Volume 11 | Issue 6 |

 Abstract

The performance of Yolo V8 when compared to traditional approaches such as infrared imaging has been thoroughly studied and documented. Studies have found that the algorithm is more accurate in detecting fires before they become too severe or spread too far, allowing for quicker response times from firefighting units. For solving this problem, we developed a detection model called upgraded-YOLO based on the state-of-the-art object detection method of YOLOv8. The proposed model is able to perform real-time object detection. Firstly, the data will be collected and data labelling is performed. Unlike other objects detection models that divide image into multiple regions and perform object detection on multiple regions, YOLOv8 performs object detection on entire image in one forward pass through the network. This makes it fast and efficient for real time applications. A comparative study with several contemporary object detectors proved that the Upgraded-YOLO performed better. Therefore, the proposed detection technology can be deployed in a monitoring center in order to protect a strategic installation even in low-visibility conditions.

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 Keywords

forest fire, detection, smoke alert, computer vision, yolo model, deep learning.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

CaudaEquina Syndrome (CES) is a rare emergency disease when the ends of the spinal cord, namely the lumbar and sacral spinal nerves, get compressed, typically as a result of disc herniation or trauma. Bowel incontinence, bladder incontinence leading to UTI, high grade ulcers in the gluteal area, and saddle anaesthesia are some of the notable CES complications. One of the most uncommon neurosurgical crises is this one. A surgical operation termed a lumbar laminectomy, which decongests the lumbar spinal nerves, is the only cure for Caudaequina syndrome. In this case study, we will talk about and assess the long-term effects of caudaequina syndrome, which affect a variety of spheres of life. In this study, the relative Quality of Life (QoL) of patients is compared to the long-term complications of caudaequina syndrome.

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 Keywords

CaudaEquina Syndrome, Quality of life, Complications, Saddle anesthesia, Rehabilitation

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

UCHA SIKSHA ME KARYRAT SEVAPURV PRSIKSHAN TATHA SEVARAT PRSIKSHAN PRAPT SIKSHAKO KI SIKSHAN SAILLY KE PRATI VIDHYARTHIO KI RAY KA AK ADHYAYAN

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 Keywords

UCHA SIKSHA ME KARYRAT SEVAPURV PRSIKSHAN TATHA SEVARAT PRSIKSHAN PRAPT SIKSHAKO KI SIKSHAN SAILLY KE PRATI VIDHYARTHIO KI RAY KA AK ADHYAYAN

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 Abstract

Obesity and Overweight is a common and severe disease in most of adults and children, which refers to abnormal or excessive accumulation of fat that causes serious health problems. In a study in 2019 around 38.2 million children under the age of 5 were obese or overweight. There is a rise in the number of overweight children under the age of 5 in Africa by nearly 24% since 2000. Nearly half of the children under the age of 5 in Asia were overweight or obese in 2019. The best way to treat obesity is to follow a reduced-calorie diet. Manually tracking the food and calories in our food is a tedious task. There are Convolutional Neural Network (CNN) models such as InceptionV3 and InceptionV4 to recognize the food items. But the existing CNN-based models are less accurate, computationally expensive and require high computational power. Here we are proposing a Convolutional Neural Network algorithm called YOLOV8, which is mostly used for object detection, to detect food items. This paper aims to develop a model that recognizes the food item and estimates its calorie values. This system provides a platform where users can estimate the number of calories in a food item by just providing pictures or videos of the food item and hence they can track the number of calories in their diet.

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 Keywords

healthy diet, nutrition, calorie count, computer vision, yolo model, deep learning.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Today, the parking industry is being transformed by new technologies that are allowing cities to reduce rates of congestion significantly. This paper describes an Internet of Things (IoT) based parking sensing System that deploys a robust outdoor vehicle localization and recognition methodologies. Although parking occupancy monitoring systems have made a considerable progress, smart parking payment is rarely studied in smart parking research. This paper proposes a new low-cost sensor system allowing real-time parking occupancy monitoring along with parking payment without requiring any user/driver interaction. The proposed on-board by using JETSON NANO DEVELOPER KIT. It has benefits in terms of detection and payment reliability, and reduced expense by reducing the system complexity, infrastructure investment and battery replacement expense. In this that kit will record the vehicle entry and show the parking slots available. Later It will note down parking entry time and exit time and generate bill automatically. In this multiple areas parking slots data will be displayed by person in a single place. Camera will capture the input and bill will be displayed by machine . Total work is done by using JETSON NANO KIT and operated through 5v of DC voltage to the kit.

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 Keywords

Vehicle Localization, IoT, Parking

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Creative Commons Attribution 4.0 and The Open Definition

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Postpartum depression (PPD) is among the most prevalent complications following childbirth, underscoring the importance of identifying effective treatments to promote optimal outcomes for mothers, infants, and families. When determining appropriate treatment for PPD, factors such as the severity of depression, whether the mother is breastfeeding, and the mother's preferences should be taken into account. Around 40% of new mothers experiencing various types of postpartum mood disorders, including symptoms of depression, before and during pregnancy. Maternal postpartum depression (PPD) is a common and debilitating complication of childbirth, often underdiagnosed and undertreated.The strongest risk factor for postpartum depression is a history of mood or anxiety disorders, particularly if the symptoms are active during pregnancy.Early detection and intervention are crucial in the treatment of postpartum depression due to the heightened vulnerability of infants in the early weeks of life. This article will provide a brief outline on PPD and helps in early detection and management.

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 Keywords

Depression, Postpartum Depression, Postpartum Complications, Baby blues symptoms, Postpartum Psychosis.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Inherited retinal diseases cause several visual deficits in children. They often cause blindness in childhood. Machine learning is one of the most widely used concepts around the world. It will be essential in the healthcare sector as it is helpful for doctors to fasten the diagnosis. The objective of this paper is to build a Machine learning model for detection of genetic eye diseases. Based on the data obtained from pupillometer, the model predicts the presence of an eye disease. The proposed work deals with neural networks, a machine learning concept. We are implementing two algorithms-SVM (an existing model) and the other is a new way using ELM (Extreme Learning Machine) algorithm. We analyse and predict the result whether the patient has a retinal disease or no disease using this algorithm. ELM algorithm gives more accuracy for the prediction. This prediction will make the detection of disease faster. This model can be helpful to the medical practitioners at their clinic as a decision support system.

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 Keywords

Inherited disease, visual defects, infants, SVM, ELM,machine learning.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Drowsiness describes the state of being drowsy or sleepy. A person experiencing drowsiness may feel tired or sluggish and have difficulty staying awake. According to Central Road Research Institute (CRRI),40% of road accidents are caused due to drive drowsiness.Nowadays, more and more professions require long-term concentration. Drivers must keep a close eye on the road, so they can react to sudden events immediately. Driver fatigue often becomes a direct cause of many traffic accidents. Therefore , there is a need to develop the systems that will detect and notify a driver of her/him bad psychophysical condition, which could significantly reduce the number of fatigue-related car accidents. Based on that large dataset, we developed and evaluated a feature selection method based on the YOLO algorithm for the driver's state classification.

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 Keywords

driver safety, drowsiness, computer vision, yolo model, deep learning.

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 Abstract

A method for designing an SSSC-based stabilizer to damp inter-area oscillations is proposed in this paper. To reach this goal, a novel objective function was employed. The displacement of critical modes and the gain (control cost) of the stabilizer are taken into account in the given objective function. In order to have an SSSC-based stabilizer with minimum phase structure, appropriate constraints are calculated and appended to the objective function. For the robust design of an SSSC-based stabilizer, different operating conditions are simultaneously considered. A hybrid optimization algorithm (hGWO-GA) is proposed by combining the grey wolf optimizer (GWO) algorithm and the genetic algorithm (GA). The proposed method was tested on two standard test systems of 4 and 50 machines. The eigenvalues analysis and nonlinear simulations prove that the introduced approach can efficiently enhance the damping of inter-area oscillations. The obtained results show the superiority of the SSSC phase-based stabilizer compared to the magnitude-based stabilizer in damping inter-area oscillations. The results also show that the performance of the proposed hGWO-GA optimization algorithm is superior to the GWO, GA, ACO, and PSO algorithms. Considering the uncertainties in the design process is the future scope of this work.

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 Keywords

Particle swarm optimization, transient stability, power system oscillations, SSSC.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This research article delves into the advantages, difficulties, and approaches related to utilizing social media platforms for the improvement of English language communication skills. The study emphasizes the accessibility, interactivity, and authentic language usage offered by social media. It investigates how social media aids in language practice, interaction, and engagement, providing avenues for honing different communication skills. The integration of social media in language learning can be achieved through various strategies, such as employing discussion forums, blogs, microblogging platforms, audio, and video platforms, as well as incorporating social media assignments and projects into language courses

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 Keywords

: social media, English language learning, writing skills, communication, creativity

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Creative Commons Attribution 4.0 and The Open Definition