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

Diesel Engine Performance on Chlorella vulgaris Biodiesel

843-845This research paper highlights the results of analyses conducted to assess performance characteristics of an unmodified CI engine fuelled by a new third-generation biodiesel derived from Chlorella vulgaris algae oil and its mixtures with neat diesel. A single-cylinder direct injection ignition compression engine was used to prepare and test three separate fuels at a rated speed of 1500 rpm. Parameters such as torque, net power, specific fuel consummation and thermal efficiency were evaluated for the engine output. Results from the experiment show that use of algae oil blend in diesel engine has performed better for the studied parameters

Diesel Engine Emission Characteristics Study using Algae Biofuel

547-551The purpose of this study is to determine the benefits of using algal biodiesel blends in motorized vehicles, as well as knowing the extent to which the biodiesel mixture can reduce its emissions to suppress the impact on the environment. The engine employed was coupled to a dynamometer and evaluated at a speed of 1500 rpm at various degrees of load. FTIR spectrum of the oil and biodiesel was studied. Various emission parameters such as carbon monoxide, unburnt hydrocarbon, nitrogen oxides and smoke opacity were analyzed using AVL emission analyzer. The results of this study have indicated a mixture of quality effects of biodiesel against diesel. In various scenarios the biodiesel blends showed a significant reduction in environmental impact

Combustion Analysis using Third Generation Biofuels in Diesel Engine

449-452In this study the use of Chlorella vulgaris biodiesel blends are tested in a naturally aspirated dual fuel diesel engine with various load conditions at a rated speed of 1500 rpm. In the engine, the test fuels such as B20 injection, B20 blending and diesel were prepared and tested. The combustion characteristic has provided a better understanding of the operation of the engine in dual-fuel mode. The combustion analysis was done at the injection timings of 23° angle before top dead centre with an injection pressure of 220 bars. The results show that 20% blend of Chlorella vulgaris microalgae biodiesel with 80% diesel produced higher cylinder pressures, heat release rate, lower combustion duration, and ignition delay as compared to diesel fuel. The experimental outcomes indicate that the usage of algae oil blend in diesel engine is a feasible option

Diesel Engine Performance on Chlorella vulgaris Biodiesel

This research paper highlights the results of analyses conducted to assess performance characteristics of an unmodified CI engine fuelled by a new third-generation biodiesel derived from Chlorella vulgaris algae oil and its mixtures with neat diesel. A single-cylinder direct injection ignition compression engine was used to prepare and test three separate fuels at a rated speed of 1500 rpm. Parameters such as torque, net power, specific fuel consummation and thermal efficiency were evaluated for the engine output. Results from the experiment show that use of algae oil blend in diesel engine has performed better for the studied parameters

Development and mapping of Simple Sequence Repeat markers for pearl millet from data mining of Expressed Sequence Tags

<p>Abstract</p> <p>Background</p> <p>Pearl millet [<it>Pennisetum glaucum </it>(L.) R. Br.] is a staple food and fodder crop of marginal agricultural lands of sub-Saharan Africa and the Indian subcontinent. It is also a summer forage crop in the southern USA, Australia and Latin America, and is the preferred mulch in Brazilian no-till soybean production systems. Use of molecular marker technology for pearl millet genetic improvement has been limited. Progress is hampered by insufficient numbers of PCR-compatible co-dominant markers that can be used readily in applied breeding programmes. Therefore, we sought to develop additional SSR markers for the pearl millet research community.</p> <p>Results</p> <p>A set of new pearl millet SSR markers were developed using available sequence information from 3520 expressed sequence tags (ESTs). After clustering, unigene sequences (2175 singlets and 317 contigs) were searched for the presence of SSRs. We detected 164 sequences containing SSRs (at least 14 bases in length), with a density of one per 1.75 kb of EST sequence. Di-nucleotide repeats were the most abundant followed by tri-nucleotide repeats. Ninety primer pairs were designed and tested for their ability to detect polymorphism across a panel of 11 pairs of pearl millet mapping population parental lines. Clear amplification products were obtained for 58 primer pairs. Of these, 15 were monomorphic across the panel. A subset of 21 polymorphic EST-SSRs and 6 recently developed genomic SSR markers were mapped using existing mapping populations. Linkage map positions of these EST-SSR were compared by homology search with mapped rice genomic sequences on the basis of pearl millet-rice synteny. Most new EST-SSR markers mapped to distal regions of linkage groups, often to previous gaps in these linkage maps. These new EST-SSRs are now are used by ICRISAT in pearl millet diversity assessment and marker-aided breeding programs.</p> <p>Conclusion</p> <p>This study has demonstrated the potential of EST-derived SSR primer pairs in pearl millet. As reported for other crops, EST-derived SSRs provide a cost-saving marker development option in pearl millet. Resources developed in this study have added a sizeable number of useful SSRs to the existing repertoire of circa 100 genomic SSRs that were previously available to pearl millet researchers.</p

Development of calcium titanium oxide coated silicon solar cells for enhanced voltage generation capacity

Depletion of fossil fuel based energy sources drive the present scenario towards development of solar based alternative energy. Polycrystalline silicon solar cells are preferred due to low cost and abundant availability. However, the power conversion efficiency of polycrystalline silicon is lesser compared to monocrystalline one. The present study aims at analyzing the effect of calcium titanium oxide (CaTiO3) antireflection (AR) coating on the power conversion of polycrystalline solar cells. CaTiO3 offers unique characteristics, such as non-radioactive and non-magnetic orthorhombic biaxial structure with bulk density of 3.91 g/cm3. CaTiO3 film deposition on the solar cell substrate has been carried out using Radio Frequency (RF) magnetron sputter coating technique under varying time durations (10 min to 45 min). Morphological studies proved the formation of CaTiO3 layer and respective elemental percentages on the coated substrate. Open circuit voltage studies were conducted on bare and coated silicon solar substrates under open and controlled atmospheric conditions. CaTiO3 coated on a solar cell substrate in a deposition time of 30 min showed 8.76 % improvement in the cell voltage compared to the bare solar cell

Etiopathogenesis and Clinical Management of Inflammatory Bowel Disease

ABSTRACT Inflammatory bowel disease, which includes Crohn&apos;s disease and ulcerative colitis, is a relapsing and remitting condition characterized by chronic inflammation at various sites in the GI tract, which results in diarrhea and abdominal pain. This review summarizes the types, epidemiology, aetiology, risk factors, pathogenesis, clinical signs and symptoms, complications, diagnosis and recent advances in clinical management of inflammatory bowel disease