Indian Sign Language Recognition System using Openpose

Volume 7 Issue 2 June - August 2019

Research Paper

Indian Sign Language Recognition System using Openpose

Pooja Malhotra*, Chirag K. Maniar**, Nikhil V. Sankpal***, Hardik R. Thakkar****

*-**** Department of Information Technology, K.J. Somaiya College of Engineering, Mumbai University, Mumbai, Maharashtra, India.

Malhotra, P., Maniar, C. K., Sankpal, N. V., Thakkar, H. R. (2019). Indian Sign Language Recognition System using Openpose, i-manager's Journal on Computer Science, 7(2), 43-50. https://doi.org/10.26634/jcom.7.2.15993

Abstract

Human beings communicate through language, be it verbal or be it a sign language that makes use of body motion. Hearing and Speech impaired people, having no way to communicate verbally, make use of Sign Language. They perform gestures using a sign language in order to convey their message and effectively communicate with each other. Since, not everyone knows about Indian Sign Language (ISL), it becomes difficult for normal people to fluently communicate with Hearing and Speech impaired community. This paper proposes ISL gesture recognition system in order to decrease this communication gap. The dataset consists of videos of ISL gestures, which are performed by different Subjects. The proposed system uses OpenPose library, which helps in creating the skeleton of human body and thus it provides keypoints of the whole human body frame by frame. The use of this library removes the dependency on lighting conditions and background. It helps in focusing on just the gesture movements. After extracting the keypoints, Long Short Term Memory (LSTM) is used for classification of gestures. LSTM model classifies which ISL gesture the particular video belongs to.

Language Translation: Enhancing Bi-Lingual Machine Translation Approach Using Python

Volume 7 Issue 2 June - August 2019

Research Paper

Language Translation: Enhancing Bi-Lingual Machine Translation Approach Using Python

Manish Rana*, Mohammad Atique**

*-**Department of Computer Science & Engineering, SantGadge Baba Amravati University, Amravati, India.

Rana, M., Atique, M.(2019). Language Translation: Enhancing Bi-Lingual Machine Translation Approach Using Python, i-manager's Journal on Computer Science, 7(2), 36-42. https://doi.org/10.26634/jcom.7.2.15597

Abstract

This paper shows the improvement in the work carried in Machine Translation as compared to the other techniques used. The work is the enhancement of “Enhancing Bi-Lingual Machine Translation Approach”. It shows the development in a Language Translation using Python, which consist of predefined packages like TextBlob and Google-API. The paper talks about enhancing Bilingual Machine Translation. English language into Indian Languages like Hindi, Marathi, Gujarati, Sindhi, Punjabi, Bengali, Urdu, and Dravidian languages. The work shown in the paper is implemented using a speech_recognition module, where speech input is taken from user so as to translate into any Indian language. After comparison with various techniques and research, the paper shows efficient result up to 94% accuracy.

Exploration of Heed Clustering Algorithm for Performance Improvement in Heterogenous WSNs

Volume 7 Issue 2 June - August 2019

Research Paper

Exploration of Heed Clustering Algorithm for Performance Improvement in Heterogenous WSNs

Nikita Gandotra*

Department of Computer Science & IT, University of Jammu, Jammu and Kashmir, India.

Gandotra, N.(2019). Exploration of Heed Clustering Algorithm for Performance Improvement in Heterogenous WSNs, i-manager's Journal on Computer Science, 7(2), 26-35. https://doi.org/10.26634/jcom.7.2.16142

Abstract

Wireless Sensor Networks are extensively used for monitoring in difficult terrains since these could be easily deployed due to their small size and their ability to work on their own without additional equipment as they communicate in adhoc manner. Each node consists of an individual power source in the form of a battery and remains active in the network till its energy is exhausted. To extend the network lifetime and optimising the use of restricted power supply, clustering algorithms are widely used to group neighbouring nodes and work in small clusters imitating the behaviour of the actual network. Hybrid Energy Efficient Distributed Clustering (HEED) algorithm was proposed to address the limited power supply and the network lifetime in WSNs. HEED selects cluster heads periodically according to their residual energy and node degree. This paper suggests a few improvements in the original HEED algorithm and a new model has been proposed based on these improvements. The algorithms are analysed with both homogeneous and heterogeneous node batteries and it was found that the proposed model improves the average energy of each node and extends the network lifetime.

Security Improvement for Realistic Data Using International Data Encryption Cryptographic Algorithm

Volume 7 Issue 2 June - August 2019

Research Paper

Security Improvement for Realistic Data Using International Data Encryption Cryptographic Algorithm

Anuraj Malav*

Department of R & D, University of Petroleum and Energy Studies (UPES), Dehradun, India.

Malav, A.(2019). Security Improvement for Realistic Data Using International Data Encryption Cryptographic Algorithm, i-manager's Journal on Computer Science, 7(2), 19-25. https://doi.org/10.26634/jcom.7.2.15465

Abstract

Now-a-days as the trend is to go online in every field like E-commerce, Banking, security is one of the main concerns. Some of this information additionally incorporates delicate data such as personal information or top secret government documents. So it is very important to protect personal data and information. Only DES or IDEA may be attacked by different sorts of cryptanalysis utilizing parallel procedure. In this paper, the author implements a symmetric key algorithm combining with two symmetric key algorithms, i.e. IDEA and 3DES with some modifications in order to improve the security and make it more complex for the attackers to break this algorithm. Using the combinations of these keys, we get 2^ (56+128) =2^184 that is 3.32 times stronger than conventional DES and 1.44 times stronger than conventional IDEA algorithm. The recommendation of S-IDEA algorithm is that it likewise be actualized in equipment utilizing VLSI innovation.

Multi-defense Framework for Mitigating Man in the Cloud Attack (MitC)

Volume 7 Issue 2 June - August 2019

Research Paper

Multi-defense Framework for Mitigating Man in the Cloud Attack (MitC)

Prabakeran Saravanan*, K. Swarnapriya **, Remina Agnes Priscilla ***

*-***Department of Computer Science & Engineering, K. C. G College of Technology, Chennai, Tamilnadu, India.

Saravanan, P., Swarnapriya, K., Priscilla, R. A.(2019). Multi-defense Framework for Mitigating Man in the Cloud Attack (MitC), i-manager's Journal on Computer Science, 7(2), 8-18. https://doi.org/10.26634/jcom.7.2.15674

Abstract

Cloud computing is the technology of forming a network of remote servers hosted on the Internet to manage process and store data. This technology has its own drawback from the security point of view. This research work aims to address the most recent attack called the man in the cloud attack and the possible solution to overcome it. The attack is tried to be defended at multilevel, so that we can protect our system to the at most level. The first level is to notify the user by detecting the phishing sites, through which the malware is sent into the user's system. At the second level, the user's token id is encrypted, so that the switching of credentials can be avoided.

Significance of R in Research

Volume 7 Issue 2 June - August 2019

Article

Significance of R in Research

G. Sateesh*, B. Padmaja**, M. V. Bhuvaneswari***

*-*** Department of Computer Science Engineering, Lendi College of Engineering, Andhra Pradesh, India.

Satheesh, G., Padmaja, B., Bhuvaneswari, M. V.(2019). Significance of R in Research, i-manager's Journal on Computer Science, 7(2), 1-7. https://doi.org/10.26634/jcom.7.2.15639

Abstract

R is a great degree adaptable factual programming language and condition that is open source and unreservedly accessible for all standard working frameworks. The aim of this paper is to bring significance of R to the allied fields of Data science and development of R in different innovations like Data Analysis, Image Processing, Big Data Analytics and Machine Learning everything under data science advancements. R studio contributes numerous packages, which are valuable in their respective environment and projects effortlessly. Its short syntax structure to quicken tasks on the data, loading and storing information for both nearby and over web, an extensive rundown of long list of tools for data analysis pulls in clients to work with R. R demonstrates that imperative strategies not accessible somewhere else can be actualized in R effectively.

Analysing Customer's Purchasing Pattern by Market Basket Analysis

Volume 7 Issue 1 March - May 2019

Research Paper

Analysing Customer's Purchasing Pattern by Market Basket Analysis

Ritu Khandelwal*, Divyasharma**, Harsh Kanwar***

* Assistant Professor, Department of Computer Science, International School of Informatics & Management, Jaipur, Rajasthan, India.

**-*** MCA Student, IIS University, Jaipur, Rajasthan, India.

Khandelwal, R., Sharma, D., Kanwar, H.(2019). Analysing Customer's Purchasing Pattern by Market Basket Analysis, i-manager's Journal on Computer Science, 7(1), 43-49. https://doi.org/10.26634/jcom.7.1.15582

Abstract

Data Mining is the process of extracting useful information from a large set of data. Market Basket Analysis is a technique of data mining which discovers an association between items with another. Market Basket Analysis refers to a process or technique, which identifies a customer's buying behavior or purchasing pattern, i.e. the items which are bought together by a customer in a single shopping cart. Market Basket Analysis is also termed as Association rule learning and another name for this technique is affinity Analysis. The main purpose of Market Basket Analysis is to extract the purchasing pattern of customers so that it increases the business efficiency and assists the retailers in making the decision regarding business in a profitable direction, increasing sales and make marketing strategies to compete with competitors. The main challenge for leading supermarkets is to attract a good number of customers, which can be done with the help of a data mining technique that is association rule mining. The frequent item sets are mined from the market basket to generate and after generation of the frequent items, the strongly associated item sets are generated with the help of support and confidence. This paper presents a recent survey of a supermarket for generating association rules to examine the customers’ buying or purchasing behavior.

Hybrid Crypto System Using Homomorphic Encryption and Elliptic Curve Cryptography

Volume 7 Issue 1 March - May 2019

Research Paper

Hybrid Crypto System Using Homomorphic Encryption and Elliptic Curve Cryptography

Prabakeran Saravanan*, Hemanth Kumar R.**, Arvind T.***, Bharath Narayanan ****

* Assistant Professor, Department of Computer Science, K.C.G College of Technology, Chennai, Tamil Nadu, India.

**-****Student, Department of Computer Science, K.C.G College of Technology, Chennai, Tamil Nadu, India.

Saravanan, P., Kumar, R.H., Arvind, T., Narayanan, B. (2019). Hybrid Cryptosystem Using Homomorphic Encryption and Elliptic Curve Cryptography Algorithm, i-manager's Journal on Computer Science, 7(1), 36-42. https://doi.org/10.26634/jcom.7.1.15667

Abstract

Providing security and privacy for the cloud data is one of the most difficult tasks in recent days. The privacy of the sensitive information ought to be protecting from the unauthorized access for enhancing its security. Security is provided using traditional encryption and decryption process. One of the drawbacks of the traditional algorithm is that it has increased computational complexity, time consumption, and reduced security. The authors have proposed a scheme where the original data gets encrypted into two different values. Elliptical Curve Cryptography (ECC) and Homomorphic are combined to provide encryption. The data in each slice can be encrypted by using different cryptographic algorithms and encryption key before storing them in the cloud. The objective of this technique is to store data in a proper secure and safe manner in order to avoid intrusions and data attacks meanwhile it will reduce the cost and time to store the encrypted data in the Cloud Storage.

ARPIT: Ambiguity Resolver for POS Tagging of Telugu, an Indian Language

Volume 7 Issue 1 March - May 2019

Research Paper

ARPIT: Ambiguity Resolver for POS Tagging of Telugu, an Indian Language

Suneetha Eluri*, Sumalatha Lingamgunta**

* Research Scholar and Assistant Professor, Department of Computer Science and Engineering, Jawaharlal Nehru Technological University Kakinada, Andhra Pradesh, India.

** Professor, Department of Computer Science and Engineering, Jawaharlal Nehru Technological University Kakinada, Andhra Pradesh, India.

Eluri, S., Lingamgunta, S.(2019). ARPIT: Ambiguity Resolver for POS Tagging of Telugu, an Indian Language, i-manager's Journal on Computer Science, 7(1), 25-35. https://doi.org/10.26634/jcom.7.1.15372

Abstract

Parts of Speech tagging (POS) is an essential preliminary task of Natural Languages Processing (NLP). Its aim is to assign parts of speech tag to each word in corpus. The basic POS tags are noun, pronoun, verb, adjective and adverb, etc. POS tags are needed for speech analysis and recognition, Machine translation, Lexical analysis like word sense disambiguation, named entity recognitions, Information retrieval and this system also helped to uncover the sentiments of given text in opinion mining. At the same time, many Indian languages lack POS taggers because the research towards building basic resources like corpora and morphological analyzers is still in its infancy. Henceforth in this paper, a POS tagger for Telugu language, a South Indian language is proposed. In this model, the lexemes are tagged with various POS tags by using pre-tagged corpus, however a word may be tagged with multiple tags. This ambiguity in tag assignment is resolved with Stochastic Machine Learning Technique, i.e. Hidden Markov Model (HMM) Bigram tagger, which uses probabilistic information built based on contextual information or word tag sequences to resolve the ambiguity. In this system, the authors have developed a pre-tagged corpus of size 11000 words with standard communal tag sets for Telugu language and the same is used for testing and training the model. This model tested with input text data consists of different number of POS tags at word level and achieved the average performance accuracy of 91.27% in resolving the ambiguity.

Enhancing Wordnet against Overlapping Returns of Senses for Efficient Polysemy Representation in Ontology Development

Volume 7 Issue 1 March - May 2019

Research Paper

Enhancing Wordnet against Overlapping Returns of Senses for Efficient Polysemy Representation in Ontology Development

Enesi Femi Aminu*, Qasim Adewale Fajobi**, Ishaq Oyebisi Oyefolahan***, Muhammad Bashir Abdullahi****, Muhammadu Tajudeen Salaudeen*****

* Lecturer, Department of Computer Science, Federal University of Technology, Minna, Nigeria.

** Department of Computer Science, Federal University of Technology, Minna, Nigeria.

*** Senior Lecturer, Department of Information and Media Technology, Federal University of Technology, Minna, Nigeria.

**** Head, Department of Computer Science, Federal University of Technology, Minna, Nigeria.

***** Senior Lecturer and Head, Department of Crop Production, Federal University of Technology, Minna, Nigeria.

Aminu,E.F., Fajobi,Q.A., Oyefolahan,I.O., Abdullahi,M.B., Salaudeen,M.T.(2019) Enhancing WordNet Against Overlapping Returns of Senses for Efficient Polysemy Representation in Ontology Development, i-manager's Journal on Computer Science, 7(1), 17-24. https://doi.org/10.26634/jcom.7.1.15720

Abstract

In order to have a web of relevant information retrieval otherwise, known as semantic web, ontology has been identified as its core stronghold to actualize the dream. Ontology is a data modeling or knowledge representation technique for structured data repository premised on collection of concepts with their semantic relationships and constraints on particular area of knowledge. Example is wordNet which is linguistic based and popular ontology which has been greatly used to be part of ontology based information retrieval system development. However, the existing wordNet would affect the expected accurate results of such system owing to its overlapping return of senses. Therefore, this research aimed to design algorithm with the aid of extended Levenshtein similarity matching function and WordWeb to proffer solution to the militating problem. At the end, an enhanced wordNet that devoid of overlapping returns of senses for efficient polysemy representation in terms of user's time and system's memory would be achieved.

Machine Learning Approach to Sentiment Analysis of Users' Movie Reviews

Volume 7 Issue 1 March - May 2019

Research Paper

Machine Learning Approach to Sentiment Analysis of Users' Movie Reviews

Adebayo Adetunmbi*, Oluwafemi A. Sarumi**, Oluwayemisi Olutomilola***, Olutayo Boyinbode****

* Professor, Department of Computer Science, Federal University of Technology, Akure, Nigeria.

** Faculty member, Department of Computer Science, Federal University of Technology, Akure, Nigeria.

***_**** Department of Computer Science, Federal University of Technology, Akure, Nigeria.

Adetunmbi, A.,Sarumi, O.A., Olutomilola, O.,Boyinbode, O.(2019). Machine Learning Approach to Sentiment Analysis of Users’ Movie Reviews, i-manager's Journal on Computer Science, 7(1), 9-16. https://doi.org/10.26634/jcom.7.1.15701

Abstract

The exponential rate at which textual information is being generated over the internet makes extracting useful knowledge from these vast volumes of information essential and increasingly important. Analysis of sentiments or opinion engineering plays a vital role in retrieving actionable knowledge from users or customer web reviews. Sentiment analysis of movie reviews help users to quickly determine which movie to purchase or watch. Also, it helps movie producers to get customers feedback on their movies. This paper presents a movie reviews sentiment classification model using Naïve Bayes (NB), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN). Prior to building sentiment classification models, data pre-processing techniques were applied on the labelled polarity movie reviews dataset. The most important features (unigram and the mixed-unibigram) were extracted from the dataset using Term Frequency (TF) and Term Inverse Document Frequency (TF-IDF) feature extraction techniques. The extracted features were classified using three (NB, SVM, and KNN) supervised machine learning algorithms. The result of the implementation shows that KNN had 95.9% accuracy with TF and mixed-unibigram features, NB and SVM had an accuracy of 90.6% and 92.22%, respectively. Therefore, the result shows that KNN gives the best performance.

An Online Lecturer Evaluation System: A Case Study of FUT Minna

Volume 7 Issue 1 March - May 2019

Research Paper

An Online Lecturer Evaluation System: A Case Study of FUT Minna

Sulaiman Sufyan Danmallan*, Adeoluwa David Apata **, Oluwaseun Adeniyi Ojerinde ***, Segilola Ifeoma Mustapha ****, Olawale Surajudeen Adebayo*****

*-****Department of Computer Science, Federal University of Technology, Minna, Nigeria.

*****Department of Cyber Security Science, Federal University of Technology, Minna, Nigeria.

Danmallan, S.S., Apata, A.D., Ojerinde, O.A., Mustapha, S.I.,Adebayo, O.S.(2019). An Online Lecturer Evaluation System: A Case Study of FUT Minna, i-manager's Journal on Computer Science, 7(1), 1-8. https://doi.org/10.26634/jcom.7.1.16126

Abstract

The evaluation of lecturers by students in higher institutions is important to monitor and control academic quality. This work was conducted to evaluate lecturers' teaching methods in Federal University of Technology (FUT Minna) using survey evaluation technique and implemented on a JavaFX platform. The scope of this work is for all undergraduate students in FUT Minna and quality assurance staff. The Lecturer Evaluation System (LES) was developed integrating various components in computer science. The participants for the LES were drawn from various departments in the institution consisting of 20 students each from 100 to 500 levels. The LES was evaluated using the System Usability Scale (SUS) and aggregations were obtained from students' reviews. Results showed that 90% of 400 level students and 60% of 200 students level preferred the system, 40% of 100 level students found the system’s usage tedious, 50% of the 300 level students and 30% of 500 level students found the system cumbersome to operate. It was concluded that the system was easy to interact with, workability process was not complex, and it could be used to assess lecturers' teaching methods.

Performance Evaluation of Cultural Artificial Bee Colony and Cultural Artificial Fish Swarm Algorithm

Volume 6 Issue 4 December - February 2019

Research Paper

Performance Evaluation of Cultural Artificial Bee Colony and Cultural Artificial Fish Swarm Algorithm

Busayo Hadir Adebiyi*, Ahmed Tijani Salawudeen**, Risikat Folashade O. Adebiyi***

*-***Department of Computer Engineering, Ahmadu Bello University, Zaria, Nigeria.

Adebiyi, B.H., Salawudeen, A.T., Adebiyi,R.F.(2019) Performance Evaluation of Cultural Artificial Bee Colony and Cultural Artificial Fish Swarm Algorithm,i-manager's Journal on Computer Science, 6(4),43-50. https://doi.org/10.26634/jcom.6.4.15725

Abstract

The introduction of Computational Intelligence (CI) algorithms in the area of optimizations have been given significant attention in science and engineering allied disciplines. This is because they always find answers to a problem while maintaining perfect stability among its components. However, these algorithms sometimes suffer from premature convergence and fitness stagnation, which usually originates from the lack of explorative search capability of its perturbation operator. This paper presents a comparative performance of a Cultural Artificial Bee Colony (called the CABCA) algorithm and Cultural Artificial Fish Swarms Algorithm (called the mCAFAC). In both algorithms (CABCA and mCAFAC), the normative and situational knowledge is employed to guide the direction and step size of the population (ABC and AFSA). Four variants of each ABC and AFSA were developed using different configurations of cultural knowledge in Matlab/Simulink environment. A collection of twelve optimization benchmark functions was used to test the performance, and it was found that the modified algorithms (CABCA and mCAFAC) outperformed their respective standard (ABC and AFSA) algorithms.

On the Use of Extreme Learning Machines for Detecting Anomalies in Students’ Results

Volume 6 Issue 4 December - February 2019

Research Paper

On the Use of Extreme Learning Machines for Detecting Anomalies in Students’ Results

Hamza O. Salami *, Mohammed O. Yahaya**

* Lecturer,Department of Computer Science, Federal University of Technology, Minna, Niger State, Nigeria..

** Lecturer,Department of Computer Science and Engineering, University of Hafr Al Batin, Saudi Arabia.

Salami, H.O., Yahaya, M.O.(2019) On the Use of Extreme Learning Machines for Detecting Anomalies in Students’ Results,i-manager's Journal on Computer Science, 6(4),34-42. https://doi.org/10.26634/jcom.6.4.15724

Abstract

Examinations are means of assessing the knowledge or skills that students have acquired, after having been taught over a period of time. Anomalies in student results are noteworthy observations that require additional clarifications. Manual detection of anomalies in results leads to human errors and wastage of manpower. This paper describes how extreme learning machines can be used to automatically detect anomalies in student results. The results show that using extreme learning machines almost always produces better or equal results compared to decision trees.

Development of a Road Surface Condition Monitoring and Database System

Volume 6 Issue 4 December - February 2019

Research Paper

Development of a Road Surface Condition Monitoring and Database System

H. Bello-Salau*, A. M. Aibinu**, A. J. Onumanyi***, S. Ahunsi, ****, E. N. Onwuka*****, J. J. Dukiya******

* Lecturer,Department of Computer Engineering, Ahmadu Bello University Zaria, Nigera.

** Professor,Department of Mechatronics Engineering, Federal University of Technology, Minna, Niger State.

*** Lecturer,Department of Telecommunication Engineering, Federal University of Technology, Minna, Niger Statee.

**** Graduate,Department of Telecommunication Engineering, Federal University of Technology, Minna, Niger Statee.

***** Professor,Department of Telecommunication Engineering, Federal University of Technology, Minna, Niger Statee.

****** Professor,Department of Transport Management, Federal University of Technology, P.M.B. 65, Minna, Niger State.

Bello-Salau, H., Aibinu, A. M., Onumanyi, A.J., Ahunsi, S. Onwuka E.N., Dukiya,J.J.(2019) Development of a Road Surface Condition Monitoring and Database System,i-manager's Journal on Computer Science, 6(4),25-33. https://doi.org/10.26634/jcom.6.4.15723

Abstract

This paper proposes a road surface condition monitoring device. The design features the use of a programmed accelerometer sensor deployed to respond to vehicular vibrations as a function of the vehicle's acceleration due to gravity (g-force). Furthermore, a database was created and hosted online to store the traces acquired over the different test surfaces. The test results show that the proposed system successfully sensed the utilized road surfaces, and effectively logged the acquired traces into the created database. This device will be beneficial to road maintenance agencies for road surface monitoring, and it can be installed in both manned and unmanned vehicles to enhance road navigation. In addition, the stored traces can be freely accessed and used by researchers working in related areas.

Comparative Study of Various Machine Learning Algorithms for Tweet Classification

Volume 6 Issue 4 December - February 2019

Research Paper

Comparative Study of Various Machine Learning Algorithms for Tweet Classification

Umar Abubakar*, Sulaimon A. Bashir**, Muhammad Bashir Abdullahi***, Olawale S. Adebayo****

*,**,*** Department of Computer Science, Federal University of Technology, Minna, Nigeria.

**** Department of Cyber Security Science, Federal University of Technology, Minna, Nigeria.

Abubakar, U., Bashir, S. A., Abdullahi, M. B., Adebayo, O. S.(2019 Comparative Study of Various Machine Learning Algorithms for Tweet Classification,i-manager's Journal on Computer Science, 6(4),12-24. https://doi.org/10.26634/jcom.6.4.15722

Abstract

Twitter is a social networking platform that has become popular in recent years. It has become a versatile information dissemination tool used by individuals, businesses, celebrities, and news organizations. It allows users to share messages called tweets with one another. These messages can contain different types of information from personal opinions of users, advertisement of products belonging to all kinds of businesses to the news. Tweets can also contain messages that are racist, bigotry, offensive, and of extremist views as shown by research. Manual identification of such tweets is impossible as hundreds of millions of tweets are posted every day and hence a solution to automate the identification of these types of tweets through classification is required for the Twitter administrators or an intelligence and security analyst. This paper presents a comparative study of traditional machine learning algorithms and deep learning algorithms for the task of tweet classification to detect different categories of abusive languages with the aim to determine which algorithm performs best in detecting abusive language that is prevalent on social media. Two approaches for building feature vectors were explored. Feature vectors based on the bag-of-words method and feature vectors based on word embeddings. These two methods of feature representation were evaluated in this paper using tweet messages representing five abusive language categories. The experiments show that the deep learning algorithms trained with word embeddings outperformed all the other machine learning algorithms that were trained with feature vectors based on the bag-of-words approach.

Enhanced Query Expansion Algorithm: Framework for Effective Ontology Based Information Retrieval System

Volume 6 Issue 4 December - February 2019

Research Paper

Enhanced Query Expansion Algorithm: Framework for Effective Ontology Based Information Retrieval System

Enesi Femi Aminu*, Ishaq Oyebisi Oyefolahan**, Muhammad Bashir Abdullahi***, Muhammadu Tajudeen Salaudeen****

* Lecturer,Department of Computer Science, Federal University of Technology, Minna, Nigeria.

** Senior Lecturer,Department of Information and Media Technology, Federal University of Technology, Minna, Nigeria.

*** Head,Department of Computer Science, Federal University of Technology, Minna, Nigeria.

**** Senior Lecturer,Department of Crop Production, Federal University of Technology, Minna, Nigeria.

Aminu,E.F., Oyefolahan,I.O., Abdullahi,M.B., Salaudeen,M.T.(2019) Enhanced Query Expansion Algorithm: Framework for Effective Ontology Based Information Retrieval System,i-manager's Journal on Computer Science, 6(4),1-11. https://doi.org/10.26634/jcom.6.4.15721

Abstract

The strength of an Information Retrieval System lies on its ability to retrieve relevant information or documents according to user's intent by considering a high level of precision and a low level of irrelevant recall of results. A recent development to actualize this dream is the application of ontology. Therefore, Ontology-Based Information Retrieval is becoming an interesting area in the current research trend of ontology and semantic web. However, the sufficiency of developing domain ontology alone to efficiently and effectively take care of information retrieval becomes a research issue. Thus, to address the research gap, a technique called Query Expansion has been identified as a veritable tool. Query Expansion is a process of expanding initial user's query term(s) with the aid of a technology such as wordNet to return relevant results according to user's intent. But returns of query results using the existing wordNet is challenging in normal or inflected terms, such as synonyms or polysemy (word mismatch). Therefore, this paper proposes improved query expansion algorithm as framework to effectively and efficiently develop ontology based information retrieval system.

Development of a Predictive Model for the Detection of CAPTCHA Smuggling Attacks Using Supervised Deep Learning based Approach

Volume 6 Issue 3 September - November 2018

Research Paper

Development of a Predictive Model for the Detection of CAPTCHA Smuggling Attacks Using Supervised Deep Learning based Approach

Moses O. Omoyele*, Joseph A. Ojeniyi**, Olawale S. Adebayo***

* Research Scholar, Department of Cyber Security Science, Federal University of Technology, Minna, Nigeria.

**-*** Lecturer, Department of Cyber Security Science, Federal University of Technology, Minna, Nigeria.

Omoyele, M., Ojeniyi, J. A., Adebayo, O. S.(2018) Development of a Predictive Model for the Detection of CAPTCHA Smuggling Attacks Using Supervised Deep Learning based Approach, ,i-manager's Journal on Computer Science 6(3),42-49. https://doi.org/10.26634/jcom.6.3.15699

Abstract

CAPTCHA is a piece of program designed to distinguish human beings from bots. These are computer generated tests which can be solved by humans but will be difficult to be solved by computers. Bots smuggled CAPTCHAs are gradually on the increase in order to deceive unsuspecting users and inadvertently infect systems. From the available literature reviewed so far, there is no model to detect or predict CAPTCHA smuggling attack. The aim of this work is to come up with a model capable of predicting this attack. The approach used was based on deep supervised neural network approach. In order to achieve the aim, framework based on hyperparameter specification was developed. The model was evaluated on the available CAPTCHA smuggling dataset. The accuracy of prediction achieved in this work is 77.89% at consistency of 0.1543. The sensitivity and specificity of the model are 78.11% and 78.2%, respectively.

Evaluation of Classification Algorithms for Phishing URL Detection

Volume 6 Issue 3 September - November 2018

Research Paper

Evaluation of Classification Algorithms for Phishing URL Detection

Oluyomi Ayanfeoluwa *, Oluwafemi Osho**, Maryam Shuaib***

* President, Information System Audit & Control Association (ISACA), Federal University of Technology, Minna, Nigeria.

** Lecturer, Department of Cyber Security Science, Federal University of Technology Minna, Nigeria.

*** Former Special Assistant, ICT Development to the Governer of Nigeria State, Nigeria.

Oluyomi, A., Osho, O., Shuaib, M.(2018) Evaluation of Classification Algorithms for Phishing URL Detection, i-manager's Journal on Computer Science 6(3),34-41. https://doi.org/10.26634/jcom.6.3.15698

Abstract

A phishing URL is a web address created with the intent of deceiving users into releasing their personal and private data or downloading malware into the users' systems without their knowledge. Increase in the adoption of the Internet has led to corresponding increase in the number of phishing sites globally. Many classification techniques have been developed for detecting phishing URLs. This paper seeks to evaluate the performances of existing techniques. With dataset obtained from UCI Machine Learning Repository, the algorithms were assessed in terms of Accuracy, Precision, Recall, F-Measure, Receiver Operating Characteristic (ROC) area and Root Mean Squared Error (RMSE). From analysis and comparison with results from related literature, the Random Forest was found to perform best.

An Adaptive Personnel Selection Expert System to Support Organization's Personnel Recruitment Decision Process

Volume 6 Issue 3 September - November 2018

Research Paper

An Adaptive Personnel Selection Expert System to Support Organization's Personnel Recruitment Decision Process

Muhammad Ahmad Shehu*, Abdu Haruna **, Abdulwahab Ahmed Jatto***, Umar Hussein****

*-** Assistant Lecturer, Department of Computer Science, Federal University, Lokoja, Nigeria.

*** Graduate, Department of Computer Science, Federal University, Lokoja, Nigeria.

**** Graduate, Department of Computer Science, Salem University Lokoja. Nigeria.

Shehu, M. A., Jatto, A. A., Abdu. H., Hussein, U.(2018)An Adaptive Personnel Selection Expert System to Support Organization’s Personnel Recruitment Decision Process, i-manager's Journal on Computer Science 6(3),25-33. https://doi.org/10.26634/jcom.6.3.15700

Abstract

Personnel recruitment operation which involves selecting the right person for the right job is an essential human resource operation of an organization due to the fact that organizations’ performance depends on its personnel performance. However, personnel selection for recruitment operation of human resource management has various operational behaviors, and when carrying out the operation the operational behaviors should be considered. Due to this consideration, carrying out personnel selection for recruitment operation is complex. To minimize the complexity, various research works developed personnel selection expert systems to carry out personnel selection for recruitment operation considering some of its operational behaviors. However, the behavioral change of personnel selection operation was not considered during the development of their respective proposed expert systems. This study identified an adaptive personnel selection model developed in a research work and the behavioral change feature of personnel selection for recruitment operation was considered. The adaptive personnel selection model was developed using a C4.5 decision tree and frequent and non-frequent patter analysis of data mining. However, the adaptive feature of the adaptive personnel selection model was improved and then implemented as this study proposed adaptive personnel selection expert system.

Password Knowledge Versus Password Management

Volume 6 Issue 3 September - November 2018

Research Paper

Password Knowledge Versus Password Management

Victor N. Adama*, Noel Moses Dogonyaro**, Victor L. Yisa***, Baba Meshach****, Ekundayo Ayobami*****

*,***** Lecturer, Department of Computer Science, Federal University of Technology, Minna, Nigeria.

**-***,**** Lecturer, Department of Cyber Security Science, Federal University of Technology, Minna, Nigeria.

Adama, V. N., Dogonyaro, N. M., Yisa, V. L., Meshach, B., Ayobami, E. (2018). Password Knowledge Versus Password Management Practice a Case Study Federal University of Technology, Minna, i-manager's Journal on Computer Science, 6(3),16-24. https://doi.org/10.26634/jcom.6.3.15697

Abstract

User authentication is one of the most important security characteristics of any system given today's globalized digital life style. The safety and security of sensitive data, privacy and also critical infrastructure relies primarily on authentication. Amongst all authentication schemes, text-based passwords are the most deployed across various platforms, thus the importance of evaluating user password management practice cannot be overemphasized. This research, via, a case study aimed at establishing the theoretical password knowledge in comparison to actual password management practice of staff and students from Information Technology (IT) inclined departments of the Federal University of Technology, Minna. Results from the survey reveal that the target respondents are knowledgeable on good password management policies. However, actual password practice results by the respondents showed that they do not comply and effectively implement the theoretical password knowledge they possess. Thus it can be concluded that there is a significant difference between what respondents know compared to their actual practice. Numerous implications abound when this is the case as it makes users more vulnerable to security risks of unauthorized access by unauthorized users.

A Soft Computing Approach to Detect E-Banking Phishing Websites using Artificial Neural Network

Volume 6 Issue 3 September - November 2018

Research Paper

A Soft Computing Approach to Detect E-Banking Phishing Websites using Artificial Neural Network

Shafi’i Muhammad Abdulhamid*, Mubaraq Olamide Usman**, Oluwaseun A. Ojerinde***, Victor Ndako Adama****, John K. Alhassan*****

*,*****Department of Cyber Security Science, Federal University of Technology, Minna, Nigeria.

**,***,**** Department of Computer Science, Federal University of Technology, Minna, Nigeria.

Abdulhamid, S. M., Usman, M. O., Ojerinde, O. A., Adama, V. N., Alhassan, J. K. (2018). A Soft Computing Approach to Detecting E-Banking Phishing Websites using Artificial Neural Network, i-manager's Journal on Computer Science, 6(3),7-15. https://doi.org/10.26634/jcom.6.3.15696

Abstract

Phishing is a cybercrime that is described as an art of cloning a web page of a legitimate company with the aim of obtaining confidential data of unsuspecting internet users. Recent researches indicate that a number of phishing detection algorithms have been introduced into the cyber space, however, most of them depend on an existing blacklist or whitelist classification. Therefore, when a new phishing web page is introduced, the detection algorithms find it difficult to correctly classify it as phishing. This paper puts forward a soft computing approach called Artificial Neural Network (ANN) algorithm with confusion matrix analysis for the detection of e-banking phishing websites. The proposed ANN algorithm produces a remarkable percentage of accuracy and reduced false positive rate during detection. This shows that, the ANN algorithm with confusion matrix analysis can produce competitive results that is suitable for detecting phishing in e-banking websites.

Computer-Based Local Area Authentication System

Volume 6 Issue 3 September - November 2018

Research Paper

Computer-Based Local Area Authentication System

O. S. Omorogiuwa*, G. O. Aziken**

*Professor,Department of Electrical and Electronic Engineering, University OF Benin, Benin City, Edo State, Nigeria.

**Professor, Department of Information and Communication Technology, University of Benin, Benin city, Edo State, Nigeria.

Omorogiuwa,O.S., Aziken,G.O.(2018)Computer-Based Local Area Authentication System, i-manager's Journal on Computer Science, 6(3),1-6. https://doi.org/10.26634/jcom.6.3.15695

Abstract

A Computer Based Test (CBT) Local Area Network (LAN) security center was developed using XAMPP (Cross-Platform Apache MariaDB PHP and Perl) integrated net-base application and JAVA object-oriented programming language, using a backend Oracle database (JBuilder and NetBeans) where the Media Access Control (MAC) address of the users will be saved and using hard to spoof measure that is correlated to location of LAN to prevent MAC address spoof. This security system is controlled through the network via the server and controls all clients that choose to use the resources like e-exam platform, e-library, etc. It also views the activities of the users in the network (surveillance system). The security system also keeps history/records of all detection sensor/ MAC address of users, to track any activities carried with the resources. The performance under test was found to be satisfactory, as all unauthorized users are blocked and appropriate warning messages are sent to the client's system by the server when the user attempts to login. This eliminates external users from gaining access to the examination platform.

Machine Learning Techniques for Effective Facilitation of Teaching and Learning: A Narrative Review

Volume 6 Issue 2 June - August 2018

Research Paper

Machine Learning Techniques for Effective Facilitation of Teaching and Learning: A Narrative Review

Anuraj Malav*, Neelu J. Ahuja**

* Junior Research Fellow, Department of Research and Development, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India.

** Professor and HOD, Department at School of Computer Science, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India.

Malav ,A.,& Ahuja, N.J. (2018). Machine Learning Techniques for Effective Facilitation of Teaching And Learning: a narrative Review. i-manager’s Journal on Computer Science, 6(2), 42-47. https://doi.org/10.26634/jcom.6.2.15032

Abstract

Traditional teaching learning has transformed significantly towards offering a learner an experience that to a greater extent mimics a human tutor; while in a computer-based or valued learning environment, Machine Learning (ML) techniques implemented as algorithms have played a significant role. This paper is a review of different interventions of machine learning in selected types of teaching learning systems, presented as a descriptive analysis, recommendations emergent from this analysis have been presented. Further the possibility of applicability of these systems for supporting learning of individual with disabilities, has been explored and evidentially advocated machine learning algorithms hold tremendous potential in terms of enriching the systems, facilitating the learning of individuals with special needs by providing versatility and adoptive learning experiences learning effectiveness, and this thought has been further extended to a recommendation for individuals with a disability, essentially with the deemed design alternatives.

Literature Survey on Automatic Code Generation Techniques

Volume 6 Issue 2 June - August 2018

Research Paper

Literature Survey on Automatic Code Generation Techniques

Dipti Pawade*, Avani Sakhapara**, Sanyogita Parab***, Divya Raikar****, Ruchita Bhojane*****, Henali Mamania******

*-**Assistant Professor, Department of Information Technology, K.J. Somaiya College of Engineering, Mumbai, Maharashtra, India.

***-****** Student, Department of Information Technology, K.J. Somaiya College of Engineering, Mumbai, Maharashtra, India.

Pawade, D., Sakhapara , A., Parab ,S., Raikar;D., Bhojane;R & Mamania;H. (2018). Literature Survey on Automatic Code Generation Techniques. i-manager’s Journal on Computer Science,6(2), 34-41. https://doi.org/10.26634/jcom.6.2.15005

Abstract

Automation is the mechanism to replace the human intervention in any process by the machine. Here the authors have considered the automation in the area of computer programming where researchers have tried to ease up the job of programmer by providing different tools and techniques to generate the programming code. The aim of this paper is to explore the research done in this area and give insight to the available automatic code generation methodologies for different types of input generating the code in different programming languages. Summary of all the available techniques has been presented in the paper.