ABSTRACT Internet of Things has changed everyone's life. It often changes the way people interact with their environment and objects. IoT devices use wearable devices that are used to monitor the activities of animals. The Internet of Things for Animal Health (IoTAH) uses biosensors and software to monitor and manage animal health information. This work concerns the development of a mobile application coupled with a hardware device (called CAHM) attached to an animal’s collar. Companion Animal Health Monitoring App. Equipped with a smart collar, the system feature’s location tracking, heart rate, body temperature measurement, disease detection and isolation, detection of reproductive cycles, as well as monitoring physiological wellbeing of the animal. With further advancements in technology, data analysis algorithms and IoT systems, using these technologies we can improve our understanding of animal health, improve prevention and support healthy, happy animals.
ABSTRACT Bank churn, or the phenomenon of customers leaving their bank for another, is a major concern for financial institutions. The ability to predict churn can help banks to retain customers, reduce costs, and increase profitability. In this paper, we propose a machine learning approach to predict bank churn using a dataset of customer transactions and demographic information. We compare the performance of different machine learning models, including logistic regression, decision tree, random forest, and neural network, using evaluation metrics such as accuracy, precision, recall, and F1-score. Our results show that the random forest model outperforms other models with an accuracy of 87.5% and an F1-score of 0.87. Furthermore, we conduct feature selection and engineering to identify the most important features contributing to bank churn. Our findings suggest that factors such as customer age, account balance, and number of transactions are significant predictors of churn. Our research has important implications for banks to improve customer retention and reduce churn by leveraging machine learning techniques.
ABSTRACT The first function of obstacle avoidance robots is to detect the presence of obstacles. When you power on the system with the help of the ON/OFF switch, the Arduino microcontroller will read the data. When the ultrasonic sensor detects the presence of an obstacle in the process of moving forward, the robot will move backward. If the robot does not sense any obstacle, that is, if the distance between an obstacle and it is wide, it will then move forward again until it senses an obstacle before it stops. Two LEDs are used in the research, one indicating the robot is moving forward and the other showing if the robot is moving backward. The third LED shows if the battery is fully charged. C programming is used for Arduino board applications to develop the program for the whole system's operation. There is also a power source unit that is used to charge the batteries used in the system.
ABSTRACT Due to their many purposes and functions for mankind, oils from fruits and seeds are given a lot of importance today. As a result, there is a higher demand for cashew, avocado, and bush pear oils. The Soxhlet extractor and petroleum ether as the extracting solvent are used in this work to extract and characterize the oils from the seeds of Cashew, Avocado, and Bush pear. Physical and chemical studies of the extracted oil were performed alongside with the identification of any trace metals. The findings assessed the yield, color, relative density, refractive index, acid value, peroxide value, iodine value, and saponification value free fatty acid for Bush pear, Avocado pear, and Cashew seeds. The yield (%) of the oil produced from the various seeds of cashew, avocado, and bush pear oils is of specific significance. This finding reveals highest oil yield of 38.2% in Avocado pear oil, compared to 34.4% and 27.4% oil yield in Bush pear seed, and Cashew seed, respectively. In addition to being edible, Bush pear and Cashew oil's low acid value of 6.4 mg KOH/g and 7.7 mg KOH/g, respectively compared to Avocado oil's low acid value of 14.6 mg KOH/g avocado suggests that Bush pear and Cashew oils finds good applications in the paint industry. The study also showed that potassium had the highest concentration of any metal, with 45.8 mg/100g in avocado oil, compared 24.9 mg/100g and 1.68 mg/100g for Cashew seed oil and Bush pear, respectively. Zinc had the lowest concentration, with a value of 0.38 mg/100g observed in Cashew compared with 1.32 mg/100g and 2.06 mg/100g for Bush pear and Avocado, respectively. Hence, this reveals that Oils extracted from Cashew finds good applications in the production of hydraulic oil with exceptional lubrication properties. The FTIR study shows that functional groups including alkanes, alkenes, carbonyls, etc. are present in the seed oils of Bush pear, Avocado pear, and Cashew seeds.
ABSTRACT The Lorentz transformation (LT) is one of the most revered set of relationships in modern physics. Overlooked in this broad assessment are a number of clear inconsistencies in its predictions, however, as will be discussed herein. For example, it makes three predictions which are not consistent with one another: Lorentz-FitzGerald length contraction (FLC), time dilation (TD) and light-speed equality for observers in relative motion to one another. Einstein’s light-speed postulate (LSP) is shown to be unviable by considering a case in which a light source passes by a stationary observer at the same time that it emits a light pulse in the same direction. It is found that, in contradiction to the LSP, that the classical velocity (Galilean) transformation (GVT) is applicable when two observers in relative motion deduce the speed of a light wave from their different perspectives. The LT also stands in violation of the Law of Causality because it fails to recognize that inertial clocks can never change their rate spontaneously; thus its two clocks must always measure elapsed times in the same ratio (Q), contrary to the LT prediction of space-time mixing. The Newton-Voigt transformation (NVT) is consistent with the Law of Causality because it assumes space and time do not mix. It is nonetheless also consistent with the relativistic velocity transformation (RVT) and also with Einstein’s mass-energy equivalence relation E=mc2. The ratio Q of clock rates for two inertial rest frames S and S’ is required input for the NVT. Experimental data obey the Universal Time-dilation Law (UTDL) which states that the measured time Δt obtained by an inertial clock for a given event is inversely proportional to γ(v)= (1-v2c-2)-0.5, where v is the speed of the clock relative to a specific rest frame referred to as the objective rest frame ORS. The Uniform Scaling method employs Q as a conversion factor between the units of time in the two rest frames. It is found that the conversion factors
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