Journal of Recent Innovations in Computer Science and Technology

The development of intelligent systems for the monitoring, exploration, and administration of underwater environments is made possible by the Internet of Underwater Things (IoUT), which is a revolutionary development in marine and environmental research. This thorough research examines the advancements, challenges, and promise of IoUT with a focus on its applications in domains such as resource extraction, the scienc...

In the rapidly evolving digital age, voice assistants have become an indispensable tool for enhancing user interaction with technology. This paper explores the design, development, and functionality of a Python-based voice assistant system, leveraging cutting-edge advancements in Artificial Intelligence (AI), Natural Language Processing (NLP), and Machine Learning. The voice assistant is designed to bridge the gap be...

Emergency management operations increasingly depend on cutting-edge technological solutions to support better disaster response, resource coordination, and recovery. This research uses artificial intelligence (AI) and cloud-based collaborative platforms to enhance emergency management in pre-disaster, disaster, and post-disaster phases. AI predictive abilities allow for early risk estimation, enhancing disaster forec...

Object detection is a crucial component of autonomous driving technology. Accurate and real-time detection of every object on the road is required to ensure the safe operation of vehicles at high speeds. In recent years, there has been a lot of research into how to balance detection speed with accuracy. Real-time object detection is one of the important technologies applied to autonomous vehicles that allow vehicles...

Skin disorders are thought to be common in humans and carry several invisible risks, including the potential to cause psychological sadness, lower self-esteem, and, in more serious cases, skin cancer. Medical professionals must diagnose these skin conditions, but doing so requires highly sophisticated diagnostic tools because they have trouble seeing clearly while examining images of the conditions. This paper focuse...

The creation of intelligent transportation systems, such as autonomous driving and traffic monitoring, is dependent on precise vehicle recognition. Autonomous vehicles detect and recognize objects in real-time, such as pedestrians, other vehicles, traffic signs, and obstacles.  This paper improves the object detection ability of autonomous vehicles (AVs') by integrating technologies including YOLOv10 and multi-modal...

Facial recognition and object detection are critical computer vision problems used in security, surveillance, autonomous systems, and human-computer interaction. This study investigates the of machine learning techniques. Use of facial recognition and object detection in deep learning has been develop to high level using machine learning. In enhancing the performance and enhancing the generalization of the model, thi...

This paper investigates the use of Variational Autoencoders (VAEs) as a deep learning-based generative framework for AI-assisted image processing in game design, focusing on the procedural generation of stylized visual assets. Its application in AI-assisted image generation in game design for the generation of diverse stylized visual assets is explored in this paper. In order to learn stylistic consistent content and...

Artificial intelligence (AI) has significantly influenced decision-making processes across various domains, including law, healthcare, and autonomous systems. Despite its advancements, AI models face several critical challenges, including sensitivity to input variations, hyperparameter tuning complexities, coordination issues in multi-agent environments, and fundamental differences in decision-making compared to huma...

Applications of machine learning are revolutionizing data processing and decision-making, which is having a significant effect on the global economy. Given the worldwide food supply crisis, agriculture is one of the industries where the effects are most noticeable. This paper focuses on crop yield prediction based on pattern analysis with the help of the machine learning approach, which focuses on data acquisition, p...

This study offers a deep learning-based method for text extraction from video frames, addressing issues like motion blur, variable text orientations, and background noise. Traditional optical character recognition (OCR) methods like Tesseract suffer from these problems, while contemporary deep learning models offer notable advancements. The suggested model uses Convolutional Neural Networks (CNNs) to identify text re...

As global food demand increases amid climate change and dwindling natural resources, there is an urgent need to adopt more intelligent and sustainable farming practices. Without real-time awareness and optimization, traditional ways of farming methods do not make full use of resources and bring about below-average crop results. With Internet of Things (IoT), farmers can automate tasks, watch over operations all the t...

In this paper, show how BI applications can lead to sales forecasting and organizational performance improvements based on the case of a retail store. To detect patterns, performance issues and actionable results, a relatively simple business intelligence model based on descriptive, diagnostic and predictive analytics was applied. Although descriptive analytics revealed regional sales differences, diagnostic analytic...

 In many parts of the world, women’s safety in public, educational, and professional settings is still a major concern. Conventional safety measures frequently depend on wearable technology or smartphone apps, which aren’t always reliable in an emergency or covert enough to keep potential criminals from spotting them. This study proposes a clever and affordable women’s safety ID card that offers both proactive and re...

The needs of the growing information technology industry have made data centers, digital platforms and cloud computing achieve a never-before-seen growth, generating an enormous energy demand and environmental footprint. This paper follows the fundamental ideas of green computing best practices and goes further to detail how they can be converted to sustainability in IT services. With the second wave of green IT, the...

Cloud gaming, as a paradigm in which games are rendered and streamed over remote servers, promises to transform the digital entertainment sector, as it eliminates the reliance on powerful local hardware. The paper discusses the trend of the traditional PC/ console game to modern cloud-based systems with essential performance indicators such as latency, bandwidth demands, and user experience. The mixed-method approach...

The proliferation of an online learning environment has opened up tremendous potential in the sphere of education, but has also posed significant problems to examination integrity. Conventional services of online proctoring, i.e., manual webcam supervision and lockdown browsers, failed to provide fairness since they were either inefficient or rather effortless to manipulate. This paper introduces an AI-based online e...

In the evolving landscape of digital communication, the need for secure, lightweight, and efficient data protection mechanisms is more critical than ever, particularly for bandwidth-constrained and privacy-sensitive applications. This paper proposes a novel hybrid cryptographic framework that synergistically combines three powerful techniques: Elliptic Curve Cryptography (ECC), DNA-based cryptography, and LZ77 data c...

This paper focuses on deep learning strategies for the assessment of satellite images for the overall assessment of disaster. The study primarily examines the ability to correctly identify those places that can be affected by various classes of natural disasters. By imbuing a wide range of satellite images with seamless integration, a new disaster detection system is designed assisted by a set of models, a prime exam...

Although chest X-rays (CXRs) are still a vital diagnostic tool for detecting thoracic disease, their interpretation can be challenging because of their multilevel findings and contradictory visual patterns.  As a result, we examine how well deep convolutional neural networks (CNNs) with transfer learning perform automated multi-label classification of CXRs.  Extensive preprocessing and augmentation techniques were us...