Every year thousands of students get admitted into different universities in Bangladesh. Among them, a large number of students complete their graduation with low scoring results which affect their careers. By predicting their grades before the final examination, they can take essential measures to ameliorate their grades. This article has proposed different machine learning approaches for predicting the grade of a student in a course, in the context of the private universities of Bangladesh. Using different features that affect the result of a student, seven different classifiers have been trained, namely: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Logistic Regression, Decision Tree, AdaBoost, Multilayer Perceptron (MLP), and Extra Tree Classifier for classifying the students' final grades into four quality classes: Excellent, Good, Poor, and Fail. Afterwards, the outputs of the base classifiers have been aggregated using the weighted voting approach to attain better results. And here this study has achieved an accuracy of 81.73%, where the weighted voting classifier outperforms the base classifiers. Kabra and Bichkar [3] collected data from the entry form, filled by the students in an engineering college during the time of admission. Using J48 algorithm, they predicted the final grades of the first year students'. When they classified the results into three categories they gained an accuracy of 60.46% and in the case of classifying the results into two categories they gained an accuracy of 69.94%. Kapur et al. [5] used various classification algorithms to classify the performance of the students into three categories: high, medium, and low. Their dataset included 480 entries with 16 attributes. Among these classifiers Random Forest showed the highest accuracy of 76.67%.

Deep learning's rapid gains in automation are making it more popular in a variety of complex jobs. The self-driving object is an emerging technology that has the potential to transform the entire planet. The steering control of an automated item is critical to ensuring a safe and secure voyage. Consequently, in this study, we developed a methodology for predicting the steering angle only by looking at the front images of a vehicle. In addition, we used an Internet of Things-based system for collecting front images and steering angles. A Raspberry Pi (RP) camera is used in conjunction with a Raspberry Pi (RP) processing unit to capture images from vehicles, and the RP processing unit is used to collect the angles associated with each image. Apart from that, we've made use of deep learningbased algorithms such as VGG16, ResNet-152, DenseNet-201, and Nvidia's models, all of which were trained using labeled training data. Our models are End-to-End CNN models, which do not require extracting elements from data such as roads, lanes, or other objects before predicting steering angle. As a result of our comparative investigation, we can conclude that the Nvidia model's performance was satisfactory, with a Mean Squared Error (MSE) value of 0.3521. But the Nvidia model outperforms the other pre-trained models, even though other models work well.

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Skin lesions or malignancies have been a source of worry for many individuals in recent years. The underline reason behind it mainly the diet and environmental pollution. Yet many individuals are unaware of the issue and, more importantly, many people do not want to visit a hospital for diagnostic or therapeutic purposes. So, we have come up with a pipeline to diagnose skin lesions at home. Firstly, we proposed a IoT base data collection device which is accessible by patient to capture skin lesions image. This IoT device will encrypt and send the collected image towards a cloud storage; then it will decrypt the image send to the computer assisted diagnosis system. In CAD, we have implemented ensemble classifier. Ensemble classifier created depending on four deep learning classifiers namely VGG16, DenseNet201, Inception V3 and Efficient B7; whereas encryption and decryption performed in order to secure a patient data from unauthorized access. For skin lesions classification, we have used "HAM10000" dataset where 7 kind of skin lesions data included; Although DenseNet201 performed well, the ensemble model provides the highest accuracy with 87.22\% as well as its test loss/error is lower than others with 0.4131.

Tujuan penelitian ini untuk mengetahui dan menganalisis kondisi dan strategi strategi peningkatan mutu pendidikan melalui standarisasi tenaga pendidik serta kendala dan solusinya di SMP Negeri 02 Rangsang Pesisir Kabupaten Kepulauan Meranti. Jenis penelitian ini adalah penelitian deskriptif dan pendekatan penelitian kualitatif. Teknik pengumpulan data dengan observasi, wawancara, dan dokumentasi. Informan dalam penelitian ini yaitu Guru SMP Negeri 02 Rangsang Pesisir Kabupaten Kepulauan Meranti yang berjumlah 5 orang. Analisis data dalam penelitian kualitatif antara lain Reduksi Data, Penyajian Data dan Conclusing Drawing/verification. Hasil penelitian menunjukkan bahwa mutu pendidikan yang meliputi kepemimpinan kepala sekolah, guru, siswa dan kurikulum yang diterapkan di sekolah sudah dilaksanakan dengan baik, Pembuatan program kerja kepala sekolah sudah didasarkan pada visi dan misi sekolah sehingga guru dapat meningkatkan pemahaman dan penguasaan kerja guru. Peningkatan kompetensi yang dimiliki guru dapat dilakukan dengan kegiatan pelatihan dan in house training serta MGMP (Musyawarah Guru Mata Pelajaran). Kurikulum harus disesuaikan dengan peraturan yang berlaku. Pembelajaran terkendala pandemi Covid 19, sehingga guru tidak dapat mengajar dengan baik, upaya yang dilakukan dengan melaksanakan membelajaran daring, membekali kompetensi guru dengan mengikutsertakan guru dalam seminar online atau pelatihan online pengajaran daring.