Enhancing UAV Path Planning Efficiency through Adam-Optimized Deep Neural Networks for Area Coverage Missions
Efficient Unmanned Aerial Vehicle (UAV) trajectory generation is crucial for successful area coverage missions, aiming to maximize coverage while minimizing resource consumption. In this research, we present a comprehensive study on optimizing UAV ...
Sentiment Analysis of Self Driving Car Dataset: A comparative study of Deep Learning approaches
Sentiment Analysis (SA) is a crucial task in understanding public opinions and perceptions towards emerging technologies. In this study, we focus on SA for a self-driving car dataset as it provides valuable insights into public perceptions and ...
ML assisted enhanced recommendation system for reliable volunteering environment
The coronavirus pandemic has negatively impacted everyone around the globe. The need to follow social distancing has increased the demand for technology that makes remote volunteering accessible. Alerting, aiding, and educating individuals at risk ...
Unleashing the Potential of Boosting Techniques to Optimize Station-Pairs Passenger Flow Forecasting
Station-pair passenger flow forecast modeling is crucial for public transportation to address emerging needs. The accurate prediction and estimation will provide backbone support for various features of transport, viz. (a) Demand forecasting for ...
Energy-Efficient Routing Optimization for Underwater Internet of Things using Hybrid Q-Learning and Predictive Learning Approach
The Underwater Internet of Things (U-IoT) connects devices used in underwater exploration, collection and transmission of data for exploration, monitoring, and management purposes. However, power supply unpredictability, harsh climates, and ...
Privacy Preserving Collaboratively Training Framework for Classification of Major Depressive Disorder using Non-IID Three Channel Electroencephalogram
Major Depressive Disorder (MDD) is characterized by low mood, loss of interest and even suicidal ideation. Electroencephalogram based diagnosis of a variety of neurological conditions has been conducted in recent years using modern neurocomputing ...
ResNet-50 vs. EfficientNet-B0: Multi-Centric Classification of Various Lung Abnormalities Using Deep Learning
Lung abnormalities are among the significant contributors to morbidity and mortality worldwide. It induces symptoms like coughing, sneezing, fever, breathlessness, etc., which, if left untreated, may lead to death. In current clinical practice, ...
A Cross-Platform Movie Filtering and Recommendation System Using Big Data Analytics
One of the best forms of entertainment today is online multimedia with movies and various other entertainment sources at the tip of our fingers. However, finding a movie from the numerous options available in a movie database to suit a user's ...
Deep Learning Based Detection of Toxic Mushrooms in Karnataka
Mushrooms are fungi that play significant roles in ecosystems, food, and medicine. However, distinguishing toxic mushrooms from non-toxic ones is challenging, as their visual characteristics can be misleading. Deep learning, a subset of artificial ...
Evaluating the Impact of Text Data Augmentation on Text Classification Tasks using DistilBERT
Data augmentation entails artificially expanding the dataset's size by applying various transformations to the existing raw data. Enhancing the quality and quantity of the datasets with varying sizes by employing varieddata augmentation techniques ...
LesNet: An Automated Skin Lesion Deep Convolutional Neural Network Classifier through Augmentation and Transfer Learning
Skin cancer is one of the most prevalent forms of cancer around the world. Initial diagnosis relies on visual assessment of the affected area, followed by detailed dermoscopic analysis. The development of an automated system for classifying skin ...
A Comparative Analysis of Advanced Machine Learning Algorithms to diagnose Parkinson's Disease
Parkinson's disease (PD) is a prevalent and debilitating neurological disorder affecting millions worldwide, necessitating early and precise diagnosis for effective management and therapy. Recent breakthroughs in artificial intelligence (AI) ...
DeepChill: ECG Analysis using Deep Learning for Automatic Stress Recognition
Stress is a common experience with significant impacts on our health, which has sparked an increase in research aimed at detecting stress. This paper explores the effectiveness of using Electrocardiogram (ECG) signals as a key indicator for stress ...
Synergistic Detection of Multimodal Fake News Leveraging TextGCN and Vision Transformer
In today's digital age, the rapid spread of fake news is a pressing concern. Fake news, whether intentional or inadvertent, manipulates public sentiment and threatens the integrity of online information. To address this, effective detection and ...
Exploring Twitter Sentiments for Predicting Match Outcomes in The Game of Cricket
In recent years, social media platforms such as Twitter have become popular among fans to discuss and share their opinions about the matches. This research aims to contribute to the growing body of knowledge on utilizing the sentiments expressed ...
ACO-DTSP Algorithm: Optimizing UAV Swarm Routes with Workload Constraints
In this research, we present a variant of the Ant Colony Optimization (ACO) algorithm tailored for optimizing routing within a limited-capacity environment for a swarm of drones. Our primary objective is to minimize task completion time while ...
Economic Order Quantity Models with Exponential Demand Rate and Single Level Trade Credit
In case of formulating more realistic inventory models, two factors of the problem have been raising attention of the researchers, one being the change in the demand rate and the other being the different types of trade credits. Keeping in view of ...
Identification of Brain Diseases using Image Classification: A Deep Learning Approach
- Jagendra Singh,
- Akansha Singh,
- Krishna Kant Singh,
- Bechoo Lal,
- Rithwik Arthur William,
- Anil V Turukmane,
- Advait Kumar
Brain diseases are a growing concern in healthcare, with an estimated 50 million people worldwide living with dementia and 15 million new cases each year. This research paper explores the significance of early identification and diagnosis in ...
Diagnosis of bacterial leaf blight, leaf smut, and brown spot in rice leafs using VGG16
In the context of agricultural sustainability and food security, timely and precise identification of plant diseases holds paramount significance. This research proposes an innovative approach to diagnose bacterial leaf blight (BLB), rice blast (...
Enhancing Parkinson's Disease Diagnosis through Deep Learning-Based Classification of 3D MRI Images
This paper presents a deep learning-based model developed to accurately classify Parkinson's disease patients using 3D MRI scans and data augmentation techniques. The study aimed to address the research question of whether this approach could ...
Syntactic Category based Assamese Question Pattern Extraction using N-grams
Syntactic categories are important because they play a major role in analysis of sentences. Assamese is a versatile language added with diversified linguistic features. Analysis and manipulation of such a rich language in terms of its vocabulary ...
A Short Review for Handwritten Math Expression Recognition Techniques
Handwritten Math Expression recognition is an active area of research that aims to enable machines to automatically interpret handwritten mathematical notation. This technology has many potential applications in education, office automation, and ...
User Centered Non-Functional Requirements Specification – An Extended Use-Case Diagram
Requirements Engineering (RE) activity is often initiated with vaguely specified software requirements. The ambiguities must be overcome to the extent possible when the requirements are specified. Both functional (FRs) and non-functional (NFRs) ...
Whale Optimization-based Synthetic Minority Oversampling Technique for Binary Imbalanced Datasets
The problem of class imbalance has become a predominant area of research recently. Synthetic Minority Oversampling Technique (SMOTE) stands as a popular and widely adopted oversampling technique that effectively addresses the challenge of class ...
Vision-based human wrist localization and with Kalman-filter backed stabilization for Bilateral Teleoperation of Robotic Arm
The development of advanced robotic systems that can be operated effortlessly by humans is crucial for expanding the capabilities of human-robot interaction and collaboration. In this work, we propose a system that enables real-time and non-...
Semantic-Region Aware Model Predictive Trajectory Tracking in Automated Guided Vehicles
Autonomous routing through curved path for point-to-point mobile robot is a well-established challenging problem in the area of intelligent path navigation. Point-to-point collision-free locomotion of AGV (Automated Guided Vehicle) requires ...
Edge based Blind Spot Avoidance and Speed Monitoring for Emergency Vehicles
In this paper, the proposed work is to design an emergency vehicle (rescue vehicle) equipped with sensors and an Arduino board as an edge device to gather information on approaching vehicle speed and blind spots. To quickly identify potential ...
Vision Based Gesture Recognition
The use of computers has become an integral part of our daily lives, and they are utilized in various fields. However, traditional input devices such as a mouse and keyboard can sometimes be limiting. Hand gestures can provide a more natural and ...
