Deep learning-based anomaly detection in images has recently been considered a popular research area with numerous applications worldwide. The main aim of anomaly detection (i.e., Outlier detection), is to identify data instances that deviate considerably from the majority of data instances. This paper offers a comprehensive analysis of previous works that have been proposed in the area of anomaly detection in images through deep learning generally and in the medical field specifically. Twenty studies were reviewed, and the literature selection methodology was defined based on four phases: keyword filter, publish filter, year filter, and abstract filter. In this review, we highlight the differences among the studies included by considering the following factors: methodology, dataset, prepro-cessing, results and limitations. Besides, we illustrate the various challenges and potential future directions relevant to anomaly detection in images

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Deep Learning based Anomaly Detection in Images: Insights, Challenges and Recommendations

Advances in spam detection for email spam, web spam, social network spam, and review spam: ML-based and nature-inspired-based techniques

COVID-19 has many symptoms and one of the serious symptoms are heart problems and the increase in body temperature. Checking the heart rate and body temperature can still be useful to be included in our daily live to prevent further spread of the virus. We proposed a solution to let the user do a COVID-19 self-detection by using Magic Mirror with IoT -based technology. This Magic Mirror uses two sensors (heart rate and temperature sensor) to measure user's heart rate and body temperature. If the user is suspected of having COVID-19, an alert will be displayed on the Magic Mirror or smartphone to let the user take further necessary action.

COVID-19 Self-Detection Magic Mirror With IoT-based Heart Rate and Temperature Sensors

The development of information and communication technology has created many positive outcomes, including convenience for people; however, cases of unsolicited communication, such as spam, also occur frequently. Spam is the indiscriminate transmission of unwanted information by anonymous users, called spammers. Spam content is indiscriminately transmitted to users in various forms, such as SMS, e-mail, and social network service posts, causing negative experiences for users of the service, while also creating costs, such as unnecessarily large amounts of network traffic. In addition, spam content includes phishing, hype or false advertising, and illegal content. Recently, spammers have also used images that contain stimulating content to effectively attract users’ curiosity and attention. Image spam contains more complex information than text, making it more difficult to analyze and to generalize its properties compared to text. Therefore, existing text-based spam detectors are vulnerable to spam image attacks, resulting in a decline in service quality. In this paper, a “hybrid features by combining visual and text information to improve spam filtering performance” method is proposed to reduce the occurrence of misclassification. The proposed method employs three sub-models to extract features from spam images and a classifier model to output the results using the features. Each sub-model extracts topic-, word-, and image-embedding-based features from spam images. In addition, the sub-models use optical character recognition, latent Dirichlet allocation, and word2Vec techniques to extract features from images. To evaluate spam image classification performance, the spam classifiers were trained using the extracted features and the results were measured using a confusion matrix. Our model achieved an accuracy of 0.9814 and a macro-F1 score of 0.9813. In addition, the application of OCR evasion techniques resulted in a decrease in recognition performance. Using the proposed model, a mean macro-F1 score of 0.9607 was obtained.

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Hybrid Features by Combining Visual and Text Information to Improve Spam Filtering Performance

Efficient detection of hacker community based on twitter data using complex networks and machine learning algorithm

Internet of Things (IoT) allows devices to communicate with each other in different and important places at the same time. Smart things are developed in ways that interact with each other, such as smart doors and smart homes. One of the most important IoT applications is the smart mirror. It is a mirror that acts as a reflective surface and as an interactive screen at the same time. Smart mirrors can be implemented for different purposes such as a simulator for medical students and an assistant in the fitting rooms. This paper presents a review upon applications of smart mirrors.

Internet of Things Based Smart Mirrors: A Literature Review

Twitter allows users to send short text-based messages with up to 280 characters which is called “tweets”. The reputation of Twitter attracts the spammers to spread malevolent programming through URLs attached in tweets. Twitter spam has become a critical problem. Spam refers to a variety of prohibited behaviours that violate the Twitter rules. In this paper, different machine and deep learning algorithms are used to detect if the tweet is spammer or not. The performance of six machine learning algorithms, namely Random Forest (RF), Naive Bayes (NB), Bayesian Network (BN), Support Vector Machine (SVM), K-Nearest Neighbour (KNN), and Multi-Layer Perceptron (MLP) and one deep learning algorithm which is Recurrent Neural Network (RNN) are evaluated. Different test options are used, namely cross validation and percentage split tests. Results show that RF predicts the best result with lowest error rate and highest classification accuracy rate with different test options comparing to all algorithms.

Machine and Deep Learning Algorithms for Twitter Spam Detection

Arabic Sign Language (ArSL) is a language of communication between deaf & dumb people in Arab countries. This study focuses on establishing a real-time speech conversion service into ArSL by 3D animation videos, to facilitate the learning process in virtual educational platforms. This study to help the deaf & dumb students in distant learning under the Covid-19 pandemic. This study applied on the TEAMS platform. A database was created containing more than 550 ArSL videos from the Al-tarjuman application and connected it with the TEAMS platform. A Python was used to link the System units. After studying some related works that were concerned with the language of the deaf & dumb, we found that some studies do not support real-time translation, has lack Arabic sign language translation, has limited DB, resulting in a low level of system performance, and use of high-cost devices such as gesture motion sensors “Kinect and Leap Motion”. As future work, we will increase the words in the DB to improve the results. We advise researchers to contribute by working on merging the videos retrieved from the DB to make an integrated serial video. Also, adding a syntactic analysis stage to extract the sentence structure.

Amjad Alfahhad

Papers

Internet of Things Based Smart Mirrors: A Literature Review

Machine and Deep Learning Algorithms for Twitter Spam Detection

Improving the Virtual Educational Platforms for the Deaf and Dumb under the Covid-19 Pandemic Circumstances