Along with the growth of the Internet, social media usage has drastically expanded. As people share their opinions and ideas more frequently on the Internet and through various social media platforms, there has been a notable rise in the number of consumer phrases that contain sentiment data. According to reports, cyberbullying frequently leads to severe emotional and physical suffering, especially in women and young children. In certain instances, it has even been reported that sufferers attempt suicide. The bully may occasionally attempt to destroy any proof they believe to be on their side. Even if the victim gets the evidence, it will still be a long time before they get justice at that point. This work used OCR, NLP, and machine learning to detect cyberbullying in photos in order to design and execute a practical method to recognize cyberbullying from images. Eight classifier techniques are used to compare the accuracy of these algorithms against the BoW Model and the TF-IDF, two key features. These classifiers are used to understand and recognize bullying behaviors. Based on testing the suggested method on the cyberbullying dataset, it was shown that linear SVC after OCR and logistic regression perform better and achieve the best accuracy of 96 percent. This study aid in providing a good outline that shapes the methods for detecting online bullying from a screenshot with design and implementation details.

Alzheimer’s illness is an ailment of mind which results in mental confusion, forgetfulness and many other mental problems. It effects physical health of a person too. When treating a patient with Alzheimer's disease, a proper diagnosis is crucial, especially into earlier phases of condition as when patients are informed of the risk of the disease, they can take preventative steps before irreparable brain damage occurs. The majority of machine detection techniques are constrained by congenital (present at birth) data, however numerous recent studies have used computers for Alzheimer's disease diagnosis. The first stages of Alzheimer's disease can be diagnosed, but illness itself cannot be predicted since prediction is only helpful before it really manifests. Alzheimer’s has high risk symptoms that effects both physical and mental health of a patient. Risks include confusion, concentration difficulties and much more, so with such symptoms it becomes important to detect this disease at its early stages. Significance of detecting this disease is the patient gets a better chance of treatment and medication. Hence our research helps to detect the disease at its early stages. Particularly when used with brain MRI scans, deep learning has emerged as a popular tool for the early identification of AD. Here we are using a 12- layer CNN that has the layers four convolutional, two pooling, two flatten, one dense and three activation functions. As CNN is well-known for pattern detection and image processing, here, accuracy of our model is 97.80%.

Skin cancer is among common and rapidly increasing human malignancies, which can be diagnosed visually. The diagnosis begins with preliminary medical screening and by dermoscopic examination, histopathological examination, and proceeding to the biopsy. This screening and diagnosis can be automated using machine learning tools and techniques. Artificial neural networks are helping a lot in medical diagnosis applications. In this research, skin images are classified into 7 different classes of skin cancer using deep learning methodology, then analyzed the results w.r.t to their respective precision, recall, support, and accuracy to find its practical applicability. This model is efficient in comparison to the detection of skin cancer with human eyes. Human eyes detection can be 79% accurate at most. Thus, having a scientific method of diagnosis can help the doctors and practitioners to accurately identify the cancer and its type. The model provides 80% accuracy on average for all 7 types of skin diseases, thus being more reliable than human eye examination. It will help the doctors to diagnose the skin diseases more confidently. The model has only 2 misclassified predictions for Basal cell carcinoma and Vascular lesions. However, Actinic keratosis diagnosis is most accurately predicted.

Feature Selection is an important phase in classification models. Feature Selection is an effective task used to decrease the dimensionality and eliminate redundant and unrelated features. In this paper, three novel algorithms for feature selection problem are proposed. The first one is a filter method, the second one is a wrapper method, and the last one is a hybrid filter method. Both the proposed algorithms use the crowding distance used in the multiobjective optimization as a new metric to assess the importance of the features. The idea behind the use of the crowding distance is that the less crowded features have great impacts on the target attribute (class), and the crowded features have generally the same impact on the class attribute. To enhance the crowded distance, a combination with other metrics will give good results. In this work, the hybrid method combines between the crowding distance and Pearson correlation coefficient to well order the importance of features. Experiments on well-known benchmark datasets including large microarray datasets have shown the effectiveness and the robustness of the proposed algorithms.

Healthcare is a fundamental human right. Vulnerable populations in healthcare refer to those who are at greater risk of suffering from health hazards due to various socio-economic factors, geographical barriers, and medical conditions. Mapping of this vulnerable population is a vital part of healthcare planning for any region. Very few such research regarding the distribution of healthcare service providers was carried out in the Nepali context previously. Thus, the results of vulnerability mapping can help with meaningful interventions for healthcare demands. This study focused on combining geo-analytics, unsupervised machine learning algorithms, and entropy methods for performing vulnerability mapping. K-means++ clustering algorithm was applied to household data of Ratnanagar municipality for the purpose of creating multiple clusters of households. An open-source routing machine was used to compute the distance to the nearest health service provider from each household in Ratnanagar municipality. The entropy method was used to evaluate the vulnerability measure of each cluster. Later, based on the population of different clusters in each ward and their respective vulnerability measures, each ward’s vulnerability measure was quantified. It can be observed that wards that are farther away from the east-west highway have higher vulnerability indices. This study found that machine learning algorithms can be effectively used in combination with the weighting method for vulnerability mapping. Using an unsupervised machine learning algorithm made sure that dimensions of vulnerability are visible.