Constructing decision rules from naive bayes model for robust and low complexity classification

A large spectrum of classifiers has been described in the literature. One attractive classification technique is a Naïve Bayes (NB) which has been relayed on probability theory. NB has two major limitations: First, it requires to rescan the dataset and applying a set of equations each time to classify instances, which is an expensive step if a dataset is relatively large. Second, NB may remain challenging for non-statisticians to understand the deep work of a model. On the other hand, Rule-Based classifiers (RBCs) have used IF-THEN rules (henceforth, rule-set), which are more comprehensible and less complex for classification tasks. For elevating NB limitations, this paper presents a method for constructing a rule-set from the NB model, which serves as RBC. Experiments of the constructing rule-set have been conducted on (Iris, WBC, Vote) datasets. Coverage, Accuracy, M-Estimate, and Laplace are crucial evaluation metrics that have been projected to rule-set. In some datasets, the rule-set obtains significant accuracy results that reach 95.33 %, 95.17% for Iris and vote datasets, respectively. The constructed rule-set can mimic the classification capability of NB, provide a visual representation of the model, express rules infidelity with acceptable accuracy; an easier method to interpreting and adjusting from the original model. Hence, the rule-set will provide a comprehensible and lightweight model than NB itself

Deep learning-based classification model for botnet attack detection

Botnets are vectors through which hackers can seize control of multiple systems and conduct malicious activities. Researchers have proposed multiple solutions to detect and identify botnets in real time. However, these proposed solutions have difficulties in keeping pace with the rapid evolution of botnets. This paper proposes a model for detecting botnets using deep learning to identify zero-day botnet attacks in real time. The proposed model is trained and evaluated on a CTU-13 dataset with multiple neural network designs and hidden layers. Results demonstrate that the deep-learning artificial neural network model can accurately and efficiently identify botnets

INVESTIGATION OF GEOTECHNICAL SPECIFICATIONS OF SAND DUNE SOIL: A CASE STUDY AROUND BAIJI IN IRAQ

ABSTRACT: While more than half the land surface of Iraq consists of deserts covered mainly with sand dunes, little research has taken place to study the characteristics and the behavior of dune soils. This paper directed toward studying the geotechnical properties of dune sands taken from Baiji city (northwest of Iraq). A vast laboratory testing program was carried out to achieve the purpose of this paper. The physical tests, chemical tests, X-ray diffraction analysis, permeability test, compaction characteristics, compressibility and collapsibility tests; and shear strength tests were included in this program. The results indicate that soil of Baiji sand dune exhibits prefer engineering properties according to their state. As such, this soil is considered suitable for use in geotechnical constructions.   ABSTRAK: Walaupun lebih separuh daripada bumi Iraq terdiri daripada gurun yang dipenuhi dengan bukit-bukit pasir, tidak banyak penyelidikan dijalankan untuk mengkaji sifat-sifat dan ciri-ciri tanah pasir  tersebut. Kertas kerja ini menyelidik sifat geoteknikal bukit pasir yang diambil dari pekan Baiji (di bahagian barat utara Iraq).  Program penyelidikan makmal yang menyeluruh telah  dijalankan bagi mencapai objektif kajian ini. Ujian fizikal, ujian kimia, analisis belauan sinar-x, ujian kebolehtelapan, ciri pemadatan, faktor ketermampatan, ujian keruntuhan dan ujian kekuatan ricih diambilkira dalam program ini. Keputusan menunjukkan bahawa tanih bukit pasir Baiji mengutamakan ciri kejuruteraan berdasarkan keadaannya. Oleh itu, tanah ini dianggap sesuai untuk kegunaan pembinaan geoteknikal

A survey on deep transfer learning and edge computing for mitigating the COVID-19 pandemic

This is an accepted manuscript of an article published by Elsevier in Journal of Systems Architecture on 30/06/2020, available online: https://doi.org/10.1016/j.sysarc.2020.101830 The accepted version of the publication may differ from the final published version.Global Health sometimes faces pandemics as are currently facing COVID-19 disease. The spreading and infection factors of this disease are very high. A huge number of people from most of the countries are infected within six months from its first report of appearance and it keeps spreading. The required systems are not ready up to some stages for any pandemic; therefore, mitigation with existing capacity becomes necessary. On the other hand, modern-era largely depends on Artificial Intelligence(AI) including Data Science; and Deep Learning(DL) is one of the current flag-bearer of these techniques. It could use to mitigate COVID-19 like pandemics in terms of stop spread, diagnosis of the disease, drug & vaccine discovery, treatment, patient care, and many more. But this DL requires large datasets as well as powerful computing resources. A shortage of reliable datasets of a running pandemic is a common phenomenon. So, Deep Transfer Learning(DTL) would be effective as it learns from one task and could work on another task. In addition, Edge Devices(ED) such as IoT, Webcam, Drone, Intelligent Medical Equipment, Robot, etc. are very useful in a pandemic situation. These types of equipment make the infrastructures sophisticated and automated which helps to cope with an outbreak. But these are equipped with low computing resources, so, applying DL is also a bit challenging; therefore, DTL also would be effective there. This article scholarly studies the potentiality and challenges of these issues. It has described relevant technical backgrounds and reviews of the related recent state-of-the-art. This article also draws a pipeline of DTL over Edge Computing as a future scope to assist the mitigation of any pandemic

A novel AI-enabled framework to diagnose Coronavirus COVID-19 using smartphone embedded sensors: design study

This is an accepted manuscript of an article published by IEEE (in press). The accepted version of the publication may differ from the final published version.Coronaviruses are a famous family of viruses that cause illness in both humans and animals. The new type of coronavirus COVID-19 was firstly discovered in Wuhan, China. However, recently, the virus has widely spread in most of the world and causing a pandemic according to the World Health Organization (WHO). Further, nowadays, all the world countries are striving to control the COVID-19. There are many mechanisms to detect coronavirus including clinical analysis of chest CT scan images and blood test results. The confirmed COVID-19 patient manifests as fever, tiredness, and dry cough. Particularly, several techniques can be used to detect the initial results of the virus such as medical detection Kits. However, such devices are incurring huge cost, taking time to instal them and use. Therefore, in this paper, a new framework is proposed to detect COVID-19 using built-in smartphone sensors. The proposal provides a low-cost solution, since most of radiologists have already held smartphones for different daily purposes. Not only that but also ordinary people can use the framework on their smartphones for the virus detection purposes. Today’s smartphones are powerful with existing computationrich processors, memory space, and large number of sensors including cameras, microphone, temperature sensor, inertial sensors, proximity, colour-sensor, humidity-sensor, and wireless chipsets/sensors. The designed Artificial Intelligence (AI) enabled framework reads the smartphone sensors’ signal measurements to predict the grade of severity of the pneumonia as well as predicting the result of the disease

Touch-based Continuous Mobile Device Authentication: State-of-the-art, challenges and opportunities

This is an accepted manuscript of an article published by Elsevier in Journal of Network and Computer Applications on 08/07/2021, available online: https://doi.org/10.1016/j.jnca.2021.103162https://www.journals.elsevier.com/journal-of-network-and-computer-applications The accepted version of the publication may differ from the final published version.The advancement in the computational capability and storage size of a modem mobile device has evolved it into a multi-purpose smart device for individual and business needs. The increasing usage of this device has led to the need for a secure and efficient authentication mechanism. For secur­ing mobile devices, password, PIN, and swipe patterns are commonly used for user authentication. Entry-point face and fingerprint recognition have also gained traction in the past years. However, these authentication schemes cannot authenticate a user after the initial-login session. This limitation might put the device exposed to information theft and leakage if an illegitimate user could bypass the initial-login session. Therefore, a mobile device needs a continuous authentication mechanism that can protect a user throughout the entire working session, which complements the initial-login authentication to provide more comprehensive security protection. Touch biometric is a behavioural biometric that represents the touch behaviour pattern of a user when interacting with the touchscreen of the device. Touch biometric has been proposed as a continuous authentication mechanism, where the device can collect touch biometric data transparently while a user is using the device. However, there are still plenty of challenges and obstacles in touch-based continuous mobile device authenti­cation due to its challenges as a biometric modality. This paper provides a comprehensive overview of fundamental principles that underpin touch-based continuous mobile device authentication. Our work discusses state-of-the-art methods in touch data acquisition, behavioural feature extraction, user classification, and evaluation methods. This paper also discusses some challenges and opportunities in the current touch-based continuous mobile device authentication domain to obtain a broad research community and market acceptance

Combined Effect of Anticancer Agents and Cytochrome C Decorated Hybrid Nanoparticles for Liver Cancer Therapy

Hepatocellular carcinoma is an aggressive form of liver cancer that displays minimal symptoms until its late stages. Unfortunately, patient prognosis still remains poor with only 10% of patients surviving more than five years after diagnosis. Current chemotherapies alone are not offering efficient treatment, hence alternative therapeutic approaches are urgently required. In this work, we highlight the potential of combination of treatment of hepatocellular carcinoma with existing chemotherapies in combination with pro-apoptotic factor cytochrome C. In order to allow cytochrome C to cross the cellular membrane and become internalized, it has been immobilised onto the surface of hybrid iron oxide-gold nanoparticles. This novel approach has been tested in vitro on HepG2, Huh-7D and SK-hep-1 cell lines in order to elucidate potential as a possible alternative therapy with greater efficacy. The data from our studies show consistently that combining treatment of clinically used anticancer agents (doxorubicin, paclitaxel, oxaliplatin, vinblastine and vincristine) significantly increases the levels of apoptosis within the cell lines, which leads to cellular death. Hence, this combined approach may hold promise for future treatment regimes