journal of Technology and Emerging Research  ·  14 Sep 2025

Silver nanoparticles (AgNPs) have received a lot of interest for their several applications, including their remarkable potential as photocatalysts for organic dye degradation. This review explores the photocatalytic capabilities of silver nanoparticles (AgNPs)—specifically those synthesized via green, eco-friendly methods—in treating synthetic dye-contaminated wastewater. The paper emphasizes the synthesis of AgNPs from various biological substrates, highlighting their economic feasibility, high conductivity, and biocompatibility. The growing concern over the improper disposal of persistent, non-biodegradable synthetic dyes is addressed by showcasing the role of AgNPs as effective agents for breaking down harmful industrial dyes. Key target dyes investigated include methyl orange, congo red, nitrophenol, methylene blue, and malachite green, with performance data reflecting the success of AgNPs from different biological sources. The review outlines the mechanisms of photocatalytic degradation facilitated by these nanoparticles, illustrating how they convert toxic dyes into less hazardous compounds. It also examines the toxicity of AgNPs themselves and strategies for their environmental remediation. Lastly, a comparative analysis of multiple biological substrates is presented to guide the selection of optimal sources for enhanced photocatalytic efficiency and sustainable wastewater treatment solutions.

Kurella Padma, Dr G.Sharmila Sujatha  ·  International Journal of Technology and Emerging Research  ·  12 Sep 2025

The exponential growth of digital information exchange demands secure, efficient, and robust data encoding methods. This paper presents a unified data encoding framework integrating AES-GCM authenticated encryption with PBKDF2-based key derivation, Compress AI-driven learned compression, and a scalable multi- part encapsulation format with integrity verification via SHA-256 digests. The framework supports large payloads by dividing encrypted data into verifiable segments, enabling resilient storage and transmission. Optional image-quality enhancement using Real-ESRGAN or OpenCV EDSR is provided when the payload is visual media. Prototype evaluations show strong compression gains from Compress AI over classical JPEG+zlib baselines, strict integrity enforcement through AES-GCM tags, and accurate end-to-end reconstruction provided that all segments are available. The proposed approach is suited for privacy-preserving storage and controlled sharing in modern digital ecosystems.

Dr. Nitin Prabhu Kulkarni, Dr Avinash S. Desai, Dr. Sandeep Tare  ·  International Journal of Technology and Emerging Research  ·  12 Sep 2025

In an increasingly globalised economy, shifting trade dynamics and tariff structures pose significant challenges for businesses striving to remain competitive. This article explores comprehensive strategies for cost optimisation in response to tariff impacts across the value chain. It outlines practical measures, including local and alternate sourcing, production process improvements, inventory cost control, and labour efficiency enhancements. It emphasises the role of energy savings, real-time operational data, product redesign, and strategic relocation of operations such as nearshoring or reshoring. The article also highlights opportunities for tax and duty optimisation and transportation cost reduction. By integrating these approaches, companies can mitigate tariff-related pressures, improve operational efficiency, and enhance overall cost competitiveness in global markets.

Dr. S. Sahaya Arockia Selvi, Dr.S. Vijayalakshmi, Dr.S. Geetha, A. Abirami  ·  International Journal of Technology and Emerging Research  ·  12 Sep 2025

In this paper, we study on (, )-level subsets of Bipolar valued -fuzzy subgroup and prove some results on these.

Pilla Divya, Dr. M. V. V. Siva Prasad  ·  International Journal of Technology and Emerging Research  ·  09 Sep 2025

Cardiovascular diseases (CVDs) remain the foremost cause of global mortality, accounting for nearly one-third of deaths worldwide. Early detection of cardiac abnormalities is essential to reduce mortality and ensure timely treatment. Electrocardiograms (ECGs) are among the most widely used diagnostic tools for monitoring cardiac activity. However, manual interpretation of ECGs is error-prone and highly dependent on medical expertise. This paper presents a Convolutional Neural Network (CNN)-based framework for automated ECG image classification. The dataset, consisting of 928 ECG images across four categories— Normal, Abnormal, History of Myocardial Infarction (HMI), and Myocardial Infarction (MI) was preprocessed through grayscale conversion, noise reduction, cropping, resizing, and normalization. A custom CNN architecture was trained on this dataset, achieving a classification accuracy of 97.92% on test data, with strong precision, recall, and F1-scores across all categories. The system was deployed using a Flask-based web application that provides real-time predictions and visualizations. The proposed solution demonstrates the applicability of deep learning in medical diagnostics, offering a reliable and scalable approach for CVD detection.

Dwarapu Daliya, Dr. Priyanka K Bhansali  ·  International Journal of Technology and Emerging Research  ·  07 Sep 2025

Handwriting is not just a way of writing; it reflects how a person thinks, feels, and behaves. It acts as a brain imprint that shows each person’s unique personality. This research uses Convolutional Neural Networks (CNNs), a type of deep learning, to detect behaviour patterns automatically from handwriting images. This research focuses on analyzing handwriting characteristics to scientifically infer personality traits from writing patterns and structures. The handwriting images were processed through grayscale conversion, noise removal, thresholding, and normalization. For model development, we divided the data into training, validation, and testing sets and used them to train the CNN model. Along with overall classification, selected handwriting samples were studied to analyze behaviour related features such as slant, margin, line spacing, word spacing, size consistency, baseline consistency and pressure. These features help understand personality traits like emotional stability, clarity of thought, confidence, and how a person interacts with others. This work can find practical use in fields such as recruitment, teaching, forensic examinations, counseling, and mental health services, where having a clear understanding of a person’s character and behaviour is highly valuable.

Dr.S Gladson Oliver, Dr.T.Suguna, Dr.C.Aswini, Dr.R.Malavika  ·  International Journal of Technology and Emerging Research  ·  26 Aug 2025

Prediction systems are the most important part of making decisions based on data. They affect areas like finance, healthcare, industrial automation, and climate science. Even with improvements in deep learning and machine learning, current predictive models still have three big problems: they make more mistakes with each prediction, they are sensitive to changes in the data distributions they are based on, and they can't always capture how different features interact with each other. This paper presents RAFE-Net (Residual Adaptive Feedback Ensemble Network), an innovative algorithm specifically formulated to tackle these constraints. RAFE-Net uses the best parts of ensemble learning, a residual feedback module (RFM) that learns from mistakes all the time, and a distribution shift detector (DSD) that changes predictions when data distributions change a lot. The framework not only makes things more accurate, but it also makes them more robust and easier to understand. This makes it good for high-stakes situations like fraud detection and medical diagnosis. Experimental assessments utilising benchmark datasets—such as the M4 time series dataset, the IEEE-CIS fraud detection dataset, and various datasets from the UCI Machine Learning Repository—illustrate that RAFE-Net attains a predictive accuracy improvement of up to 7.2% and a reduction in false positives by 12.5% relative to leading-edge baselines. These findings underscore the promise of feedback-driven ensemble frameworks as the forthcoming generation of predictive modelling systems.

Dr. Pricila R, Letitia K Vinoy  ·  International Journal of Technology and Emerging Research  ·  25 Aug 2025

Sankagiri, an impressive fort is seen on a hillock of Salem district, with its elaborate, massive ramparts running all the way the hill. This fort is said to have been the holding of “Theeran Chinnamalai” an indigenous warrior who fought against British oppression. This paper tries to traces out the historical significance of Sankagiri fort and elaborates its architectural features.

Dr. Nitin Prabhu Kulkarni, Dr Avinash S. Desai, Dr. Sandeep Tare  ·  International Journal of Technology and Emerging Research  ·  24 Aug 2025

Industrial ovens are substantial energy consumers and play a crucial role in influencing product quality. Therefore, enhancing their performance should be a priority for manufacturers. This review outlines an innovative and actionable strategy for enhancing oven performance, with a focus on improving energy efficiency, optimising processes, and promoting environmental sustainability. The proposed approach is divided into three phases: gaining a deep understanding of the product, refining the production process, and optimising process parameters. Key parameters such as temperature, air flow rate, and cycle time are adjusted to achieve energy savings while minimising environmental impact.

Mukesh Jangid, Dr. Jyoti Jagwani  ·  International Journal of Technology and Emerging Research  ·  24 Aug 2025

Income tax revenue plays a pivotal role in a nation’s economic stability and development. This study examines the decadal growth of India's income tax revenue from 2013-14 to 2023-24, focusing on taxpayer engagement and state-wise contributions. Using statistical analyses such as descriptive statistics, Mann-Kendall trend analysis, and regression modeling, this research explores the relationship between tax collections, number of returns filed, return filers and taxpayers. Income-tax time series data have been taken from official income tax portal. This study involves the analysis of data using Microsoft Excel, focusing on the application of various built-in tools to extract meaningful insights. It assesses the effectiveness of government initiatives aimed at broadening the tax base. The findings highlight a significant upward trend in tax revenue, emphasizing the impact of fiscal reforms, digitalization, and increased taxpayer participation. Additionally, the study underscores regional disparities in tax contributions, with Maharashtra, Karnataka, and Delhi leading in collections. These insights provide some policy recommendations for enhancing tax compliance and revenue generation through strategic reforms.

Dr. Hetal Bhaidasna, Dr. Zubin Bhaidasna  ·  International Journal of Technology and Emerging Research  ·  22 Aug 2025

This research demonstrates a novel attempt to help people who are both deaf and mute by creating a communication assist system that translates hand signs into words. The system uses a camera to capture hand movements and the trained recognition model identifies them. After recognition, text translation followed by speech synthesis through a voice module is performed. To train and evaluate the system, a custom dataset capturing common gestures was created. The sign-to-speech solution is tailored to operate on constrained, cost-effective hardware such as smartphones and tablets. Furthermore, this review discusses the commonly used datasets in sign-to-speech research and their limitations in terms of size, diversity, and standardization. It also suggests a general flow of implementation starting from data collection, preprocessing, feature extraction, model training, and conversion to speech. The paper highlights key challenges such as gesture variability, occlusion, and real-time processing.

Dr Nellutla Sasikala, Vemuri Pravalika  ·  International Journal of Technology and Emerging Research  ·  19 Aug 2025

For computer vision tasks like object detection, recognition, and classification, relay on feature extraction, labelling and segmentation of captured videos or images. Applications like smart city, health care, geoscience and remote sensing are based on video analysis. Image segmentation for video analysis plays an vital role. One of the novel segmentation strategies which has been recently developed is panoptic segmentation. Panoptic segmentation is a fusion of semantic and instance segmentation. In self autonomous driving, medical image analysis, crowd counting, etc. have complicated background components, the high variability of object appearances, numerous overlapping objects and ambiguous object boundaries makes the task challenging. For such applications panoptic segmentation is used which provides several state-of-art methods and robust learning.

Nanda Korde, Aakash Sanjeev Singare  ·  International Journal of Technology and Emerging Research  ·  19 Aug 2025

This study explores the synthesis of biodegradable plastics using banana peel starch, a renewable agro-waste, combined with glycerol& sorbitolas plasticizers. Bioplastics were tested to understand their strength, flexibility, and biodegradability. The results indicates that the choice of plasticizers significantly influence the properties of the resulting bioplastics, highlighting their potential as sustainable alternatives to conventional plastics.

Dr Y Raghunatha Reddy, B. Sravanthi , S.Rehana  ·  International Journal of Technology and Emerging Research  ·  13 Aug 2025

Air pollution remains a pressing environmental and public health challenge in India, with fne particulate matter (PM2.5) posing severe respiratory and cardiovascular risks. This study conducts a comparative statistical inference analysis of daily PM2.5 concentrations for Delhi and Mumbai, based on 2024 data sourced from the Central Pollution Control Board (CPCB). Two estimation approaches are applied: the classical parametric t-based confidence interval method, which assumes normality, and the non-parametric bootstrap approach, which relies on re-sampling without distributional assumptions. The analysis reveals that while Delhi consistently exhibits substantially higher PM2.5 levels than Mumbai, the estimated means and confidence intervals from both methods are closely aligned, indicating that the parametric method’s assumptions are reasonably met in this dataset. The findings underscore the utility of bootstrap methods in validating classical inference, particularly in environmental data analysis, and provide robust evidence for policy-oriented air quality interventions.

Dr. M. Rajeshwari, Abhishek S, Anil Kumar S, Dev S Shah, Vijay Kumar  ·  International Journal of Technology and Emerging Research  ·  13 Aug 2025

Real-Time Human Motion Capture using wearable sensors has emerged as a promising technology in various fields, such as sports analysis, rehabilitation, and virtual reality. This work presents a novel approach to capturing human motion in real-time using lightweight and unobtrusive wearable sensors. By employing sensor fusion techniques and advanced algorithms, the system accurately tracks and reconstructs the movements of individuals, providing detailed information about joint angles, velocities, and trajectories. The real-time aspect of the system enables instantaneous feedback, making it ideal for applications requiring immediate analysis or interaction. The proposed method demonstrates high accuracy and reliability, paving the way for widespread adoption of wearable sensor-based motion.