Devaraj Verma - Academia.edu (original) (raw)

Papers by Devaraj Verma

2023 International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)

International Journal For Multidisciplinary Research

Electroencephalography, or EEG for short, is a technique used to record the electrical activity o... more Electroencephalography, or EEG for short, is a technique used to record the electrical activity of the brain. This EEG detects errors that affect how the human brain functions. This method is the most commonly used for recording the brain in laboratory research, clinical investigations, patient health monitoring, diagnostics, and a variety of other applications due to its non-invasiveness and cost-benefit ratio. Most EEG recordings are contaminated by a variety of irregularities, including those caused by electrode displacement, motion, ocular, and muscular activity related EMG anomalies. These unwanted artifacts may make it difficult to distinguish genuine information from them, in addition to confusing the brain's information processing that supports them. EEG signal artifacts can be removed in a variety of ways. The top and most popular artifact reduction techniques are listed on this page as PCA, pure EEG, and wavelet transform. The study provides a thorough evaluation of cu...

Journal of machine and computing, Jan 5, 2023

Breast cancer represents one of the leading cancer-related diseases worldwide, affecting mostly w... more Breast cancer represents one of the leading cancer-related diseases worldwide, affecting mostly women after puberty. Even though the illness is fatal and kills thousands of people each year, it is mostly curative if found quickly. As a result, prompt and precise detection methods are critical to patient survival. Previously, doctors used manual detection systems for this objective. However, such techniques have been slow and frequently dependent on the physician's expertise. As technology advanced, these primitive methodologies were supplemented by computer-aided detection (CAD) algorithms. Deep learning is extremely common because of the massive development in large data, the Internet of Things (IoT), linked devices, and high-performance computers using GPUs and TPUs. The Internet of Things (IoT) has advanced recently, and the healthcare industry is benefiting from this growth. Sensors that gather data for required analysis are crucial components utilized in the Internet of Things. Physicians and medical staff will be able to carry out their tasks with ease and intelligence thanks to the Internet of Things. The proposed research focus on integrating Alexnet and ResNet101 for accurate prediction of Breast malignancy from mammogram data. This methodology will target the features more precisely than any other combination of the pre-trained model. Finally, to resolve the computational burden issue, the feature reduction ReliefF methodology is used. To demonstrate the proposed method, an online publicly released set of data of 750 BU images is used. For training and testing the models, the set of data has been further split into 80 and 20% ratios. Following extensive testing and analysis, it was discovered that the DenseNet-201 and MobileNet-v2 trained SVMs to have an accuracy of 98.39 percent for the original and augmented Mammo images online datasets, respectively. This research discovered that the proposed approach is efficient and simple to implement to assist radiographers and physicians in diagnosing breast cancer in females.

2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART)

International Journal for Research in Applied Science and Engineering Technology

: Facial attendance using face recognition and detection technology is a modern method of recordi... more : Facial attendance using face recognition and detection technology is a modern method of recording attendance based on facial features. This method is commonly used in automated attendance management systems and is efficient in tracking employee or student attendance without physical interaction. Face recognition has numerous applications such as security, surveillance, and human-computer interaction. This research aims to compare the performance of two popular face recognition techniques: HOG and KNN. The HOG algorithm extracts feature from an image using pixel intensity gradients while the KNN algorithm matches a test image with the most similar image in the training dataset. The study was conducted using the Labeled Faces in the Wild dataset available at https://vis-www.cs.umass.edu/lfw/. The results of the investigation show that the KNN algorithm outperforms the HOG algorithm in terms of accuracy. This research provides valuable insights into the effectiveness of different face recognition algorithms, helping researchers and developers choose the most suitable algorithm for their specific requirements.

Swarm Intelligence and Machine Learning, Jul 5, 2022

International Journal of Cloud Applications and Computing

To meet the ever-growing demand for computational resources, it is mandatory to have the best res... more To meet the ever-growing demand for computational resources, it is mandatory to have the best resource allocation algorithm. In this paper, Particle Swarm Optimization (PSO) algorithm is used to address the resource optimization problem. Particle Swarm Optimization is suitable for continuous data optimization, to use in discrete data as in the case of Virtual Machine placement we need to fine-tune some of the parameters in Particle Swarm Optimization. The Virtual Machine placement problem is addressed by our proposed model called Improved Particle Swarm Optimization (IM-PSO), where the main aim is to maximize the utilization of resources in the cloud datacenter. The obtained results show that the proposed algorithm provides an optimized solution when compared to the existing algorithms.

Journal of emerging technologies and innovative research, 2021

This paper discusses about the recognition of handwritten characters of Kannada. In particular we... more This paper discusses about the recognition of handwritten characters of Kannada. In particular we concentrate about the hybrid technique which is used in here. This approach uses two algorithms which are genetic and fuzzy logic for recognition. Feature extraction is the estimation of certain attributes of the target patterns. Selection of the right set of features is the most crucial and complex part of building a pattern recognition system. The novelty of this approach is to achieve better accuracy and reduced computational time for recognition of handwritten characters using Genetic Algorithm which optimizes the number of features along with a simple and Fuzzy Logic. We will analyze the performance and accuracy of the system.

Journal of emerging technologies and innovative research, 2020

In our daily lives, organizing resources like books or webpages into a set of categories to ease ... more In our daily lives, organizing resources like books or webpages into a set of categories to ease future access is a common task. The usual largeness of these collections requires a vast endeavour and an outrageous expense to organize manually. As an approach to effectively produce an automated classification of resources, consider the immense amounts of annotations provided by users on social tagging systems in the form of bookmarks. This project deal with the utilization of user provided tags to perform a social classification of resources. Those resources are accompanied by categorization data from sound expert-driven taxonomies. We analyse different representations using tags and compare to other data sources by using different settings of SVM classifiers. Finally, we explore combinations of different data sources with tags using classifier committees to best classify the resources.

Absract: The operating system in our computer machines have changed a lot during the course of ti... more Absract: The operating system in our computer machines have changed a lot during the course of time, where in the initial stage of their development they were used to process a single task (process) at a time but now, in the era of supercomputers we have multiprogramming operating system running in our machines. At present we have a number of scheduling algorithms which are used to decide the order in which the processes loaded into the memory are to be executed. But none of the conventional scheduling algorithms is ideal, they have their own drawbacks. In this paper, an advanced fuzzy-based logic has been proposed for soft real time system toovercome the drawbacks of other algorithms for better CPU utilization and to minimize waiting, turnaround and response time. The proposed algorithm is preemptive in nature with minimum context switching and work to complete process within its deadline.

International Journal of Innovative Technology and Exploring Engineering, 2020

Breast Cancer is the most often identified cancer among women and a major reason for the increase... more Breast Cancer is the most often identified cancer among women and a major reason for the increased mortality rate among women. As the diagnosis of this disease manually takes long hours and the lesser availability of systems, there is a need to develop the automatic diagnosis system for early detection of cancer. The advanced engineering of natural image classification techniques and Artificial Intelligence methods has largely been used for the breast-image classification task. Data mining techniques contribute a lot to the development of such a system, Classification, and data mining methods are an effective way to classify data. For the classification of benign and malignant tumors, we have used classification techniques of machine learning in which the machine learns from the past data and can predict the category of new input. This study is a relative study on the implementation of models using Support Vector Machine (SVM), and Naïve Bayes on Breast cancer Wisconsin (Original) D...

Journal of emerging technologies and innovative research, 2019

These days, a basic appraisal effort in clinical affiliations biometrics is finding careful bioma... more These days, a basic appraisal effort in clinical affiliations biometrics is finding careful biomarkers that permit settling on clinical choice assistance instruments. Parkinson’s disease (PD). Surprisingly even though the medical fields have advanced so much but still the cause of the second most common neurodegenerative still the main cause is unknown and we are still using the traditional way diagnosis of Parkinson disease, the disease may not be detected when it is in early stage. We have used several Convolutional Neural Network (CNN) models to detect Parkinson's disease. Here we have utilized data that contain drawings like waves and spirals already analyzed by the proficient specialist examine it. We have used several Convolutional Neural Network (CNN) models to detect Parkinson's disease. This examination was performed utilizing a public dataset: Parkinson's Disease Spiral Drawings dataset. Here we have attained the accuracy rate of 92.5% on ResNet50 and the best ...

This paper provides a total overview of OCR. Optical character recognition is ... more This paper provides a total overview of OCR. Optical character recognition is nothing but the ability of the computer to collect and decipher the handwritten inputs from documents, photos or any other devices. Over these many years, many researchers have been researching and paying attention on this topic and proposed many methods which can be solved. This research provides a historical view and the summarization of the research which done on this field. Keywords: Historical view, handwritten recognition, OCR

This paper investigates and analyzes the graphical password scheme, relying on recognition. Few g... more This paper investigates and analyzes the graphical password scheme, relying on recognition. Few graphical password schemes focused on recognition are being researched and evaluated with respect to their security risks. Preventative measures and recommendations for avoiding and reducing the threats are provided. The results include a comparative description of the chosen recognition-based graphical password scheme. Key words–Authentication, Graphical Password, Information Security.

Wireless sensor networks [1-2] is a technology which as a diverse number of applications. While t... more Wireless sensor networks [1-2] is a technology which as a diverse number of applications. While these networks are infrastructure less and do not have any public address. They are made up of many tiny sensor nodes and have insecure radio links. Thus they are highly vulnerable to security threats since the sensor nodes are the core weakness as they are with limited-resource. This paper aims at mitigating the security threats [7] to the wireless sensor network by implementing the reinforcement Q learning algorithm [4-6]. A new intrusion detection system called the Markovian [1] IDS is designed, to protect sensor nodes from malicious attacks. The Markovian IDS incorporates Q learning [4-6] to sense the network and attributes of each nodes.