Showing papers in "International Journal of Computer Applications in 2020"

Identifying and Categorizing Opinions Expressed in Bangla Sentences using Deep Learning Technique

Abstract: Identifying and categorizing opinions in a sentence is the most prominent branch of natural language processing. It deals with the text classification to determine the intention of the author of the text. The intention can be for the presentation of happiness, sadness, patriotism, disgust, advice, etc. Most of the research work on opinion or sentiment analysis is in the English language. Bengali corpus is increasing day by day. A large number of online News portals publish their articles in Bengali language and a few News portals have the comment section that allows expressing the opinion of people. Here a research work has been done on Bengali Sports news comments published in different newspapers to train a deep learning model that will be able to categorize a comment according to its sentiment. Comments are collected and separated based on immanent sentiment. The deep learning algorithms that have been used are Convolutional Neural Network (CNN), Multilayer Perceptron, Long Short-Term Memory (LSTM). General Terms Sentiment Analysis, Deep Learning, Emotion Classification

Application of Soft-Set Relations and Soft Matrices in Medical Diagnosis using Sanchez’s Approach

Abstract: This article presents the concepts of soft set theory and its relations. It the common observation that more symptoms give more accuracy to calculate the disease, so we enhanced the work (Jafar at el., 2019) from trapezoidal to octagonal symptoms symbolically. So, we have used Sanchez’s Approach in terms of Octagonal fuzzy number. In the end for the better understanding the theory an elaborative example using hypothetical data has also been presented.

Penetration Testing of IEEE 802.11 Encryption Protocols using Kali Linux Hacking Tools

Abstract: The use of wireless network as a medium of communication has tremendously increased due to its flexibility, mobility and easy accessibility. Its usage is inevitable at hotels and restaurants, airports, organizations and currently predominant in homes. As large number of devices connect to wireless network, valuable and sensitive information are shared among users in the open air, attackers can easily sniff and capture data packets. This paper aims at using penetration testing to assess vulnerabilities and conduct attacks on Wireless Equivalent Privacy (WEP), Wi-Fi Protected Access (WPA) and 802.11i (WPA2) security protocols. The penetration testing was conducted using Kali Linux with its Aircrack-ng tools.

The Efficient Automatic Water Control Level Management using Ultrasonic Sensor

Abstract: In everyday life, million liters of water are getting wasted by overflowing and also heavy usage. To reduce this ,we have to implement some overflow control techniques to minimize the heavy wastage of water. For this purpose, we propose the Smart water level management system. This system uses an ultrasonic sensor to detect the water level and it is based on the sound made from flow of water it calculates the level of water in percentage and returns the value to the LCD display. It will calculate the level of water up to 100% by the intervals of 10%. After each interval it reaches the value is displayed on the LCD screen. This system is connected with relay switch which will automatically turn OFF and ON based on level of water. When the water level reaches 0% it will automatically turned ON and if it reaches 100% it will automatically turned OFF. We can also control the system online using a WiFi module which connects the system with web application we created. By also using PH sensor we can check the purity of the water. The result of this will be displayed on our web application.

A Survey of Game Theory Applications in Electrical Power Micro-Grid Systems

Abstract: The technology of Smart Grid is believed to be the future of power system networks. Smart Grid (SG) gains its importance due to its proven ability to improve stability, efficiency and robustness of electrical power grids. SG system consists mainly of two components which are electrical distribution system and communication layer. In the electrical distribution system, the generated energy comes from a network of distributed energy resources \"DERs\", which is called microgrid. In most cases, these DERs are recommended to be renewable energy sources \"RESs\" to reduce emissions and harmful environmental effects. One of the main drawbacks of renewable energy sources is that their availability varies with time and so that the micro-grid technology faces various technical challenges which motivate many researchers to adopt techniques to overcome these challenges. In this regard and due to its capability of studying complex interactions between independent rational players, game theory is expected to have a great contribution in the phase of design and analysis of micro-grids. In this paper, the fundamental concepts of game theory are streamlined, an overview on the applications of game theoretical concepts in various microgrid optimization problems are presented, a novel classification of research points covered by researchers are provided. Finally, some future opportunities that are expected to solve some of the technical challenges facing micro-grid technology are introduced.

Efficient, Ultra-facile Breast Cancer Histopathological Images Classification Approach Utilizing Deep Learning Optimizers

Abstract: Conventional approaches to breast cancer diagnosis are associated with drawbacks that ultimately affect the quality of diagnosis and subsequent treatment, pushing for the need for automatic and precise classification of breast cancer tumors. The advent of deep learning methods has witnessed an increasing interest in their applications in many tasks. The specific case of using convolutional neural networks with transfer learning has witnessed tremendous successes in many classification tasks. Nonetheless, with transfer learning, the sheer number of parameters associated with deep networks coupled with the distance disparity between source data and target data leave networks prone to overfitting, particularly in the case of limited data. Also, negative transfer may occur in the situation where the source and target domains are not related. This work proposes a simple convolutional neural network model trained from scratch for discriminating benign and malignant breast cancer tumors in histopathological images. Four deep learning optimization algorithms are leveraged and explored to ascertain how optimizers aid in finding good sets of parameters that help minimize loss and increase overall classification accuracy. By adopting a polynomial learning rate decay scheduling and implementing several

Does Awareness of Social Engineering Make Employees More Secure

Abstract: Social engineering has become one of the biggest security threats facing organizations. Rather than relying upon information security technical-related shortcomings to break into computer networks, social engineers make use of employees’ individual and organizational traits to deceive them. In such a scenario, it is crucial for organizations to ensure that their employees not only possess sound knowledge about information security but also about the concept of social engineering and threats emerging from social engineering attacks. This study aims to test whether awareness of social engineering can predict and explain individuals’ securityprotective practices. We conducted a survey of 265 employees working in different organizations in Saudi Arabia. The results suggest that awareness of social engineering is a positive predictor of security-protective practices above and beyond the predictability power of possessing information security knowledge. Thus, to reduce the probability of potential consequences of social engineering attacks, our study suggests that organizations should not only strive to enhance employees’ security knowledge but should also invest in increasing employees’ awareness of social engineering.

Handwritten Digit Recognition using Machine and Deep Learning Algorithms

Abstract: The reliance of humans over machines has never been so high such that from object classification in photographs to adding sound to silent movies everything can be performed with the help of deep learning and machine learning algorithms. Likewise, Handwritten text recognition is one of the significant areas of research and development with a streaming number of possibilities that could be attained. Handwriting recognition (HWR), also known as Handwritten Text Recognition (HTR), is the ability of a computer to receive and interpret intelligible handwritten input from sources such as paper documents, photographs, touch-screens and other devices [1]. Apparently, in this paper, we have performed handwritten digit recognition with the help of MNIST datasets using Support Vector Machines (SVM), Multi-Layer Perceptron (MLP) and Convolution Neural Network (CNN) models. Our main objective is to compare the accuracy of the models stated above along with their execution time to get the best possible model for digit recognition.

An Integrated Success Model for Adopting Biometric Authentication Technique for District Health Information Management System 2, Ghana

Abstract: This paper evaluated users’ perspective of adopting a biometric authentication technique by utilizing a proposed model derived from the technology acceptance model to determine how effective user accepts a proposed keystroke biometric authentication in an E-Health System. This paper combined the TAM of Davis et al with the success adoption model of DeLone and McLean where external variables for the TAM of Davis et al were derived from the four dimensions considered in the model of DM. The research design is a self-administered survey and the empirical part of the research is quantitative. The aim of the empirical part is to test the fit of the conceptual model with received data based on a questionnaire. This paper uses a crosssectional approach that provides a “snapshot” of the secured system’s usefulness and ease-of-use from the perspective of the end-users. Based on empirical findings, users with a higher degree of perceived usefulness, privacy concerns, and security concerns will demonstrate a more positive attitude towards adopting keystroke biometric authentication in an e-Health System. The proposed model and its elements prove that it can be a useful tool for decision makers in evaluating authentication techniques in e-health systems.

A Method of Secret Coding technique on Two Star Graphs

Abstract: Super Mean Labeling is one the interesting topic in graph theory. There also are search that has proved that the super mean labeling is not possible forth the star graphs. In this paper, we generalized the concept of super mean labeling on two star graphs and develop a technique of coding a secret messages using two star graph through super mean labeling. Two types of coding, picture and matrix coding have been illustrated by using different types of numbering of alphabets and algorithm is established. General Terms Coding Method, Matrix coding, Algorithms

IOT-based Smart Bins

Abstract: In this paper, we describe the formatting guidelines for IJCA Journal Submission Nowadays, waste management has become a major problem in life cycle. It includes the collection, transport, treatment and disposal of waste, together with monitoring and regulation of the waste management process. The significant cause of waste management is brisk growth in the rate of urbanization and thus there is a need of proper planning. To avoid all such harmful scenarios and maintain public cleanliness and health, we intend to propose a solution for this problem “Smart Bin” which will focus on resolving this problem. This process will alarm and notify the authorized person through a software when the garbage bin is about to fill. This system pivots around the overflowing bins and keeping the areas clean.

Multi-Robot Localization System using an Array of LEDs and LDR Sensors

Abstract: a new positioning system for indoor multi-robot localization is proposed. This system solves the problem of localization by using an array of Light Emitting Diodes (LEDs) distributed uniformly in the environment. The localization is achieved by collecting the information from a group of Light Dependent Resistor (LDR) sensors with which the robot is equipped. The binary search algorithm is used to reduce the time of the localization process by controlling the lights of the LED array. The minimum bounded circle algorithm is used to draw a virtual circle from the information collected by the LDR sensors and the center of this circle represents the robot’s location. This algorithm can be implemented in a multi-robot system when the main control unit can distinguish among the LDR sensors’ information. In the case of unknown information, the K-means Clustering algorithm is used to separate this information into clusters. Each cluster can be used to estimate the location of one robot. The suggested system is simulated and practically implemented in an environment with (32*32) arrays of LEDs. The simulation and experimental results of this system show good performance in the localization process.

Gender Classification and Age Estimation using Neural Networks: A Survey

Abstract: Researchers have shown more interest in soft biometrics area to fill the commination gaps between humans and machines with the growth of real-world application has increased day to day life. Soft-biometric consists of age, gender, ethnicity, height, facial measurements and etc. This paper contains a detail discussion about the contribution of the researchers in the area of gender classification and age estimation using neural networking. Most of the work is done using Convolutional neural networks and auto encoders. Various elements related to neural network model such as dataset, findings, calculative metrics and results are embraced for effortless interpretation of tabular correlation research. Finally, the authors summarize germane tasks for future various research aspects.

On Studying the Inter-relationships Amongst the various Challenges to Archaeological Sites Particularly in Context of Asian Countries

Abstract: Present research deals with first exploring the various challenges faced by government of developing countries for restoring the archaeological sites . Thereafter , it studies the inter-relationship amongst them using ISM methodology .

Implementation of MVC (Model-View-Controller) Concept in Develop Management Information System for Transportation Fees (Uang Jalan Operasional) and Revenue in Trucking Companies

Abstract: The trucking business in Cilegon and Anyer Indonesia is growing quite well in line with the growth of new middle and upper scale industries. The research case study was conducted at the logistics company PT. Buana Centra Swakarsa, one of whose business units is trucking, where supervision and control of transportation fees (Uang Jalan Operasional UJO) must be carried out properly so as not to interfere with the company's cash flow. Along with the development of the trucking business, the company's data processing must be done systematically, stored in a database with an integrated system so that data storage and data retrieval processes can be done more easily and accurately. In running the trucking business unit, PT. Buana Centra Swakarsa performs data processing of transportation fees (UJO) and revenue using spreadsheets Ms. Excel. Processing data like this, results in reports to manager or management that are not realtime, billing to customers is often late, recruitment of drivers and assigning jobs to drivers is not transparent. It is necessary to develop a computer-based information system to overcome these problems. Apart from the problems above, the process in this trucking system also often changes because it adapts to the contract or customer needs. In the future, changes in business processes or system development will inevitably occur. The development of this system requires a large amount of effort for the programmer to carry out program maintenance or repairs. An idea emerged to develop a system using the MVC (Model-View-Controller) concept so that program maintenance and adding modules would be easier and faster, especially if the programmer had to resign. In this research, the author will use the MVC concept with the Codeigniter framework. The methodology used to collect and analyze data is Research and Development (R & D) by collecting reports that are used by users, observation and interviews. The software development method used in a series of software development activities is the Waterfall Model and business process modeling using UML (Unified Modeling Language).

A Comprehensive Study of Intuitionistic Fuzzy Soft Matrices and its Applications in Selection of Laptop by using Score Function

Abstract: This paper is being carried out to discuss Intuitionistic Fuzzy Soft Matrices and their operations have been described employing decisive issues by using Score Function of Intuitionistic Fuzzy soft matrices resulting in the efficiency of Intuitionistic Matrices over fuzzy matrices. Finally at the end we have presented a case study for the best selection of laptop.

Indoor-DSL: A Model Driven Approach to Modeling Indoor Representation Models for a Navigation Infrastructure

Abstract: Internal positioning and navigation tools provide important information about semantic aspects of buildings, however information about indoor maps construction is not usually available and designing tools used for modeling indoor environments are hard to use or expensive. In this article, a Domain Specific Language (DSL) for modeling indoor environments is presented, allowing to create internal representation models, independent of platform. This work aims to create indoor map models for infrastructure of Indoor Navigation System (INavigS), where all the domain concepts present in the tool are used to specify models. The principles of the Model Driven Approach (MDA) are applied to define a metamodel language. In addition, a graphical interface is provided for modeling indoor environment models used by INavigS, allowing to model internal indoor environments quickly and easily, keeping the focus on concerns related to the domain of navigation infrastructure.

Unstructured Data Collection from APK files for Malware Detection

Abstract: For Malware Detection Machine Learning methods are applied extensively in ascertaining if the given APK file is malware or not. Machine learning methods are found to be less time consuming and less resource consuming compared to non-machine learning-based techniques. We have focused on Machine Learning methods for detecting unknown malware. For detecting the malware a researcher needs to create a dataset of its own. Our dataset generation process includes Android File Collection, Decompilation, and Feature Mining phases. We have already discussed the Android File Collection phase in our previous paper [1]. We have collected 15508 Malware files and 4000 Benign Files using Android File Collection. Android Files contains unstructured data in the form of text and XML files which are complex to process and store. Here our goal is to perform the decompilation of these collected Android files such that we get all the resources as well as the source code in a single instance. We aim to handle the big data in terms of Android Files and process them properly performing the Decompilation. In this paper, we have proposed an available automated solution for decompiling the files that also solves the complexity of handling and processing the big data. We have also discussed our

Automated Detection of Diabetic Retinopathy using Deep Residual Learning

Abstract: Significant amount of people suffer from Diabetic Retinopathy (DR), which is one of the major causes of vision loss. The incidence of this disease is even higher due to not being diagnosed at the right time. On numerous occasions, due to neglect and poor care, diabetic retinopathy can lead to significant damage to the eyes. That is why, early diagnosis of eye diseases, proper treatment and care for the disease can prevent vision loss. Referral of eyes with diabetic retinopathy for advanced assessment and treatment would aid in reducing the chances of vision loss, allowing proper diagnoses. The purpose of this study is to develop resilient and flexible diagnostic techniques for the detection of DR and to identify dynamic DR grading using residual networks to facilitate the network training that are significantly intense than previously used networks. Even though lots of research has been done on DR, its identifications remains challenging due to time and space complexity along with higher accuracy specificity. Here, a residual learning framework has been proposed that overcomes the challenges while efficiently detecting DR. Hence, using a high-end Graphics Processor Unit (GPU) the model has been trained on the publicly available Kaggle dataset and empirical evidence has been provided in order to support the results with a sensitivity of 95.6% and an accuracy of 93.20%.

Different Machine Learning based Approaches of Baseline and Deep Learning Models for Bengali News Categorization

Abstract: Today’s universe is the type of world where everyone thrives to live in virtual life. According to the perspective of the present time, the online news portal holds a major door to that gradually increasing greedy life. So around the globe, the various platform has been developed to fulfill the requirement of mankind. A heavy load of work has been carried out for making this platform autonomous in the English language. That’s why the machine learning approach is quite a fully developed field in English in news classification. But it can't be said the same for Bangla language. These put in the inspiration to do a research on this topic. So, here Bangla news which has been collected from newspapers and gathered around to make a Bengali Corpus. After preprocessing the news text, different sorts of procedures to classify the news text using baseline and deep learning models of Machine Learning are applied.

Assessment of Security Issues in Banking Sector of Libya

Abstract: Information security in the banking sector is heavily controlled as banks store and manage their clients’ private information. Information security has always been the responsibility of the information technology (IT) department in organizations. Banks have become a component of the internet and daily lives. Libyan banks facade limited cash due to the part up political circumstance since 2014, as a result of limited cash individuals incapable to get their salaries. To solve this problem, most of Libyan banks are set up online electronic installment arrangements to assist individuals in buying their day by day needs. However, Cyber-attacks increasing day by day, and this is the challenge facing by banking where data is critical. These incidents could be prevented by implementing adaptable countermeasures promptly and minimizing risk. In this paper, a framework is proposed to assess the information security issue in Libyan banks. The study aimed at the assessment of security strategy in Libyan banks to identify security gaps. To achieve the aim of this study data collected by interview information security staff to evaluate the current security strategy in Libyan banks. General Terms Security, Risk Assessment.

Predicting the Presence of Heart Diseases using Comparative Data Mining and Machine Learning Algorithms

Abstract: Heart disease, an example of cardiovascular diseases is the number one notable reason for the death of many people in the world. Of recent, studies have concentrated on using alternative efficient techniques such as data mining and machine learning in the diagnosis of diseases based on certain features of an individual. This study will use data exploratory and mining techniques to extract hidden patterns using python. By this, machine learning algorithms (logistic linear regression, decision tree classifier, Gaussian Naïve Bayes models) will be developed to predict the presence of heart diseases in patients. This will try to seek better performance in predicting heart diseases to reduce the number of tests require for the diagnosis of heart diseases. The k-fold cross validation approach will be used in assessing the resulting models for receiver operating characteristic (ROC) curves (sensitivity against specificity). The dataset was collected from UCI machine learning repository which contains information on patients with heart disease. The dataset has 14 attributes and measured on 303 individuals. General Terms Algorithms, pattern recognition, supervised learning, machine learning, heart disease.

Application of Libqual Method to Measure the Optimization of Slims 8 Akasia System in the Library of Smkn Mandiri 36 Jakarta

Abstract: The Activities of Library at SMKN Mandiri 36 Jakarta (a Vocational High School), prior to the use of library information system was still using manual system where library activities such as the registry for library visitors, borrowing and returning books were recorded in physical books. At this point, the library of SMKN Mandiri 36 Jakarta has been using the library information system, SliMS 8 Akasia, which means that library activities have applied automation in their system.. The method applied is the libqual method with 3 variables namely Quality and Access to Information (Information Control), Performance of officers in services (Affect of Services) and Library as Place, and then the data is processed through the Likert scale. The purpose of this study is to find out the quality of SLiMS library information system services to user satisfaction and to know the statements that need to be improved in order to improve the quality of SLiMS library information system services. The number of Respondents were 89 people consisted of teachers, education staff and students chose through the Slovin formula. The results from the analysis of the libquals calculation shows that the application of SLiMS service at the SMKN Mandiri 36 Jakarta library has been optimal and adequate. This can be seen from the average

Assessment of Quality of Internet Service Delivery in Selected Southwestern Universities in Nigeria

Abstract: This study examines types of Internet services available, frequency of use of the same, and also determines the quality of Internet delivered in the selected Universities in Southwestern Nigeria. The research design employed by this study is survey method, using questionnaire instrument. Three universities were selected, these include: Obafemi Awolowo University (OAU) Ile-Ife, Ekiti State University (EKSU) AdoEkiti and BOWEN University (BU), Iwo. The research adopted reliability, responsiveness, tangibility, empathy and assurance as metrics to measure the dynamics of the services delivered in the selected universities. The research purposively sampled four hundred academic and nonacademic staff including students in the selected universities. The findings show that Internet services which are mostly web related were present in the selected Institutions. The research specifically indicated that 97.6% of the respondents use World Wide Web (WWW), followed by e-mail (87.3%) and instant messaging (54.6%). Daily use of WWW mainly characterises the frequency of use of the services. This is followed by e-mail, which is been used daily and sometimes weekly. The overall quality of Internet services delivered was noted to be somewhat impressive when considering some indicators such as responsiveness and competencies of the staff responsible to providing the service. On the other hand, when considering information management, especially dissemination of information to users regarding downtime, schedule maintenance and other forms of bottlenecks were noted to be poor. It is therefore imperative for the stakeholders in the higher institutions, especially the top management to ensure that they continue to support free flow of Internet services, since this has the tendency to impact the productivity of the members of the University community at large. General Terms Internet service delivery, Quality of Service (QoS), Internet diffusion.

Review on the Security Threats of Internet of Things

Abstract: Internet of Things (IoT) is being considered as the growth engine for industrial revolution 4.0. The combination of IoT, cloud computing and healthcare can contribute in ensuring well-being of people. One important challenge of IoT network is maintaining privacy and to overcome security threats. This paper provides a systematic review of the security aspects of IoT. Firstly, the application of IoT in industrial and medical service scenarios are described, and the security threats are discussed for the different layers of IoT healthcare architecture. Secondly, different types of existing malware including spyware, viruses, worms, keyloggers, and trojan horses are described in the context of IoT. Thirdly, some of the recent malware attacks such as Mirai, echobot and reaper are discussed. Next, a comparative discussion is presented on the effectiveness of different machine learning algorithms in mitigating the security threats. It is found that the k-nearest neighbor (kNN) machine learning algorithm exhibits excellent accuracy in detecting malware. This paper also reviews different tools for ransomware detection, classification and analysis. Finally, a discussion is presented on the existing security issues, open challenges and possible future scopes in ensuring IoT security.

Fuzzy Mic-Mac Analysis on Factors Affecting the Bank Queuing Systems

Abstract: Present research focuses on identifying various barriers to bank queuing systems. It thereafter studies the interrelationship amongst them both by ISM methodology as well as fuzzy ISM methodology. The comparison of approaches is a novel attempt made in the field of bank queuing related research work to the best of author knowledge.