Accident Detection and Alert System Using Big Data Analytics

Road accidents are a serious hazard to life and limb and result in considerable financial damages on a global scale. For reducing reaction times and ensuring that victims receive aid quickly, quick and effective accident detection technologies are essential. A brief description of a crash monitoring and warning system that uses big data analytics to improve traffic safety is provided in this abstract. To correctly identify and anticipate accidents, the proposed system incorporates a variety of info sources such as real-time traffic data, meteorological information, and car telematics. The system can analyze huge amounts of disparate data in real-time by using modern data analytics techniques like machine neural networks and predictive modeling. Road accidents are becoming more commonplace across the world, which calls for the creation of cutting-edge technologies that can quickly identify incidents and notify the appropriate authorities for fast help. The use of big data analytics has developed in recent years as a viable strategy to improve accident identification and response. These systems are able to recognize possible accidents and produce alerts in real time by using enormous quantities of varied sources of data, such as real-time traffic data, meteorological conditions, and vehicle telematics. Data Preprocessing: To guarantee quality and consistency, collected data is preprocessed. This entails managing missing values, data normalization, noise reduction, and data cleaning. Relevant elements, such as traffic patterns, weather patterns, types of roads, and vehicle behavior, are retrieved from preprocessed data. Techniques for feature engineering turn unstructured data into useful representations. Alert generation: When an accident is detected, alerts are created and transmitted to the appropriate parties, such as medical professionals, law enforcement, and passing cars, with details on the accident's location, level of seriousness, and suggested next steps. System assessment: To determine the efficiency of the system in accurately detecting accidents and producing timely alerts, performance assessment is carried out using metrics including reliability, precision, recollection, and reaction time. Taken of   Radar Model Example, Azimuth, Elevation, Horizontal resolution, Maximum detectable speed Taken of   Evaluation parameters:  Radar type, Short Range, Radar Mid-Range, Radar Long Range Radar Model Example, Azimuth, Elevation, Horizontal Resolution, and Maximum Detectable Speed are alternative parameters. These parameters are defined in the materials and methods section. The figure below represents an accident detection and alert system using big data analytics with performance value, weightage, weighted normal decision matrix, and preference score. Every parameter is monitored to some extent in the graph, and the evaluation parameters are also precisely stated in the materials and techniques section. The Figure below represents an accident detection and alert system using big data analytics with performance value, weightage, weighted normal decision matrix, and the preference score the alternative parameters are Radar Model Example, Azimuth, Elevation, Horizontal Resolution, and Maximum Detectable speed which is defined as in the materials and methods section. The Evaluation parameters are also clearly defined in the materials and methods section, every parameter is measured to a certain degree in the graph

Computer-Aided Detection of Skin Cancer Detection from Lesion Images via Deep-Learning Techniques

More and more genetic and metabolic abnormalities are now known to cause cancer, which is typically fatal. Any particular body part may become infected by cancerous cells, which can be fatal. One of the most prevalent types of cancer is skin cancer, which is spreading worldwide.The primary subtypes of skin cancer are squamous and basal cell carcinomas, as well as melanoma, which is clinically aggressive and accounts for the majority of fatalities. Screening for skin cancer is so crucial.Deep Learning is one of the best options to quickly and precisely diagnose skin cancer (DL).This study used the Convolution Neural Network (CNN) deep learning technique to distinguish between the two primary types of cancers, malignant and benign, using the ISIC2018 dataset. The 3533 skin lesions in this dataset range from benign to malignant, and nonmelanocytic to melanocytic malignancies. The images were initially enhanced and edited using ESRGAN. The preprocessing stage involved resizing, normalising, and augmenting the images. By combining the results of numerous repetitions, the CNN approach might be used to categorise images of skin lesions. Several transfer learning models, such as Resnet50, InceptionV3, and Inception Resnet, were then used for fine-tuning. The uniqueness and contribution of this study are the preprocessing stages using ESRGAN and the testing of various models (including the intended CNN, Resnet50, InceptionV3, and Inception Resnet). Results from the model we developed matched those from the pretrained model exactly. The efficiency of the suggested strategy was proved by simulations using the ISIC 2018 skin lesion dataset. In terms of accuracy, the CNN model performed better than the Resnet50 (83.7%), InceptionV3 (85.8%), and Inception Resnet (84%) models

Wetting Characteristics of Oleate Monolayers Adsorbed at Calcium Mineral Surfaces:a Molecuar Dynamics Study

In this paper we present our Molecular Dynamics (MD) simulations results on the wettability characteristics of the surfaces of three calcium minerals namely, fluorite, calcite and fluorapatite with an adsorbed oleate monolayer in the presence of water. The contact angle of water on these oleate-adsorbed mineral surfaces are computed and compared with experimental values available in the literature. The modeled adsorbed oleate monolayers on calcite (104), fluorite 11 1 1 ) and fluoroapatite (100) are packed so as to achieve the most favorable configuration, that is, 100%, 67% and 75% packing respectively

Surface and Flotation Characteristics of Spodumene:A Molecular Modeling Approach

Surface characteristics of spodumene [LiAl(SiO3)27 and aluminosilicates (feldspar [KA1Si3041 and muscovite IK2A14(A1257602a)(01-00 are modeled using molecular simulations. Surface energies are computed for various cleavage planes of these minerals and compared with those measured experi-mentally. Adsorption mechanisms of oleate collector on spodumene in the selective flotation of spodumene from these aluminosilicates are studied using molecular model-ing methods. Further, the wettability characteristics of spodumene are quantified by computing the contact angle of a drop of water on the adsorbed oleate layer. These simul-ated computed contact angles are compared with those measured experimentally

Optimization of Chlorella vulgaris Biodiesel usage in Diesel Engine

750-752The experimental investigation has been carried out on the diesel engine using Chlorella vulgaris algal biodiesel as blends with neat diesel. Two input parameters, such as load and fuel and four reaction parameters such as hydrocarbons (HC), carbon monoxide (CO), nitrogen oxides (NOx), and smoke, were used for the emissions on the engine. The experimental results show that there's been less pollution from using algae oil blend in the diesel engine. It is employed to find the optimum level for multi-objective prediction carried out for CO, HC, NOx, and smoke. The outcomes after grey relational analysis (GRA) were tested using the response surface methodology (RSM), wherein the optimal combination has been found out using a particular behaviour

Radial variation of elastic properties of SCS-6 silicon carbide fiber

The upper and lower bounds of the bulk, shear, and Young's moduli are calculated point-by-point along the radius of SCS-6 silicon carbide fibers using the Hashin-Shtrikman equations from considerations of the Auger spectra along the fiber radius. The calculated values are in agreement with measurements of the average Young modulus obtained over relatively large radial regions of the fiber using scanning acoustic microscopy (SAM). The validity of the Hashin-Shtrikman bounds calculations is enhanced by the agreement found between experimental SAM V(z) curves for fixed z along the fiber radius and the theoretical bounds of such curves obtained from the bounds of the calculated bulk and shear moduli

Role of Emergency Gastro Intestinal Ostomies in Present Scenario

INTRODUCTION: Intestinal stomas are an integral part of gastrointestinal surgery. An intestinal stoma is an opening of the intestinal or urinary tract onto the abdominal wall, constructed surgically or appearing inadvertently. A colostomy is a connection of the colon to the skin of the abdominal wall. An ileostomy involves exteriorization of the ileum on the abdominal skin. In rare instances, the proximal small bowel may be exteriorized as a jejunostomy. Stomas may be life saving in the treatment of bowel perforation or severe sepsis. The burden of care to support and counsel these patients is considerable. Although short term support is needed for patients with temporary stoma, the impact on life is considerable. Stomas may need to be permanent for treatment of low rectal cancer or crohn’s disease. 1. To evaluate the epidemiology of emergency gastro intestinal ostomies. 2. To analyse the various etiology and commonest cause for emergency ostomies. 3. To study the various types of ostomies made. 4. To study the complications of ostomies. 5. To evaluate the morbidity and mortality of patients after ostomy. MATERIALS AND METHODS: This is a prospective study of 42 patients for whom emergency gastrointestinal ostomy was done, in the Department of General Surgery, Government Royapettah Hospital, Kilpauk Medical College, Chennai. All the patients who underwent emergency gastro intestinal ostomy were studied prospectively, their demographic details, clinical features, past medical history, indications for surgery and type of ostomy performed, post operative complications, duration of stay in hospital and outcome. The study period was 25 months from September 2006 to September 2008. Admissions were carried out from outpatients department or through the casualty department as cases of acute abdomen. Majority of these patients (86.60%) presented with abdominal distention, tenderness and abdominal rigidity. There was marked dehydration and toxemia especially in those who presented late in the course of illness. Immediate resuscitative measures were taken in all the patients regardless of age and sex. This comprised maintenance of intravenous line, nasogastric suction, catheterization, intravenous broad spectrum antibiotics and intravenous fluids. The principal diagnostic tools in all the patients were a detailed history and examination, basic blood biochemistry investigations, x-ray chest and x-ray abdomen erect and supine view. The other sophisticated investigations would not be done as majority of patients were taken up for emergency surgery, after due resuscitations laprotomy was performed by a midline incision, and depending upon the intraoperative findings and severity of contamination, type of ostomy was decided. Inclusion All patients who required gastrointestinal ostomy in emergency abdominal surgery were included in the study. Exclusion: Elective surgeries in which ostomies made were excluded Emergency / Elective procedures like feeding jejunostomy / gastrostomy were excluded. CONCLUSION: 1. Patients presenting with peritonitis and severe contamination constitute the most common indication for ostomy, followed by patients with malignancy presenting as intestinal obstruction. 2. Ileal perforation was found to be the most common cause for patients presenting with peritonitis who needed ostomy. 3. Ileostomy, especially split ileostomy, the most common ostomy performed. 4. Peristomal ulceration (skin irritation) – was found to be the most common complication of ostomy. 5. Ostomy was found to be a life saving procedure especially so in moribund patients presenting late in the course of illness