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• Open Access   Article

  Discrete Wavelet Transform and Event-triggered Particle Swarm Optimization Approach for Infrared Image Enhancement

  G. Onuh, J.B. Akan, B. Muhammad, R.I. Nwosu

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.8 , Issue.4 , pp.83-89, Aug-2020

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  Infrared images suffer from low contrast and poor image quality mainly as a result of data collection and transmission. Conventional techniques used in infrared image enhancement have being reported with over enhancement and brightness distortion. This work proposes an infrared image enhancement technique based on discrete wavelet transform (DWT) and event-triggered particle swarm optimization (ETPSO). This technique will be implemented by first performing image preprocessing using Daubechies D4 filter, image transformation and enhancement based on discrete wavelet transform and finally brightness correction using event-triggered particle swarm optimization. The proposed algorithm was implemented on a dataset obtained from Dynamic Graphics Project laboratory database of infrared images and the output was put side by side with conventional approaches. A quantitative comparison shows that the proposed technique performs better with an average peak signal-to-noise ratio (PSNR) and discrete entropy (DE) values of 20.9 and 6.49 respectively

  Key-Words / Index Term
  Infrared Image Enhancement; Discrete Wavelet Transform; Particle Swarm Optimization

  References
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  Citation
  G. Onuh, J.B. Akan, B. Muhammad, R.I. Nwosu, "Discrete Wavelet Transform and Event-triggered Particle Swarm Optimization Approach for Infrared Image Enhancement," International Journal of Scientific Research in Computer Science and Engineering, Vol.8, Issue.4, pp.83-89, 2020

• Open Access   Article

  Users Perception of Social Media Opportunities and Challenges

  Cornelius Onimisi Adejoro, Francisca Nonyelum Ogwueleka

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.8 , Issue.4 , pp.90-96, Aug-2020

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  The far-reaching acceptance of social media platforms and several other information and communication technology innovations not only springs up new challenges for audience researchers but also provide thrilling opportunities to re-invigorate audience research as an academic topic as well as a practical endeavor. Though there?s been an extremely interesting record of growth in social media, however, there are intriguing part as well, when one carefully looks at the large amount of applications unlocked by these tools and their capabilities in influencing the health of population. This study looks at the perception of user of social media in Nigeria in terms of opportunities and challenges. The data that was analysed to draw conclusion for this study was collected from primary source using online questionnaires. SPSS was used to carry out the statistical analysis on seven hundred and forty-five (745) data set gotten from individuals that are mainly Nigerians

  Key-Words / Index Term
  Perception, Social Media, Opportunities, Challenges

  References
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  Citation
  Cornelius Onimisi Adejoro, Francisca Nonyelum Ogwueleka, "Users Perception of Social Media Opportunities and Challenges," International Journal of Scientific Research in Computer Science and Engineering, Vol.8, Issue.4, pp.90-96, 2020

• Open Access   Article

  Medical Image Encryption Scheme Based On Hybrid Chaotic Permutation

  J.B. Akan, E.A. Adedokun, G. Onuh, A. Umar, R.I. Nwosu, Y. Ibrahim

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.8 , Issue.4 , pp.97-104, Aug-2020

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  Abstract
  Securing patient?s information in form digital image from unauthorized modification is a crucial concern in modern day information system. With respect to this, a variant of chaos-based image encryption algorithm is proposed for securing medical images, catering for the challenges observed in the chaos-based image encryption algorithm. In the proposed improved algorithm a hybrid permutation approach based on bit-level and block scrambling is used for shuffling pixel after which, chaotic random data with pixel format and inserted on the plain-image surrounding. The plain image is divide into 8 variable sub blocks, then a 3D logistic map is used for generation of scrambling sequence. A combination of bit-level and block level pixel scrambling process was performed using the generated scrambling sequence, before image pixel diffusion process is performed. Simulation results showed an improve performance with respect to information entropy distribution of image in the adjacent direction which greatly reduces pixel correlation and strong redundancy, NPCR and UACI respectively. Thus, the proposed algorithm shows high robustness, sensitivity and resistance to chosen plaintext, known plaintext and chosen cipher text attack

  Key-Words / Index Term
  Medical Image Encryption, Chaotic Cryptography, Differential Attacks, Pseudorandom Key-stream, Pixel Scrambling

  References
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  Citation
  J.B. Akan, E.A. Adedokun, G. Onuh, A. Umar, R.I. Nwosu, Y. Ibrahim, "Medical Image Encryption Scheme Based On Hybrid Chaotic Permutation," International Journal of Scientific Research in Computer Science and Engineering, Vol.8, Issue.4, pp.97-104, 2020

• Open Access   Article

  Predicting Factors of Vehicle Traffic Accidents Using Machine Learning Algorithms: In the Case of Wolaita Zone

  Aklilu Elias Kurika, Tigist Simon Sundado

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.8 , Issue.4 , pp.105-115, Aug-2020

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  Vehicle traffic accident is the ultimate and major agenda for government in which special attention has been given to continuously reduce its occurrence and related risks. Wolaita zone is one of the major areas in which increased vehicle traffic accident occurs. Government and concerned bodies have given special attention to reduce accident rate in the country. By having this point as the motivating factor for study, this work tried to predict factors of vehicle accidents by using machine learning algorithms. We used unbalanced datasets with 1611 instances which was seven years data from 2005-2011 E.C. In order to analyze data and evaluate patters of datasets, KDD process model was applied. The learning algorithms applied for experiments were decision tree classifiers (J48, Random forest and Rep tree, Bayesian classifiers (Na?ve Bayes and Bayesian network). The experimental results, model evaluation and performance measurement shows that F-measure of J48 and Rep tree classifiers are comparatively similar i.e. 97.87% and 97.80% respectively and Random Forest tree performed less i.e. 90.9%. We identified the 1st experiment of J48 tree as the best model by performance and 23 best rules were generated from this experiment; best features were also identified. The most common victims, most commonly participated vehicles in accident and black spot areas for frequent accidents occurrences were identified. The findings of this study are significant for road and traffic authority and police commission for the revision and endorsement of the rules, regulations and standards related to traffic accidents; and therefore vehicle traffic accidents and related risks can be reduced generally in our country Ethiopia and specially at Wolaita Zone. We made accident data ready for further analysis in order to get most important patterns of datasets for any future researchers

  Key-Words / Index Term
  Vehicle traffic accident, Decision Tree, Bayesian Classifiers, Machine Learning Algorithms, Performance measurement

  References
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  Citation
  Aklilu Elias Kurika, Tigist Simon Sundado, "Predicting Factors of Vehicle Traffic Accidents Using Machine Learning Algorithms: In the Case of Wolaita Zone," International Journal of Scientific Research in Computer Science and Engineering, Vol.8, Issue.4, pp.105-115, 2020

• Open Access   Article

  Diabetic Retinopathy Detection Using Artificial Neural Network

  Rohini M., Gokila M., Nanthini N., Pavithra M., Ruth Lovelyn M.

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.8 , Issue.4 , pp.116-121, Aug-2020

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  Abstract
  Diabetic retinopathy is a one type of eye disease which affect the people with diabetics. Diabetics mellitus commonly referred to as diabetics is a group of metabolic disorder in which there a high blood sugar level over prolonged period. It creates eye disease if left untreated. It causes many complications and one such complications is blindness. The high blood sugar level causes damage to blood vessels in retina that many are not aware of. There are three main phases namely preprocessing, segmentation, classification. The proposed model is implemented in MATLAB and the results are analyzed based on certain parameters. Preprocessing is done by median filter to remove unwanted noise and preserve the edges. From the input image the blood vessels are isolated and used in identifying the presence of microneurysms and exhudates. The blood vessels of the image are detected using morphological operation. Detecting the disease at an earlier stage can prevent the patients from vision loss. In this work ANN approach is used for better classification of diabetics and the classified images can be categorized under the stages of various abnormalities associated with eye

  Key-Words / Index Term
  Artificial Neural Network, Segmentation, Feature Extraction

  References
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  Citation
  Rohini M., Gokila M., Nanthini N., Pavithra M., Ruth Lovelyn M., "Diabetic Retinopathy Detection Using Artificial Neural Network," International Journal of Scientific Research in Computer Science and Engineering, Vol.8, Issue.4, pp.116-121, 2020

• Open Access   Article

  Recognization of Online News Using Machine Learning

  Umme Habiba Maginmani, Mujamil Dakhani

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.8 , Issue.4 , pp.122-126, Aug-2020

  Abstract

  Full-Text HTML

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  Citation

  Abstract
  This Paper devise the employments of NPL (Natural Programming Language) strategies for recognizing the ?phony-news` that is beguiling reports which is begins from the non-real resource. Just by construction of a representation reliant upon a check-vectorization (utilizing statement counts) or a (Word Occurrence Inverse Text Frequency) WOITF grid, (statement checks comparative with how every now and again they are utilized in various editorial in our data-set) can simply obtain you up until this point. Regardless, these models we don`t consider the huge attributes similar to statement mentioning & setting. There might be probability that the 2 editorial which might be relative in their promise incorporate will be absolutely exceptional to their significance. An information science organize has reacted by acquiring exercises against this issue. There is contention named as? Kaggle ? which is also called as " Fake News Challenge" & Face-book is using Artificial Intelligence for looking at the false reports through the customers channels. Fighting the ?Fake-news? is a praiseworthy book request adventure with an unambiguous suggestion. It might be practical for us to develop a sculpt which can isolate among "genuine news" and "counterfeit news". So a projected effort on gathering of data-set equally for counterfeit & genuine news which use a ?Na?ve-Bayes classifier? to make a representation to orchestrate a piece of writing into counterfeit or genuine reliant on its words and articulations.

  Key-Words / Index Term
  Counterfeit news, NPL, Na?ve Bayes, Genuine news, WOITF grid, check- vectorization

  References
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  [5]Hadeer Ahmed, Issa Traore, and Sherif Saad.. ?Detection of Online Fake News Using N-Gram Analysis and Machine Learning Techniques?, in Intelligent, Secure, and Dependable Systems in Distributed and Cloud Environments, ser. Lecture Notes in Computer Science. Springer. pp. 127-138, 2017
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  Citation
  Umme Habiba Maginmani, Mujamil Dakhani, "Recognization of Online News Using Machine Learning," International Journal of Scientific Research in Computer Science and Engineering, Vol.8, Issue.4, pp.122-126, 2020

• Open Access   Article

  Ae-RF- An Enhanced Methodology for Just In Time Software Defect Detection

  Ruchika Malhotra, Bibi Aisha Nazari

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.8 , Issue.4 , pp.127-135, Aug-2020

  Abstract

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  Citation

  Abstract
  Just in time Software Defects Detection (JIT-SDD) is one of the interesting areas in predicting software faults at change level. Identifying just in time bugs or defects in the development process would not only ensure consistency and quality of the software, but also allows developers to test and address bugs quickly as well. Deep learning has acquired a prominent role in the literature on machine learning. It may be utilized to enhance the JIT-SDD?s performance. This paper suggests a methodology named Ae-RF (Auto-encoder ? Random Forest) which exploits deep learning algorithms to detect changes that are susceptible to defects. We first use the Autoencoder algorithm to extract a group of semantic features among various basic change features. We use six big public projects to probe our method (i.e., Mozilla, JDT, Bugzilla, Platform, PostgreSQL, and Columba) comprising a total amount of 137,417 changes. We use just one of the mentioned datasets (Platform) to construct and train the Autoencoder Model. Using this trained model, we can extract the relevant features of the other five datasets. Then we use the Random Forest algorithm for the classification phase. We selected the Area Under the Curve (AUC) as the main evaluation metric in this study. We gained the AUC values of 0.91 for Mozilla, 0.84 for JDT, and 0.71 for Bugzilla, 0.85 for Platform, 0.80 for PostgreSQL, and Columba with 0.78 AUC value.

  Key-Words / Index Term
  Just In Time Software Defect Detection, Deep Learning, Autoencoder, Random Forest

  References
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  Citation
  Ruchika Malhotra, Bibi Aisha Nazari, "Ae-RF- An Enhanced Methodology for Just In Time Software Defect Detection," International Journal of Scientific Research in Computer Science and Engineering, Vol.8, Issue.4, pp.127-135, 2020