HYPOTHYROID DISEASE ANALYSIS BY USING MACHINE LEARNING

Thyroid illness frequently manifests as hypothyroidism. It is evident that people with hypothyroidism are primarily female. Because the majority of people are unaware of the illness, it is quickly becoming more serious. It is crucial to catch it early on so that medical professionals can treat it more effectively and prevent it from getting worse. Machine learning illness prediction is a challenging task. Disease prediction is aided greatly by machine learning. Once more, unique feature selection strategies have made the process of disease assumption and prediction easier. To properly monitor and cure this illness, accurate detection is essential. In order to build models that can forecast the development of hypothyroidism. In this project, we utilized machine learning approaches such Logistic Regression, Decision Trees, and Naive Bayes. Here we used thyroid function-related measures and characteristics from a UCI Machine Learning Repository dataset. The main goals were to properly assess each machine learning model\u27s performance and fine-tune its hyperparameters. With an accuracy rate of 99.87%, the findings of this study generated the model\u27s ability to predict hypothyroidism were pretty remarkable. This high degree of accuracy shows how useful these machine learning algorithms are as diagnostic v and therapeutic tools for hypothyroid patients early on. This experiment demonstrates the potential of machine learning in healthcare and has an impact on diagnosis. It is crucial that you do this appropriately

Topology-aware GPU scheduling for learning workloads in cloud environments

Recent advances in hardware, such as systems with multiple GPUs and their availability in the cloud, are enabling deep learning in various domains including health care, autonomous vehicles, and Internet of Things. Multi-GPU systems exhibit complex connectivity among GPUs and between GPUs and CPUs. Workload schedulers must consider hardware topology and workload communication requirements in order to allocate CPU and GPU resources for optimal execution time and improved utilization in shared cloud environments. This paper presents a new topology-aware workload placement strategy to schedule deep learning jobs on multi-GPU systems. The placement strategy is evaluated with a prototype on a Power8 machine with Tesla P100 cards, showing speedups of up to ≈1.30x compared to state-of-the-art strategies; the proposed algorithm achieves this result by allocating GPUs that satisfy workload requirements while preventing interference. Additionally, a large-scale simulation shows that the proposed strategy provides higher resource utilization and performance in cloud systems.This project is supported by the IBM/BSC Technology Center for Supercomputing collaboration agreement. It has also received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 639595). It is also partially supported by the Ministry of Economy of Spain under contract TIN2015-65316-P and Generalitat de Catalunya under contract 2014SGR1051, by the ICREA Academia program, and by the BSC-CNS Severo Ochoa program (SEV-2015-0493). We thank our IBM Research colleagues Alaa Youssef and Asser Tantawi for the valuable discussions. We also thank SC17 committee member Blair Bethwaite of Monash University for his constructive feedback on the earlier drafts of this paper.Peer ReviewedPostprint (published version

Comparative Analytics on Chilli Plant Disease using Machine Learning Techniques

This thesis concerns the detection of diseases in chilli plants using machine learning techniques. Three algorithms, viz., Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Multi-Layer Perceptron (MLP), and their variants have been employed. Chilli-producing countries, India, Mexico, China, Indonesia, Spain, the United States, and Turkey. India has the world’s largest chilli production of about 49% (according to 2020). Andhra Pradesh (Guntur) is the largest market in India, where their varieties are more popular for pungency and color. This study classifies five kinds of diseases that affect the chilli, namely, leaf spot, whitefly, yellowish, healthy, and leaf curl. A comparison among deep learning techniques CNN, RNN, MLP, and their variants to detect the chilli plant disease. 400 images are taken from the Kaggle dataset, classified into five classes, and used for further analytics. Each image is analyzed with CNN (with three variants), RNN (with three variants), and MLP (with two variants). Comparative analytics shows that the higher number of epochs implies a higher execution time and vice versa for lower values. The research implies that MLP-1 (36.08 in seconds) technique is the fastest, requiring 15 epochs. More hidden layers imply higher execution time. This research implies that the MLP-1 technique yields the lowest number of hidden layers. Thereby giving the highest execution time (349.1 in seconds) for RNN-3. Lastly, RNN and MLP have the highest accuracy of 80% (for all variants). The inferences are that these approaches could be used for disease management in terms of the use of proper pesticides in the right quantity using proper spraying techniques. Based on these conclusions, an agricultural scientist can propose a set of right regulations and guidelines

Analysis of Non-motorised Transport and Public Transport Facilities Using Bicycle Compatibility Index and Stop Coverage Ratio, in Vijayanagar, Bengaluru

Transportation development is one of the parameters indicating the development of any country. Due to improper maintenance of infrastructure, non-eco-friendly designs and practices etc. have led to congestion, accidents and environmental depletion. These problems can be addressed by developing a structured transportation system integrated with sustainability and safety. The aim of this study is to analyze the existing facilities in the study area and to suggest integration of non-motorised transport and public transport through questionnaire survey. The bicycle compatibility index is calculated to find the bicycle level of service of the study area. The bus stop coverage analysis was done by calculating the ideal stop accessibility index, actual stop accessibility index and stop coverage ratio index. From bicycle compatibility index, it can be concluded that the compatibity of the surveyed region was moderately high for bicyclists. Based on bus-stop coverage analysis, Kalyan nagar bus stop is said to have good accessibility whereas ITI layout has poor accessibility

Measurement and modeling of solitary wave induced bed shear stress over a rough bed

Bed shear stresses generated by solitary waves were measured using a shear cell apparatus over a rough bed in laminar and transitional flow regimes (~7600 < Re < ~60200). Modeling of bed shear stress was carried out using analytical models employing convolution integration methods forced with the free stream velocity and three eddy viscosity models. The measured wave height to water depth (h/d) ratio varied between 0.13 and 0.65; maximum near- bed velocity varied between 0.16 and 0.47 m/s and the maximum total shear stress (sum of form drag and bed shear) varied between 0.565 and 3.29 Pa. Wave friction factors estimated from the bed shear stresses at the maximum bed shear stress using both maximum and instantaneous velocities showed that there is an increase in friction factors estimated using instantaneous velocities, for non-breaking waves. Maximum positive total stress was approximately 2.2 times larger than maximum negative total stress for non-breaking waves. Modeled and measured positive total stresses are well correlated using the convolution model with an eddy viscosity model analogous to steady flow conditions (nu_t=0.45u* z1; where nu_t is eddy viscosity, u* is shear velocity and z1 is the elevation parameter related to relative roughness). The bed shear stress leads the free stream fluid velocity by approximately 30° for non-breaking waves and by 48° for breaking waves, which is under-predicted by 27% by the convolution model with above mentioned eddy viscosity model

Measurements and modeling of direct bed shear stress under solitary waves

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

ECT associated musical hallucinations in an elderly patient: a case report

Electro Convulsive Therapy (ECT) is a medical treatment for severe mental illness in which small, carefully controlled electricity is applied to the brain. This electric stimulation is done in conjunction with anesthesia and muscle relaxant medications to produce a mild generalized seizure. This is used to treat a variety of psychiatric disorders. This is most effective in the treatment of severe depression providing a rapid relief. We report and discuss an unusual presentation of a ninety three year old lady with a diagnosis of Major Depressive Disorder, Recurrent, Severe with Psychotic features (296.34) who experienced musical hallucinations whilst she was treated on ECT. Clinically there was an inverse relationship between the biological symptoms of depression and musical hallucination during the ECT management. Though similar reports have never been reported earlier, we noticed a good association between the initiation of ECT and musical hallucination in our patient. The patient stopped experiencing musical hallucinations and improved of her biological symptoms of depression completely after the full course of ECT

Mechanically induced fcc phase formation in nanocrystalline hafnium

A face-centered-cubic (fcc) phase was obtained in high-purity hafnium (Hf) metal powders subjected to mechanical milling in a high-energy SPEX shaker mill. X-ray diffraction and electron microscopy techniques were employed to evaluate the structural changes in the milled powder as a function of milling time. The effects of mechanical milling included a reduction in grain size, an increase in lattice strain, and formation of an fcc phase instead of an equilibrium hexagonal-close-packed (hcp) phase. During milling, the grain size of Hf decreased to below about 7 nm. Additionally, there was approximately 6% increase in atomic volume during the formation of the fcc phase. Chemical analysis of the milled powder indicated the presence of significant amounts of interstitial impurities. Even though any or all of the above factors could contribute to the formation of the fcc phase in the milled powder, it appears that the high level of interstitial impurities is at least partially responsible for the formation of the fcc phase