Volume 12 | Issue 5 |

 Abstract

Railway track safety is of paramount importance to prevent accidents involving humans and animals trespassing onto the tracks, as well as natural hazards like landslides or rockfalls. To address these challenges, we propose RailGuard, an innovative system that combines computer vision and sensor technologies for enhanced safety measures. Utilizing a combination of Python, YOLOv3 algorithm, and a range of hardware components including a Raspberry Pi, Arduino Uno, LCD display, ultrasonic sensor, alarm, and DC motor, RailGuard detects and responds to potential threats in real-time. The YOLOv3 algorithm is trained to recognize specific entities such as "person", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", and "giraffe", enabling precise identification of trespassers and animals on the tracks. Upon detection, RailGuard activates the alarm and halts the movement of trains by stopping the DC motor, thereby averting potential collisions and ensuring passenger and animal safety

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 Keywords

Predictive Railway Safety, AI-powered Track Monitoring, Multi-sensor Obstacle Detection, Automated Train Response Systems, Self-regulating Railway Network.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Abstract In recent years, the agricultural sector has witnessed a paradigm shift towards digitalization, driven by the integration of cutting-edge technologies such as edge and fog computing .Smart agriculture is often perceived as one key enabler when considering the twin objectives of eliminating world hunger and undernourishment. This survey delves into the transformative potential of edge and fog computing in revolutionizing smart agriculture practices. By pushing computational tasks closer to the data source, edge computing enhances real-time data processing, enabling timely decision-making and resource optimization on farms. Edge computing offers a potentially tractable model for mainstreaming smart agriculture. Additionally, fog computing extends this capability by leveraging intermediate nodes to further distribute computational tasks and manage data flows efficiently. Through a comprehensive analysis of existing literature, this survey explores the key challenges, opportunities, and emerging trends in the adoption of edge and fog computing in smart agriculture. Moreover, it investigates the integration of these technologies with other emerging technologies such as Internet of Things (IoT), artificial intelligence (AI), and block chain to create robust and sustainable agricultural ecosystems. By shedding light on the current state and future prospects of edge and fog computing in agriculture, this survey aims to provide valuable insights for researchers, practitioners, and policymakers to harness the full potential of these technologies for sustainable and efficient agricultural production.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Smart agriculture, Edge computing, Fog computing, Internet of Things (IoT), Digitalization.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Recent years have witnessed remarkable progress in agriculture sciences, driven by technological innovations and evolving research paradigms. This abstract provides an overview of key advancements shaping the agricultural landscape. Precision agriculture has emerged as a cornerstone, leveraging technologies like GPS, sensors, and AI to optimize resource use and enhance productivity while reducing environmental impact. Genetic engineering and genomics have revolutionized crop breeding, enabling the development of resilient varieties with improved traits. Vertical farming and controlled environment agriculture have redefined traditional farming practices, offering sustainable solutions for urban food production. Moreover, bioinformatics and big data analytics have empowered researchers to harness vast datasets for informed decision-making and predictive modeling. Agroecological approaches promote biodiversity and soil health, while remote sensing technologies enable real-time monitoring of crops and environmental conditions. Additionally, alternative protein sources and water management technologies address pressing challenges related to food security and resource sustainability. Collectively, these advancements underscore the transformative potential of agriculture sciences in addressing global challenges and fostering a more sustainable future.

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 Keywords

Agricultural Science, Enhancing Sustainability, Environmental Science

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

An intrusion detection system, or IDS, is designed to be a software program that keeps an eye on system or network activity and alerts users when anything suspicious is happening. Concerns regarding how to safely transmit and preserve digital information are raised by the internet's explosive expansion and use. In order to obtain important information, hackers today employ a variety of attack techniques. New things like viruses and worms being imported as the internet becomes more prevalent in society. In order to create system vulnerabilities, malicious individuals employ a variety of methods, such as password cracking and the detection of unencrypted information. As a result, users require security to protect their system from hackers. One of the most often used security methods is the firewall mechanism, which is intended to keep private networks isolated from public networks. IDS are utilized in credit card fraud, medical applications, insurance agencies, and network-related operations. These assaults are detectable with the aid of numerous intrusion detection techniques, methods, and algorithms. This paper's primary goal is to present a comparative analysis of intrusion detection methods utilizing different deep learning and machine learning approaches. In this paper we can

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 Keywords

-- Intrusion detection, Machine learning, Deep learning, Convolutional neural network, Network datasets

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This research outlines an incredibly easy-to-use and effective technique for identifying Malayalam text in color, natural scene photos captured offline using a mobile phone camera. Important phases in text understanding from natural scene photographs are Malayalam text detection, text segmentation, skew correction of the discovered text, and character recognition.For a variety of applications, including text translation in other nations and assistive technology for the blind, text understanding in natural scene photos is crucial.Malayalam's high degree of complexity in comparison to other languages has made it difficult to learn. The experimental findings demonstrate that our approach can effectively extract and recognize text with little complexity, making it suitable for usage in mobile devices with constrained capabilities.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Skew angle estimation, text detection, text segmentation, text recognition

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 Abstract

Hydrophobic silica aerogel is known as "Frozen Smoke". Its lightweight porous materials exhibit exceptional properties. This abstract provides an overview of hydrophobic silica aerogel. It's synthesized through a sol-gel process where it's modified to hydrophobic properties by replacing -OH groups. This hydrophobicity also preserves the aerogel's low density, high porosity, and thermal insulating properties. These allergies have multiple applications due to their extraordinary properties. In aerogel properties low density and high porosity are applicable in lightweight materials such as in spacecraft technology. In this abstract, we get the details of the preparation method, characteristics, hydrophobicity of silica aerogel, Chemical Reaction, and their application in the future perspective research technology.

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 Keywords

Silica aerogel, parameters, Hydrolysis and condensation

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In today's world, where people are increasingly concerned about their health and dietary choices, the primary dilemma we face is the quantity and quality of the ingredients we add to our meals. As our understanding evolves, we recognize that eating isn't just about keeping hunger away; it's also about mindful intake, essential for maintaining health and fitness. In this endeavor, we use modern technologies such as machine learning and human pose estimation techniques to address these challenges. Our users receive personalized diet plans, carefully tailored to consider the nutritional content of the recommended foods, helping them in achieving their dietary objectives. Users provide information such as age, weight, height, food preferences, and goals, which is utilized to calculate their BMI and prescribe a customized diet plan, with the flexibility to adjust according to their preferences. However, a healthy diet alone is not sufficient for fitness; a proper workout regimen is also crucial. Thus, users are provided with a curated set of exercises, monitored through Human Pose Estimation technology. This technology enables the identification of key points and angles between landmarks, facilitating the monitoring of exercise execution to ensure correct form and repetitions, ultimately guiding users toward their fitness goals.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

BMI(Body Mass Index), K-Means Clustering, OpenCV, Media pipe

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Social interaction impairments are core to Autism Spectrum Disorder (ASD), including atypical eye contact in social contexts. Early ASD screening remains challenging, while traditional methods like EEG or MRI can be impractical for young children. Eye-tracking offers a child-friendly alternative for investigating visual attention patterns in ASD. This study develops a low-cost eye-tracking system using WebGazer and heatmap.js libraries to facilitate early ASD screening. The system aims to improve accessibility and detection rates compared to existing methods by analyzing children's eye gaze patterns.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Autism, Autism Spectrum Disorder, Eye-Tracking, Early Detection

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Semantic similarity measures play a crucial role in various natural language processing tasks, aiding in tasks such as information retrieval, text classification, and semantic search. This paper provides a comprehensive review of semantic similarity measures, examining their methodologies, applications, and performance. We discuss different approaches to measuring semantic similarity, including knowledge-based, corpus-based, and hybrid methods, highlighting their strengths, limitations, and comparative evaluations. Additionally, we explore the challenges and future directions in semantic similarity research, aiming to provide insights for researchers and practitioners in the field of natural language processing.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Semantic similarity, natural language processing, knowledge-based methods, corpus-based methods, hybrid methods.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In the rapidly evolving field of artificial intelligence, the ability to generate data Visualizations through natural language queries represents a significant advancement. This presentation explores the capabilities of models like GPT-3, ChatGPT, and Codex in transforming textual descriptions into graphical data insights. By leveraging deep learning and transformer architectures, these models facilitate an intuitive interface for data interaction, enhancing decision-making and analytical processes.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

data Visualizations

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Creative Commons Attribution 4.0 and The Open Definition