PELATIHAN TEKNIK DASAR DAN SOSIALISASI PERATURAN PERMAINAN OLAHRAGA KURASH PADA PENGURUS BELA DIRI KURASH KOTA MAKASSAR

Tujuan PKM ini adalah untuk membantu mitra yakni Pengurus Cabang Kurash Kota Makassar mengatasi   permasalahan   yang   ada.Kegiatan tim sosialisasi   mengadakan   sosialisasi   dan  dilanjutkan  dengan kegiatan  coaching  clinic. Adapun  permasalahan  mitra  dalam kegiatan  PKM  adalah  kurangnya Pemahaman  tentang  teknik  dasar  dan  peraturan  dalam permainan  olahraga  kurash karena  banyak  pengurus  yang  baru  mengenal  olahraga  kurash ini. Berdasarkan  permasalahan  mitra maka  solusi  yang  ditawarkan  adalah (1) Memberikan pengetahuan dan  penjelasan  tentang peraturan permainan dan teknik dasar olahraga  kurash secara sistematis bagi Pengurus  Cabang  Kurash kota Makassar, metode yang digunakan:  ceramah dan tanya jawab.(2) Memperkenalkan aturan permainan dan teknik dasar olahraga Kurash bagi Pengurus  Cabang  Kurash Kota Makassar, metode yang digunakan demonstrasi atau praktek.(3) Menyediakan  alat  dan  perlengkapan  untuk melakukan praktek  kepada Pengurus Cabang Kurash Kota Makassar untuk melakukan gerakan demontrasi. Pengurus Cabang Kurash Kota Makassar nantinya diharapkan  bisa  melaksanakan  pembinaan  untuk  mencari  atlit-atlit  Kurash berbakat  yang  nanti agar bisa bersaing dengan Pengcab lain yang ada di Sulawesi Selata

Prostate cancer prediction using feedforward neural network trained with particle swarm optimizer

Prostate cancer has been one of the most commonly diagnosed cancers in men and one of the leading causes of death in the United States. Because of the complexity of the masses, radiologists are unable to diagnose prostate cancer properly. Many prostate cancer detection methods have been established in the recent past, but they have not effectively diagnosed cancer. It is worth noting that most current studies employ machine learning techniques, especially when creating prediction models from data. Despite its possible benefits compared to standard statistical analyses, these methods break down the problem statements into different parts and combine their results at the final stage. This makes complexity, and the prediction accuracy not consistently high. In this paper, the Feedforward Neural Networks (FNNs) is trained by using Particle Swarm Optimizer (PSO) and the FNNPSO framework is applied to the prediction of prostate cancer. PSO is one of the novel metaheuristics and frequently used for solving several complex problems. The experimental results are evaluated using the mean, best, worst, and standard deviation (Std.) values of the fitness function and compared with other learning algorithms for FNNs, including the Salp Swarm Algorithm (SSA) and Sine Cosine Algorithm (SCA). The experimental finding shows that the FNNPSO framework provides better results than the FNNSSA and FNNSCA in FNN training. Moreover, FNN trained with PSO is also shown to be better accurate than other trained methods to predict prostate cancer

Automatic brain tumor detection using feature selection and machine learning from MRI Images

A brain tumor is a group of defective cells in the brain. It happens when a cell in the brain develops a dysfunctional structure. Nowadays it becoming a crucial factor of death for a large number of people. Among all the varieties of tumors, the seriousness of a brain tumor is high. Therefore, instant detection and proper care to be done to save a life from brain tumors. Microscopic examination can separate the tumor cells from healthy cells. They are typically less well separated than normal cells. In modern imaging technology, the detection and classification of brain tumors is a primary concern. For a clinical supervisor or radiologist, it is time-consuming and frustrating work. The accuracy of recognition and classification of tumors executed by radiologists or clinical experts is depended on their experience only. Therefore, accurate identification and classification of brain tumors can be determined by image processing techniques. This research suggests a machine learning module to detect brain tumors using magnetic resonance imaging (MRI) of brain tumors. The method consists of pre-processing of nearly raw raster data (NRRD) of the MRI images, feature extraction, feature selection, and the classification learner to evaluate and construct the final model. The classification learner is designed with a support vector machine (SVM) classifier. The classification method performs well with weighted sensitivity, specificity, precision, and accuracy of 98.81%, 98.88%, 98.82%, and 98.81% respectively. The findings may infer a remarkable step for detecting the presence of tumors in neuro-medicine diagnosis

Feature selection and prediction of heart disease using machine learning approaches

Heart Disease (HD) is the world's most serious illness that seriously impacts human life. The heart does not push blood to other areas of the body in cardiac disease. For the prevention and treatment of cardiac failure, accurate and timely diagnosis of heart disease is critical. The diagnosis of cardiac disease has been considered via conventional medical history. Non-invasive approaches like machine learning are effective and powerful to categorize healthy people and people with heart disease. In the proposed research, by using the cardiovascular disease dataset, we created a machine-learning model to predict cardiac disease. In this paper, it is capable of recognizing and classifying the heart disease patient from healthy people by using three standard machine learning algorithms: Random Forest (RF), Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). In addition, the Area Under Curve (AUC) value is calculated for each classification algorithms. In the proposed scheme, we also used the feature selection algorithm to reduce dimensions over a qualified heart disease dataset. After that, the whole structure for the classification of heart disease has been created. On complete features and reduced features, the performance of the proposed approach has been verified. The decrease in features affects the accuracy and time of execution of the classifiers. With the selected features, the highest classification accuracy is obtained for the KNN algorithm is about 93%, with a sensitivity is 0.9750 and specificity is 0.8529. Therefore, with the complete features, the classification accuracy is about 91%

Visual Warning System for Worker Safety on Roadside Workzones

Growing traffic on US roadways and heavy construction machinery on road construction sites pose a critical safety threat to construction workers. This report summarizes the design and development of a worker safety system using Dedicated Short Range Communication (DSRC) to specifically address the workers’ safety for the workers working around the heavy machinery. The proposed system has dual objectives. First objective is to improve workers’ safety by providing visual guidance to the operators of the construction vehicles about the workers’ presence in the vicinity. This visual guidance keeps the operators of the heavy machinery well informed about the whereabouts of the workers in close proximity while operating the heavy vehicle. The second objective of the proposed system is to improve the work-zone traffic mobility by dynamically posting suitable speed limits and other warning messages on the DSRC-equipped variable message signs (VMSs) depending on the workers’ presence in an active work-zone to appropriately warn the drivers of the passing-by vehicles. A prototype was developed and field tests were conducted to demonstrate and evaluate the performance of the proposed system. The evaluation test results show that the system can successfully show the presence of workers around a construction vehicle on an Android tablet with acceptable distance (1.5 – 2 m) and direction (15 – 20 degrees) accuracies. Furthermore, the test results show that a DSRC-equipped VMS can successfully post a suitable speed limit corresponding to the presence of workers in its vicinity

PROBLEMS FACED BY THE PARENTS FOR PROVIDING HIGHER EDUCATION TO THEIR CHILDREN IN RURAL AREAS.

The study was designed to investigate the problems faced by the parents residing in rural areas for providing graduate level education to their children. The aim of the study was to find out the problems faced by the parents for providing graduate level education to their children in rural areas. The objectives of this study were (1). To find out the social status of parent?s of graduate students. (2). To find out the difficulties faced by the parents for providing graduate level education to their male and female children. (3). To find out the socio-economic effect of the respondent. The descriptive survey method was used in the study. The target population was comprised of parents of student?s of 5 Degree Colleges (included both public and private). A sample of 80 parents was consisting in the study, 40 parents of students were selected from Government Degree College for Boys and 40 parents of students were selected from Government Degree College for Girls. Research questionnaire was developed on likert scale for gathering the data on the basis of parents of student?s perception on problems faced by the parents for providing graduate level education to their children in rural areas. The questionnaire was prepared and administered for parents from selected colleges. The collected data were analyzed through SPSS 21. The score of responses were tabulated in to the frequencies and represented in percentage. The results and findings of the study reveal that the majority of parent?s perception regarding the major problem is faced by the parents is high rate unemployment. There are lack of facilities and lack of awareness about education. It is also due to limited resources, shortage of colleges and non availability of transport. The parents are facing someother finical, social and economic problems for providing graduate level education to their children