Volume 12 | Issue 5 |

 Abstract

Sepsis is a potentially life-threatening condition that occurs when the body's response to an infection causes inflammation throughout the body. This inflammation can trigger a cascade of changes that can damage multiple organ systems, leading to organ failure and death if not treated promptly. Early recognition and aggressive treatment with antibiotics and supportive care are crucial for improving outcomes in septic patients. The " Accurate Prediction of Sepsis in ICU Patients" is a project that combines awareness and predictive modeling to address sepsis, a life-threatening condition commonly encountered in intensive care units (ICUs). This project is a robust awareness campaign designed to educate both the general public and healthcare professionals about sepsis. With focusing on generating awareness about sepsis, can lead to early detection and seeking medical help. By bringing the limelight on this disease, it can potentially save lives. Concurrently, advanced machine learning techniques, specifically random forest algorithms, are employed to construct a predictive model for sepsis. This model undergoes meticulous fine-tuning to ensure accurate identification of sepsis risk in ICU patients. It uses a dataset for training the predictive model. The integration of the Sequential Organ Failure Assessment (SOFA) score, including the quick SOFA (qSOFA) criteria, enhances predictive accuracy. The qSOFA criteria play a crucial role in rapid risk assessment for early intervention. Moreover, the project maintains a dedicated website that serves as an essential platform for sepsis education and the dissemination of the predictive model to the medical community.

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 Keywords

Sepsis, ICU, SOFA, qSOFA, Random Forest, Predictive Modelling, Awareness Campaign

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Pure essential oils are concentrated oils generated from a variety of natural plants, flowers, plant roots, seeds, resins, plant exterior tissue, trees or shrubs, and fruit rinds. These oils are well-known among humans for their benefits to the body, skin, and spirit. These oils are also commercially employed due to their superior medicinal or odoriferous characteristics. To research extraction strategies available to extract oils from plants and trees, to come across pros and disadvantages of a few extraction methods, selection and efficiency of a single method. The method used to extract essential oil from plants is critical, as some processes employ solvents that can harm the healing benefits of plants and trees. There are different extraction procedures, but the oil's quality and production never remain consistent. The Soxhlet apparatus technique was used in this investigation because of its mild extraction conditions and low operating cost. Steam is a critical component in the oil extraction process. Extraction of essential oils using diverse methods and innovative techniques reduces the risk of losing the vital component of plants and trees, reduces chemical risk, shortens extraction time, is environmentally friendly, and improves the quality and production of essential oils.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Eucalyptus oil, Steam Distillation using Soxhlet apparatus

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Creative Commons Attribution 4.0 and The Open Definition

 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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Creative Commons Attribution 4.0 and The Open Definition

 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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Creative Commons Attribution 4.0 and The Open Definition

 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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Creative Commons Attribution 4.0 and The Open Definition

 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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Creative Commons Attribution 4.0 and The Open Definition

 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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Creative Commons Attribution 4.0 and The Open Definition

 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