Umair Javed

Bio: Umair Javed is an academic researcher from University of Engineering and Technology, Lahore. The author has contributed to research in topics: Web Ontology Language & Anomaly (physics). The author has an hindex of 1, co-authored 2 publications receiving 10 citations.

A Review of Content-Based and Context-Based Recommendation Systems

Abstract: In our work, we have presented two widely used recommendation systems. We have presented a context-aware recommender system to filter the items associated with user’s interests coupled with a context-based recommender system to prescribe those items. In this study, context-aware recommender systems perceive the user’s location, time, and company. The context-based recommender system retrieves patterns from World Wide Web-based on the user’s past interactions and provides future news recommendations. We have presented different techniques to support media recommendations for smartphones, to create a framework for context-aware, to filter E-learning content, and to deliver convenient news to the user. To achieve this goal, we have used content-based, collaborative filtering, a hybrid recommender system, and implemented a Web ontology language (OWL). We have also used the Resource Description Framework (RDF), JAVA, machine learning, semantic mapping rules, and natural ontology languages that suggest user items related to the search. In our work, we have used E-paper to provide users with the required news. After applying the semantic reasoning approach, we have concluded that by some means, this approach works similarly as a content-based recommender system since by taking the gain of a semantic approach, we can also recommend items according to the user’s interests. In a content-based recommender system, the system provides additional options or results that rely on the user’s ratings, appraisals, and interests.

A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives

Abstract: Anomaly detection is a significant problem that has been studied in a broader spectrum of research areas due to its diverse applications in different domains. Despite the usage of modern technologies and the advances in system monitoring and anomaly detection techniques, false-positive rates are still high. There exist many anomaly detection algorithms, among them few are domain-specific, and others are more generic techniques. Despite a significant amount of advance in this research area, there does not exist a single winning anomaly detector known to work well across different datasets. In this paper, we review the literature related to types of anomalies, data types of anomalies, data types of time-series, components of time-series data, classification of anomalies context, and classification methods of time-series anomalies detection. We presented a taxonomy to characterize the various aspects related to time-series anomaly detection. One of the key challenges in current anomaly detection techniques is to perform anomaly detection with regards to the type of activities or the context that a system is exposed. We hope that this investigation gives a more remarkable ability to understand the evolving methods of time-series anomaly detection and how computational methods can be applied in this domain in the future.

A Comparative Performance Analysis of Data Resampling Methods on Imbalance Medical Data

Abstract: Medical datasets are usually imbalanced, where negative cases severely outnumber positive cases. Therefore, it is essential to deal with this data skew problem when training machine learning algorithms. This study uses two representative lung cancer datasets, PLCO and NLST, with imbalance ratios (the proportion of samples in the majority class to those in the minority class) of 24.7 and 25.0, respectively, to predict lung cancer incidence. This research uses the performance of 23 class imbalance methods (resampling and hybrid systems) with three classical classifiers (logistic regression, random forest, and LinearSVC) to identify the best imbalance techniques suitable for medical datasets. Resampling includes ten under-sampling methods (RUS, etc.), seven over-sampling methods (SMOTE, etc.), and two integrated sampling methods (SMOTEENN, SMOTE-Tomek). Hybrid systems include (Balanced Bagging, etc.). The results show that class imbalance learning can improve the classification ability of the model. Compared with other imbalanced techniques, under-sampling techniques have the highest standard deviation (SD), and over-sampling techniques have the lowest SD. Over-sampling is a stable method, and the AUC in the model is generally higher than in other ways. Using ROS, the random forest performs the best predictive ability and is more suitable for the lung cancer datasets used in this study. The code is available at https://mkhushi.github.io/

A systematic review of ontology use in E-Learning recommender system

Abstract: Ontology and knowledge-based systems typically provide e-learning recommender systems. However, ontology use in such systems is not well studied in systematic detail. Therefore, this research examines the development and evaluation of ontology-based recommender systems. The study also discusses technical ontology use and the recommendation process. We identified multidisciplinary ontology-based recommender systems in 28 journal articles. These systems combined ontology with artificial intelligence, computing technology, education, education psychology, and social sciences. Student models and learning objects remain the primary ontology use, followed by feedback, assessments, and context data. Currently, the most popular recommendation item is the learning object, but learning path, feedback, and learning device could be the future considerations. This recommendation process is reciprocal and can be initiated either by the system or students. Standard ontology languages are commonly used, but standards for student profiles and learning object metadata are rarely adopted. Moreover, ontology-based recommender systems seldom use the methodology of building ontologies and hardly use other ontology methodologies. Similarly, none of the primary studies described ontology evaluation methodologies, but the systems are evaluated by nonreal students, algorithmic performance tests, statistics, questionnaires, and qualitative observations. In conclusion, the findings support the implementation of ontology methodologies and the integration of ontology-based recommendations into existing learning technologies. The study also promotes the use of recommender systems in social science and humanities courses, non-higher education, and open learning environments.

Realguard: A Lightweight Network Intrusion Detection System for IoT Gateways

Abstract: Cyber security has become increasingly challenging due to the proliferation of the Internet of things (IoT), where a massive number of tiny, smart devices push trillion bytes of data to the Internet. However, these devices possess various security flaws resulting from the lack of defense mechanisms and hardware security support, therefore making them vulnerable to cyber attacks. In addition, IoT gateways provide very limited security features to detect such threats, especially the absence of intrusion detection methods powered by deep learning. Indeed, deep learning models require high computational power that exceeds the capacity of these gateways. In this paper, we introduce Realguard, an DNN-based network intrusion detection system (NIDS) directly operated on local gateways to protect IoT devices within the network. The superiority of our proposal is that it can accurately detect multiple cyber attacks in real time with a small computational footprint. This is achieved by a lightweight feature extraction mechanism and an efficient attack detection model powered by deep neural networks. Our evaluations on practical datasets indicate that Realguard could detect ten types of attacks (e.g., port scan, Botnet, and FTP-Patator) in real time with an average accuracy of 99.57%, whereas the best of our competitors is 98.85%. Furthermore, our proposal effectively operates on resource-constraint gateways (Raspberry PI) at a high packet processing rate reported about 10.600 packets per second.

A Machine Learning Approach for Identification of Malignant Mesothelioma Etiological Factors in an Imbalanced Dataset

Abstract: In today’s world, lung cancer is a significant health burden, and it is one of the most leading causes of death. A leading type of lung cancer is malignant mesothelioma (MM). Most of the MM patients do not show any symptoms. Etiology plays a vital factor in the diagnosis of any disease. Positron emission tomography (PET), magnetic resonance imaging (MRI), biopsies, X-rays and blood tests are essential but costly and invasive MM risk factor identification methods. In this work, we mainly focused on the exploration of the MM risk factors. The identification of mesothelioma symptoms was carried out by utilizing the data of mesothelioma patients. However, the dataset was comprised of both healthy and mesothelioma patients. The dataset is prone to a class imbalance problem in which the number of MM patients significantly less than healthy individuals. To overcome the class imbalance problem, the synthetic minority oversampling technique has been utilized. The association rule mining-based Apriori algorithm has been applied to a preprocessed dataset. Before using the Apriori algorithm, both duplicate and irrelevant attributes were removed. Moreover, the numerical attributes were also classified into nominal attributes and the association rules were generated in the dataset. Our results show that erythrocyte sedimentation rate, asbestos exposure and its duration time, and pleural and serum lactic dehydrogenase ratio are major risk factors of MM. The severe stages of MM can be avoided by earlier identification of risk factors of the disease. The failure of identification of risk factors can lead to increased risk of multiple medical conditions, including cardiovascular diseases, mental distress, diabetes and anemia.

Review of automated time series forecasting pipelines

Abstract: Time series forecasting is fundamental for various use cases in different domains such as energy systems and economics. Creating a forecasting model for a specific use case requires an iterative and complex design process. The typical design process includes five sections (1) data preprocessing, (2) feature engineering, (3) hyperparameter optimization, (4) forecasting method selection, and (5) forecast ensembling, which are commonly organized in a pipeline structure. One promising approach to handle the ever‐growing demand for time series forecasts is automating this design process. The article, thus, reviews existing literature on automated time series forecasting pipelines and analyzes how the design process of forecasting models is currently automated. Thereby, we consider both automated machine learning (AutoML) and automated statistical forecasting methods in a single forecasting pipeline. For this purpose, we first present and compare the identified automation methods for each pipeline section. Second, we analyze these automation methods regarding their interaction, combination, and coverage of the five pipeline sections. For both, we discuss the reviewed literature that contributes toward automating the design process, identify problems, give recommendations, and suggest future research. This review reveals that the majority of the reviewed literature only covers two or three of the five pipeline sections. We conclude that future research has to holistically consider the automation of the forecasting pipeline to enable the large‐scale application of time series forecasting.