Nowadays, the huge amount of patient’s data significantly increases with respect to the time in repositories and data mining is increasingly used as an emerging research area in medical fields for extracting useful and previously unknown insights/patterns from the repository data. These unknown patterns/hidden insights can help in discovering new knowledge hidden in these data repositories. From the observation, different ARV regimens were ordered for different patients. However, combination of these drugs causes different side effects on the patients. It has been observed that there was a lack of predictive studies and designed models available in hospitals specifically ART Centers that accurately determine or classify the patient’s ARV regimen to TDF + 3TC + EFV, TDF + 3TC + NVP, AZT + 3TC + ATV/R, AZT + 3TC + LPV/R, TDF + 3TC + LVP/R, TDF + 3TC + ATV/R, 8888, and ABC + 3TC + LPV/R. In order to solve these kinds of problems, we built an accurate classifier system or model using parameters like Patient Age, Patient Encounter Day, Patient Encounter Month, Patient Encounter Year, Patient Weight, Patient CD4 Count Adult, Patient TB Screen, Patient Following WHO Stage, Patient CD4 Percent Child, Patient Regimen Specify, Patient Regimen, and so on. The general objective of this research was predictive modeling for the patient’s ARV regimen class through data mining techniques so as to improve them. The study used the CRIPS-DM methodology to find and interpret patterns in repositories. A decision tree (J48 and Random Forest) algorithm was used for classification. Using all tested classifiers, the investigation of the study shows that the total accuracy was more than 60%. On the other hand, among different classifications, class H (ABC + 3TC + LPV/R) has shown the worst prediction. But it was revealed that the J48 classifier relatively produces higher classification accuracy for the D (AZT-3TC-NVP) regimen. Here, classification depended on the selected parameters, which revealed that prediction accuracy value differed among all classifiers and the selected attributes. Finally, the study concluded that data mining can be used as a significant technique to discover patient regimen based on salient affecting factors with 96.1% precision achieved. Ensemble learning resolves the categorizing models of greater anticipating performance with different learning algorithms. This model aligned with sentimental investigation to magnify the appearances of the dataset either from the social media or from primary data collection. The empirical investigation with different parameters shows the detailed improvement of their learning methods.

Cotton is one of the economically significant agricultural products in Ethiopia, but it is exposed to different constraints in the leaf area. Mostly, these constraints are identified as diseases and pests that are hard to detect with bare eyes. This study focused to develop a model to boost the detection of cotton leaf disease and pests using the deep learning technique, CNN. To do so, the researchers have used common cotton leaf disease and pests such as bacterial blight, spider mite, and leaf miner. K-fold cross-validation strategy was worn to dataset splitting and boosted generalization of the CNN model. For this research, nearly 2400 specimens (600 images in each class) were accessed for training purposes. This developed model is implemented using python version 3.7.3 and the model is equipped on the deep learning package called Keras, TensorFlow backed, and Jupyter which are used as the developmental environment. This model achieved an accuracy of 96.4% for identifying classes of leaf disease and pests in cotton plants. This revealed the feasibility of its usage in real-time applications and the potential need for IT-based solutions to support traditional or manual disease and pest’s identification.

<p>In this paper, three different impedance source inverters: Z-source inverter, EZ- source inverter, TZ-Source for wind energy conversion system (WECS) were investigated. Total output power and THD of each of these systems are calculated. The proposed system can boost the output voltage effectively when the low output voltage of the generator is available at low wind speed. This system has higher performance, less components, increased efficiency and reduced cost. These features make the proposed TZSI based system suitable for the wind conversion systems. MATLAB simulink model for wind generator system is developed and simulation studies are successfully performed. The simulation is done using MATLAB and the simulation results are presented. This comparison shows that the TZ-source inverter is very promising for wind energy conversion system.</p>

The usefulness of wireless sensor networks has fascinated the world’s attention. Usage of low-power microcontrollers and wireless sensors to handle real-world problems such as environmental, medicinal, and structural monitoring has exploded. Wireless sensor nodes are extremely tiny and are designed for low-duty applications such as recording physical characteristics. Wireless sensor network operations such as sensing, calculations, and communication take extensively more energy than these low-powered sensor nodes. They are used both in attainable and inaccessible areas and are usually powered by batteries. Since the sensor is powered by batteries, replacing and charging the battery after its depletion are challenging. Manual battery replacement is hampered by geographical restrictions, which results in significant reduction of wireless sensor network performance and longevity. As a result, this study addresses the energy-constrained wireless sensor networks by creating a technological model to a heterogeneous clustered wireless sensor network in an outdoor application using solar-enabled energy harvesting photovoltaic cells. Due to lack of energy, this effort was employed to overcome the challenges of relatively restricted processing performance and limited radio frequency transmission bandwidth. The program was created to efficiently utilize the energy produced from solar panels and charge the batteries in a variety of ways. The algorithm also controls the discharging process. According to the results of extensive investigation and experimentation, the sensor is continuously operated even when there is no solar light. The batteries are charged on bright day and discharged at night and on overcast days. The algorithm is used to govern the energy supplement to rechargeable lithium-ion polymer batteries as well as a load (sensor). In the worst scenario (no solar light/cloudy, and sensors reporting data every 30 minutes), the present cluster head sensor with 6 ordinary nodes in the cluster lasts just 16.6667 days. But the life duration of the newly designed model algorithm has been raised to 54.16667 days. The normal sensor node’s life duration has also been enhanced from 50 to 91.66667 days.

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