Explainable Misinformation Detection Across Multiple Social Media Platforms

In this work, the integration of two machine learning approaches, namely domain adaptation and explainable AI, is proposed to address these two issues of generalized detection and explainability. Firstly the Domain Adversarial Neural Network (DANN) develops a generalized misinformation detector across multiple social media platforms DANN is employed to generate the classification results for test domains with relevant but unseen data. The DANN-based model, a traditional black-box model, cannot justify its outcome, i.e., the labels for the target domain. Hence a Local Interpretable Model-Agnostic Explanations (LIME) explainable AI model is applied to explain the outcome of the DANN mode. To demonstrate these two approaches and their integration for effective explainable generalized detection, COVID-19 misinformation is considered a case study. We experimented with two datasets, namely CoAID and MiSoVac, and compared results with and without DANN implementation. DANN significantly improves the accuracy measure F1 classification score and increases the accuracy and AUC performance. The results obtained show that the proposed framework performs well in the case of domain shift and can learn domain-invariant features while explaining the target labels with LIME implementation enabling trustworthy information processing and extraction to combat misinformation effectively.Comment: 28 pages,4 figure

The frontline antibiotic vancomycin induces a zinc starvation response in bacteria by binding to Zn(II).

Vancomycin is a front-line antibiotic used for the treatment of nosocomial infections, particularly those caused by methicillin-resistant Staphylococcus aureus. Despite its clinical importance the global effects of vancomycin exposure on bacterial physiology are poorly understood. In a previous transcriptomic analysis we identified a number of Zur regulon genes which were highly but transiently up-regulated by vancomycin in Streptomyces coelicolor. Here, we show that vancomycin also induces similar zinc homeostasis systems in a range of other bacteria and demonstrate that vancomycin binds to Zn(II) in vitro. This implies that vancomycin treatment sequesters zinc from bacterial cells thereby triggering a Zur-dependent zinc starvation response. The Kd value of the binding between vancomycin and Zn(II) was calculated using a novel fluorometric assay, and NMR was used to identify the binding site. These findings highlight a new biologically relevant aspect of the chemical property of vancomycin as a zinc chelator.This work was supported by funding from the Royal Society, UK (516002.K5877/ROG), the Medical Research Council, UK (G0700141). A.Z. was supported from the Said foundation and Cambridge Trust.This is the final version of the article. It first appeared from Nature Publishing Group via http://dx.doi.org/10.1038/srep1960

Bio-inspired algorithm optimization of neural network for the prediction of Dubai crude oil price

Previous studies proposed several bio-inspired algorithms for the optimization of Neural Network (NN) to avoid local minima and to improve accuracy and convergence speed. To advance the performance of NN, a new bio-inspired algorithm called Flower Pollination Algorithm (FPA) is used to optimize the weights and bias of NN due to its ability to explore very large search space and frequent chosen of similar solution. The FPA optimized NN (FPNN) was applied to build a model for the prediction of Dubai crude oil price unlike previous studies that mainly focus on theWest Texas Intermediate and Brent crude oil price benchmarks. Result

Агрессивная форма ювенильной макромастии

ПОДРОСТКИМОЛОЧНЫЕ ЖЕЛЕЗЫ ЧЕЛОВЕКА /АНОМАЛБОЛЕЗНИ РЕДКИЕМАКРОМАСТИЯГИПЕРТРОФИЯМОЛОЧНОЙ ЖЕЛЕЗЫ ПЛАСТИКАХИРУРГИЧЕСКИЕ ОПЕРАЦИИ ВОССТАНОВИТЕЛЬНЫЕМОЛОЧНОЙ ЖЕЛЕЗЫ БОЛЕЗНИ /ТЕР /ХИ

Ensemble Neurocomputing Based Oil Price Prediction

Abstract. In this paper, we investigated an ensemble neural network for the prediction of oil prices. Daily data from 1999 to 2012 were used to predict the West Taxes, Intermediate. Data were separated into four phases of training and testing using different percentages and obtained seven sub-datasets after implementing different attribute selection algorithms. We used three types of neural networks: Feed forward, Recurrent and Radial Basis Function networks. Finally a good ensemble neural network model is formulated by the weighted average method. Empirical results illustrated that the ensemble neural network outperformed other models

Lie Recognition with Multi-Modal Spatial–Temporal State Transition Patterns Based on Hybrid Convolutional Neural Network–Bidirectional Long Short-Term Memory

Recognition of lying is a more complex cognitive process than truth-telling because of the presence of involuntary cognitive cues that are useful to lie recognition. Researchers have proposed different approaches in the literature to solve the problem of lie recognition from either handcrafted and/or automatic lie features during court trials and police interrogations. Unfortunately, due to the cognitive complexity and the lack of involuntary cues related to lying features, the performances of these approaches suffer and their generalization ability is limited. To improve performance, this study proposed state transition patterns based on hands, body motions, and eye blinking features from real-life court trial videos. Each video frame is represented according to a computed threshold value among neighboring pixels to extract spatial–temporal state transition patterns (STSTP) of the hand and face poses as involuntary cues using fully connected convolution neural network layers optimized with the weights of ResNet-152 learning. In addition, this study computed an eye aspect ratio model to obtain eye blinking features. These features were fused together as a single multi-modal STSTP feature model. The model was built using the enhanced calculated weight of bidirectional long short-term memory. The proposed approach was evaluated by comparing its performance with current state-of-the-art methods. It was found that the proposed approach improves the performance of detecting lies

Recent advances in mobile touch screen security authentication methods: A systematic literature review

The security of the smartphone touch screen has attracted considerable attention from academics as well as industry and security experts. The maximum security of the mobile phone touch screen is necessary to protect the user's stored information in the event of loss. Previous reviews in this research domain have focused primarily on biometrics and graphical passwords while leaving out PIN, gesture/pattern and others. In this paper, we present a comprehensive literature review of the recent advances made in mobile touch screen authentication techniques covering PIN, pattern/gesture, biometrics, graphical password and others. A new comprehensive taxonomy of the various multiple class authentication techniques is presented in order to expand the existing taxonomies on single class authentication techniques. The review reveals that the most recent studies that propose new techniques for providing maximum security to smartphone touch screen reveal multi-objective optimization problems. In addition, open research problems and promising future research directions are presented in the paper. Expert researchers can benefit from the review by gaining new insights into touch screen cyber security, and novice researchers may use this paper as a starting point of their inquiry. - 2019The authors will like to acknowledge Tetfund Institutional Based Research Grant, Federal College of Education (Technical), Gombe. Tahir Musa Ibrahim is currently a logistics manager at Jos Electricity Distribution Plc, Bauchi Trading Zone, since 2014. He received his B.Sc. Computer Science degree in 2011, from a private university; Kwararafa University, Wukari, Taraba State, Nigeria. He is also currently pursuing his M.Sc. degree in Information Technology at National Open University, Nigeria. His current research topic is, recent advances on mobile touch screen security authentication methods: A systematic Literature Survey. He is also a member of International Project Management Professionals (IPMP). Shafi'i Muhammad Abdulhamid received his Ph.D. in Computer Science from Universiti Teknologi Malaysia (UTM), MSc in Computer Science from Bayero University Kano (BUK), Nigeria and a Bachelor of Technology in Mathematics/Computer Science from the Federal University of Technology Minna, Nigeria. His current research interests are in Cyber Security, Cloud computing, Soft Computing and BigData. He has published many academic papers in reputable International journals, conference proceedings and book chapters. He has been appointed as an Editorial board member for UPI JCSIT and IJTRD. He has also been appointed as a reviewer of several ISI and Scopus indexed International journals such as JNCA Elsevier, ASOC Elsevier, EIJ Elsevier, NCAA Springer, BJST Springer, & IJNS. He is a member of IEEE, IACSIT, IAENG, ISOC, Computer Professionals Registration Council of Nigeria (CPN), Cyber Security Experts Association of Nigeria (CSEAN) and Nigerian Computer Society (NCS). Presently, he is a Senior Lecturer at the Department of Cyber Security Science, Federal University of Technology Minna, Nigeria. Ala Abdusalam Alarood is currently an Assistant Professor of Computer Science in Faculty of Computer and Information Technology, University of Jeddah. He obtained his Bachelor, Masters and Ph.D. in Computer Science from Yarmok University Jordan and Universiti Teknologi Malaysia respectively. His current research interest is in the area of Information Security, Networks Security, Steganalysis, Machine Learning, and Neural Network. Haruna Chiroma is a faculty member in Federal College of Education (Technical), Gombe, Nigeria. He received B.Tech. and M.Sc. both in Computer Science from Abubakar Tafawa Balewa University, Nigeria and Bayero University Kano, respectively. He earned a Ph.D. in Artificial Intelligence from University of Malaya, Malaysia. He has published articles relevance to his research interest in international referred journals, edited books, conference proceedings and local journals. He is currently serving on the Technical Program Committee of several international conferences. His main research interest includes metaheuristic algorithms in energy modeling, decision support systems, data mining, machine learning, soft computing, human computer interaction, and social media in education, computer communications, software engineering, and information security. He is a member of the ACM, IEEE, NCS, INNS, and IAENG. Mohammed Ali Al-Garadi received Ph.D. degree from University of Malaya, Malaysia and M.Tech. degree in electronic and communication engineering from Jawaharlal Nehru Technological University, Hyderabad, India. He has published several articles in academic journals indexed in well reputed databases such as ISI-indexed and Scopus-indexed. His field of research is online social networking, text mining, deep learning, and information retrieval. Nadim Rana is a lecturer in the department of Information Systems, Jazan University Kingdom of Saudi Arabia. He is currently a Ph.D. researcher at the Faculty of Computer Science and Information Technology, University Technology Malaysia. His expertise is in Information Systems (Business Informatics), Databases and Data Mining. Amina Nuhu Muhammad currently a Graduate Assistant in the Department of Mathematics, Gombe State University. She received B.Sc. in Computer Science from Gombe State University in 2012. She is now an M.Sc. researcher in the Department of Mathematics, Abubakar Tafawa Balewa University Bauchi. Her research interest is on The Advances of Artificial Neural Network for Internet of Things. Adamu Abubakar received B.S. and M.S. degrees in Geography and Computer Science in School of Science from Bayero University Kano Nigeria in 2004 and 2006, respectively. He obtained his Ph.D. in Information Technology from the Department of Information System Kulliyyah of Information and Communication Technology, International Islamic University Malaysia; Since November 2012, he is with the Department of Computer Science School International Islamic University Malaysia currently as Assistant Professor. His current research interests include Machine Learning and Parallel computing. Khalid Haruna is currently a lecturer in the Department of Computer Science, Bayero University Kano, Nigeria. He received B.Sc. and M.Sc. degrees in Computer Science from Bayero University, Kano, Nigeria in 2011 and 2015 respectively. He has also received his PhD degree in Information Systems from the University of Malaya, Malaysia in 2018. His research interests include Recommender Systems, Internet of Things, Semantic Web, Big Data Analytics, Sentiment Analysis, Soft Set and E-Learning Systems. Lubna A. Gabralla has a Doctor of philosophy in Computer Science from Sudan University of Science & Technology, with a specilisation in Computer science: Artificial intelligence, Datamining, Machine Learning and Soft computing. She has previously held the position of Assistant Professor in the Faculty of Computer Science at Future University, Khartoum, and is also currently an Assistant Professor at the Sudan Academy for Banking Sciences, Khartoum- Sudan. She has published many publications and conference papers, including a book (Crude Oil Price Forecasting Using Machine Learning, ISBN: 978- 3-659-92,377-7).Scopu

A Survey: To Govern, Protect, and Detect Security Principles on Internet of Medical Things (IoMT)

The integration of medical equipment into the Internet of Things (IoT) led to the introduction of Internet of Medical Things (IoMT). Variation of IoT devices have been equipped in medical facilities. These devices provided convenience to healthcare provider since they can continuously monitor their patients in real-time, while allowing them to have greater physical flexibility and mobility. However, users of healthcare services (such as patients and medical staff) often are less concerned about security issues associated with IoT. These alleviate existing problems and jeopardize the lives of their patients by making them susceptible to attacks. Furthermore, IoMT applications have direct access to healthcare services because it handles sensitive patient information. Therefore, it is extremely important to preserve and establish the security and privacy of IoMT. This further justifies the need to investigate and address the related issues. Despite existing literature on security and privacy mechanisms, the domain still requires more attention. Therefore, this paper aims to discuss the security and privacy principles, as well as challenges associated with IoMT. Besides, a comprehensive analysis of privacy and security solutions for IoMT is also presented. In addition, we introduced a novel taxonomy of IoMT security and privacy based on cyber security principles such as “govern,” “protect,” and “detect”. In conclusion, this paper provides a discussion on existing challenges and future direction for researchers

Automated Diagnosis for Colon Cancer Diseases Using Stacking Transformer Models and Explainable Artificial Intelligence

Colon cancer is the third most common cancer type worldwide in 2020, almost two million cases were diagnosed. As a result, providing new, highly accurate techniques in detecting colon cancer leads to early and successful treatment of this disease. This paper aims to propose a heterogenic stacking deep learning model to predict colon cancer. Stacking deep learning is integrated with pretrained convolutional neural network (CNN) models with a metalearner to enhance colon cancer prediction performance. The proposed model is compared with VGG16, InceptionV3, Resnet50, and DenseNet121 using different evaluation metrics. Furthermore, the proposed models are evaluated using the LC25000 and WCE binary and muticlassified colon cancer image datasets. The results show that the stacking models recorded the highest performance for the two datasets. For the LC25000 dataset, the stacked model recorded the highest performance accuracy, recall, precision, and F1 score (100). For the WCE colon image dataset, the stacked model recorded the highest performance accuracy, recall, precision, and F1 score (98). Stacking-SVM achieved the highest performed compared to existing models (VGG16, InceptionV3, Resnet50, and DenseNet121) because it combines the output of multiple single models and trains and evaluates a metalearner using the output to produce better predictive results than any single model. Black-box deep learning models are represented using explainable AI (XAI)