Authors: Dr. Kumari Priyanka

Abstract: Role of a secondary alcohol (Propan-2-ol) in affecting the kinetics and the thermodynamic extensive properties of the acid catalysed solvolysis of iso-butyl formate has been highlighted by studying the kinetics of the reaction in different aquo-propan-2-ol reaction media having 20 to 80% (v/v) of propan-2-ol and at five different temperatures rising from 20 to 40°C at interval of 5°C It was found that with increase in Jemperature of the reaction from 20 to 40°C from 0.172 to 1.344 molecules of water are associated with the activated complex and from this, it is inferred that mechanistic path followed by the reaction in presence of propan-2-ol is changed from bimolecular to unimolecular. The depletion and enhancement observed respectively in iso-composition and iso-dielectric activation energies reveal that the transition state is solvated and initial state is desolvated with addition of propan-2-ol in reaction media. Almost unity value of the slope of the plots of log k values against log [H+] values shows that the reaction follows AAC2 mechanism. From the values of iso-kinetic temperature, which comes to be 287, it may be concluded that in water-propan-2-ol reaction media, the reaction follows Barclay-Butler rule and there is weak but acceptable interaction between solvent and solute in aquo-propan-2-ol reaction media

DOI: https://doi.org/10.5281/zenodo.18373220

 

Published by: vikaspatanker

Authors: Gayatri Solanki, Abhay Mundra

Abstract: Gender identification has gained significant attention in recent years because it supports many real-world applications related to demographic analysis and human-centered systems. Gender classification refers to the automated process of predicting a person’s gender (typically male or female) based on visual appearance, most commonly from facial images. However, extracting reliable and discriminative facial features remains challenging due to variations in lighting, pose, facial expressions, occlusion, and image quality. With advances in machine learning and deep learning, automatic gender classification systems have become increasingly accurate and widely adopted across multiple domains. These systems can be useful in security and access-control environments, as well as in demographic analytics and personalized services. In certain contexts, gender identification may also be applied to manage access in gender-specific spaces and services, such as women-only transportation sections or gender-segregated facilities. This review summarizes key traditional machine learning approaches (e.g., SVM-based methods) and modern deep learning techniques (e.g., convolutional neural networks), and discusses commonly used benchmark datasets and evaluation practices for gender classification.

Published by: vikaspatanker

Authors: Devendra Gupta, Abhay Mundra

Abstract: Brain tumor segmentation is a critical task in medical imaging, supporting accurate diagnosis, treatment planning, and continuous monitoring of tumor progression. Over the years, a variety of segmentation strategies have been proposed, each offering distinct advantages and limitations. Early traditional approaches—including thresholding, edge-based detection, and region-growing methods—are computationally efficient and simple to implement, but they often perform poorly in the presence of noise, intensity inhomogeneity, and complex or ambiguous tumor boundaries. Statistical and model-driven techniques, such as clustering methods and deformable models, improve adaptability to anatomical variability but typically require careful parameter selection and may involve higher computational cost. In recent years, machine learning and deep learning methods have transformed brain tumor segmentation, particularly through the use of Convolutional Neural Networks (CNNs) and U-Net-based architectures, which have demonstrated strong accuracy and robustness across large and diverse MRI datasets. More recently, hybrid methods that combine classical image processing with deep learning have gained attention for improving efficiency, interpretability, and generalization. This review summarizes the evolution of brain tumor segmentation methods, compares major categories of approaches, and discusses current challenges and promising future research directions.

Published by: vikaspatanker

Authors: Devendra Gupta, Abhay Mundra

Abstract: Brain tumor detection from magnetic resonance imaging (MRI) is essential for early diagnosis, treatment planning, and improved patient outcomes. This study conducts a comparative evaluation of five deep learning approaches—CNN, ResNet50, U-Net, YOLOv7, and InceptionResNetV2—for automated brain tumor classification. Prior to model training, the dataset was prepared through systematic preprocessing, including data cleaning, normalization, and augmentation to improve robustness and reduce overfitting. Model performance was assessed using standard classification metrics: accuracy, precision, recall, and F1-score. Experimental results indicate that all evaluated architectures achieved strong predictive performance; however, InceptionResNetV2 consistently outperformed the other models, achieving near-perfect scores across all evaluation measures. This strong performance suggests improved reliability in reducing both false-positive and false-negative predictions, making InceptionResNetV2 a promising candidate for clinical decision-support applications. Overall, the findings highlight the importance of advanced deep learning architectures in delivering accurate and dependable MRI-based brain tumor detection.

Published by: vikaspatanker

Authors: Gayatri Solanki, Abhay Mundra

Abstract: Gender identification from facial images is an important problem in computer vision and has attracted growing interest due to its applications in surveillance, security, and human-centered systems. Although humans can infer gender naturally, developing automated systems that perform reliably across real-world conditions remains challenging. This work presents a gender classification framework that leverages face recognition feature vectors for prediction. First, face images are detected, aligned, and preprocessed to obtain a standardized facial representation. Next, a face recognition network extracts compact feature embeddings that encode discriminative facial characteristics. Finally, machine learning and deep learning classifiers are applied to these embeddings to determine gender. The proposed system integrates advanced components including VGG-Face, Deep Belief Networks, and shifted filter responses to improve robustness. Multiple deep learning architectures were investigated—CNN, VGG16, ResNet50, InceptionV3, and EfficientNet—with ResNet152 showing the strongest overall performance. Experimental findings indicate that ResNet152 achieves approximately 9% improvement over leading alternatives and demonstrates enhanced resilience to anomalies and variations compared with earlier approaches.

Published by: vikaspatanker

Authors: Umer Khan

Abstract: Agriculture is a primary source of livelihood for a large population in India and remains essential for human survival and national economic development. However, variations in climate and local environmental conditions increase the risk of crop diseases, which can significantly reduce yield and quality. In many cases, the earliest symptoms of plant infections appear on leaves and, if not detected in time, the disease can spread throughout the plant and across the field, leading to major production losses. Early and accurate identification of plant diseases is therefore critical for reducing agricultural losses and improving the quality of farm produce. Since manual inspection is difficult and time-consuming due to the large number of plants in cultivation areas, automated disease detection methods are increasingly required. This work proposes an AI-based software approach for detecting leaf infections to enable fast and reliable diagnosis, followed by evaluation and actionable insights to prevent large-scale crop damage. The proposed framework involves key steps including image dataset collection, image preprocessing, feature extraction/selection from leaf images, model evaluation, and disease classification. The overall goal is to support timely disease management and improve crop productivity and profitability.

Published by: vikaspatanker

Authors: Deepika Soni, Neelesh Shrivtastav, Pradeep Tripathi

Abstract: Using imaging technology, we suggest that plant disease detection systems automatically identify the symptoms that occur on the leaves and stems of a plant, allowing for the cultivation of healthy plants on a farm to be improved. It is these systems that monitor the plant's characteristics, such as its leaves or stem, and any variations that are seen from those characteristics will be automatically recognised and sent to the user. The purpose of this paper is to conduct an evaluation of the available disease detection methods in plants. The most recent breakthrough in deep learning-based convolutional neural networks (CNNs) has resulted in a significant improvement in picture categorization accuracy. This Thesis, which is motivated by CNN's success in picture classification, uses a pre-trained deep learning-based technique for identifying plant illnesses to detect plant diseases. The contribution of this work may be divided into two categories: Predictions for a dataset may be made using the most powerful large-scale architectures available today, such as AlexNet GoogleNet, which are utilised for illness detection and the usage of baseline and transfer learning techniques for predictions. CNN's suggested model was trained and tested using data sets gathered from the website, according to the network. The results of training, testing, and experiments demonstrate that the suggested architecture is capable of realising and increasing GoogleNet model getting to 99.10 percent. when compared to other models, the accuracy.

 

Published by: vikaspatanker

Authors: Nitu Shah, Saima Khan, Dr. Vikas Gupta, Sandip Nemade

Abstract: This study explores the application of smart algorithms on improved adaptive modulation and coding schemes in 6G systems. The rapid development of wireless communication requires system requirements advanced sufficient to dynamically maximize the spectral efficiency, simultaneously the ultra-reliable low-latency communication. By using machine learning-based intelligent modulation and coding scheme (MCS) selection, such as deep reinforcement learning with a Q-learning method and CNNs, this study attempts to fine-tune MCS selection in 6G networks. The conclusion from the proposed hypothesis is that intelligent algorithm driven adaptive MLC can deliver substantially higher performance in throughput/spectral efficiency/BER as compared to traditional lookup table and outer loop link adaptation methods. Results show that modular adaptive modulation and coding based on reinforcement learning achieves 10%-20% gain in terms of throughput over traditional outer loop link adaptation, and spectral efficiency gains lie between 12.64% ∼ 21.52% for different velocity conditions. The computational complexity of deep learning methodologies decrease up to 80%, with comparable block error rate performance. Results indicate that intelligent algorithms can achieve real-time channel quality adjustment, and improve key 6G performance metrics. This work paves the way for self- organizing wireless networks supporting a variety of quality-of-service demands in future generation communication systems.

DOI: http://doi.org/10.5281/zenodo.18231654

Published by: vikaspatanker

Authors: Umer Khan, Ranjan Thakur

Abstract: Image restoration the task of recovering degraded or damaged images has become essential across many technical domains, including space imaging, medical imaging, and several post-processing applications. Most restoration techniques begin by modeling the degradation process that corrupts an image, typically involving blur and noise, and then attempt to reconstruct an approximation of the original image. However, in real-world scenarios, degradation is often unknown, requiring the simultaneous estimation of both the true image and the blurring function directly from the observed degraded image, without relying on prior knowledge of the blur mechanism. This thesis proposes a novel digital image restoration approach based on punctual kriging, supported by multiple machine learning algorithms. The work focuses on restoring images corrupted by Gaussian noise by achieving an effective trade-off between two competing goals: producing smooth, visually pleasing results while preserving edge details and structural integrity.

DOI:

 

Published by: vikaspatanker

Authors: Shradha Kumavat, Kapil Shah

Abstract: Image restoration is a fundamental task in image processing with wide-ranging applications in modern life, including medical imaging, remote sensing, radar imaging, and digital preservation of historical and museum artifacts. The objective of image restoration is to recover a high-quality image from degraded observation by reducing the effects of noise and blur. Effective restoration depends on understanding the degradation process; therefore, identifying the type of noise and the blur model is essential. In practical scenarios, images are often degraded by atmospheric and environmental conditions and restoring them requires appropriate restoration techniques tailored to the distortion characteristics. This paper reviews and assesses contemporary machine learning-based image restoration methods. The proposed evaluation reports quantitative performance across four standard benchmark datasets Kodak24, CBSD68, Urban100, and LIVE—using PSNR (dB), MSE, and SSIM as primary quality metrics. The achieved PSNR scores are 27.24 dB, 29.38 dB, 30.04 dB, and 30.91 dB on Kodak24, CBSD68, Urban100, and LIVE, respectively. The corresponding MSE values are 367.56, 224.88, 193.10, and 158.02, while SSIM values are 0.8690, 0.9337, 0.9432, and 0.8008. These results demonstrate the effectiveness of the evaluated approach in improving image quality across diverse image restoration benchmarks.

Published by: vikaspatanker