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Mobile banking becomes an interesting technique within the modern bank establishments. It facilitates the transactions and day lifestyle of customers. It minimizes the impact of location and time for doing bank activities and communicate with bank servers. However, the process is exposed to risks from attackers and hackers, especially, user behaviors that open and create much vulnerability in this system. This work presents a new taxonomy for mobile banking attackers and threats. Through this taxonomy, this work will identify the important user vulnerabilities that attackers may misuse for penetrating systems and steal privacy and sensitive data. The main contribution of this work is to provide important suggestions for mobile banking users so that they can take them as a precaution for protecting their privacy and financial aspects. The work concluded that there are many user behaviors of mobile banking which lead to threats in the systems. The work presents many suggestions for users so that their systems can be protected from malicious activities and malwares. Also, many future aspects have been presented.

In the past fifteen years, the grow of using Bag of Words (BoW) method in the field of computer vision is visibly observed. In addition,-for the text classification and texture recognition, it can also be used in classification of images, videos, robot localization, etc. It is one of the most common methods for the categorization of text and objects. In text classification, the BoW method records the number of occurrences of each bag that is created for each instance type or word disregarding the order of the words or the grammar. And in visual scene classification it is based on clusters of local descriptors which are taken from the images disregarding the order of the clusters. The key idea is to generate a histogram for the words in the documents or the features in the images to represent the specified document or image. The BoW method is computationally and even conceptually is simpler than many other classification methods. For that reason, BoW based systems could record new and higher performance scores on common used benchmarks of text and image classification algorithms. This paper presents an overview of BoW, importance of BoW, how does it work, applications and challenges of using BoW. This study is useful in terms of introducing the BoW method to the new researchers and providing a good background with associated related works to the researchers that are working on the model.

This research introduces an approach to diagnose diseases from medical check-up test reports. The proposed approach is produced from Optical Character Recognition (OCR) technology to convert the hard copy test reports into editable textual data, Bag of Words (BoW) model as feature selection algorithm, Naive Bayes as classification algorithm, and AdaBoost technique to enhance the performance of the Naive Bayes classifier. The performance of the proposed approach is very good in terms of validity and can be used in diagnosing of diseases from medical check-up test reports. The proposed approach is trained on dedicated trained partitions of multiple medical datasets, and then tested on the testing sets partitioned from the original datasets. The proposed algorithm is compared with the Support Vector Machine (SVM), Naive Bayes (NB), Decision Table (DT), and k-Nearest Neighbors (k-NN) classifiers, in which all the algorithms are tested on the same datasets. The proposed algorithm showed higher accuracy than the other four classifiers. So, the proposed approach which is the combination of BoW with AdaBoost technique is used to predict the name of the diseases from the medical check-up test reports. After that, an image as an example of the disease will be presented as well with the name of the disease to the physician and the patient. The image presentation is very important for the patients, because they may not familiar with the medical terms and disease names. Finally, the proposed approach can be used in the medical area because of its good performance and showing validated results after it is tested.

One of the main areas in computer vision is automatic face recognition which deals with detecting human face autonomously. Developments and the progress in the field of face recognition have shown that many face recognition systems and applications the automated methods outperform humans. The conventional Scale-Invariant Feature Transform (SIFT) is used in face recognition where they provide high performances. However, this performance can be improved further by transforming the input into different domains before applying SIFT algorithm. Hence, we apply Discrete Wavelet Transform (DWT) or Gabor Wavelet Transform (GWT) at the input face images, which provides denser and extra information to be used by the conventional SIFT algorithm. Matching scores of SIFT from each subimage is fused before making final decision. Simulations show that the proposed approaches based on wavelet transforms using SIFT provides very high performance compared to the conventional algorithm.