International Journal of Intelligent Systems and Applications (IJISA)

ISSN: 2074-904X (Print)

ISSN: 2074-9058 (Online)

DOI: https://doi.org/10.5815/ijisa

Website: https://www.mecs-press.org/ijisa

Published By: MECS Press

Frequency: 6 issues per year

Number(s) Available: 130

(IJISA) in Google Scholar Citations / h5-index

IJISA is committed to bridge the theory and practice of intelligent systems. From innovative ideas to specific algorithms and full system implementations, IJISA publishes original, peer-reviewed, and high quality articles in the areas of intelligent systems. IJISA is a well-indexed scholarly journal and is indispensable reading and references for people working at the cutting edge of intelligent systems and applications.

 

IJISA has been abstracted or indexed by several world class databases:  Scopus, Google Scholar, Microsoft Academic Search, CrossRef, Baidu Wenku, IndexCopernicus, IET Inspec, EBSCO, JournalSeek, ULRICH's Periodicals Directory, WorldCat, Scirus, Academic Journals Database, Stanford University Libraries, Cornell University Library, UniSA Library, CNKI Scholar, ProQuest, J-Gate, ZDB, BASE, OhioLINK, iThenticate, Open Access Articles, Open Science Directory, National Science Library of Chinese Academy of Sciences, The HKU Scholars Hub, etc..

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IJISA Vol. 16, No. 2, Apr. 2024

REGULAR PAPERS

Cost-effective Robotic Arm Simulation and System Verification

By Apostolos Tsagaris Charalampos Polychroniadis Anastasios Tzotzis Panagiotis Kyratsis

DOI: https://doi.org/10.5815/ijisa.2024.02.01, Pub. Date: 8 Apr. 2024

In recent years, the utilization of virtual environments in industry 4.0 has witnessed significant growth, particularly in the design, implementation, and management of robotic systems. This paper addresses the need for enhanced control in robotic arms by presenting the design and implementation of a 5DoF robotic arm transformed into a digital platform through specialized software. The methods employed involve detailed direct and inverse kinematic modeling to replicate the physical arm in a digital environment. Our measurements indicate an impressive accuracy ranging from 97% to 100% in the movements of the digital model, closely mirroring its physical counterpart. This research not only contributes to the development of simulation systems but also holds promise for the broader adoption of digital twins. The paper discusses the background, outlines the methodology, highlights key findings, and concludes with the potential future impact of this work on the advancement of robotic systems and simulation technologies.

[...] Read more.
A New Approach to Improving Search Efficiency in Digital Libraries

By Irada Alakbarova Dilbar Alizada

DOI: https://doi.org/10.5815/ijisa.2024.02.02, Pub. Date: 8 Apr. 2024

The development of Internet technologies influences the activities of libraries and changes their nature. The volume of content collected in digital libraries is growing rapidly. This requires the use of new technologies to search and obtain electronic materials (text, video, images, sound files) stored in the e-library. Today, using the new capabilities of network technologies and intelligent systems, the proper organization of the digital library, and increasing the efficiency of library services are the main factors leading to an increase in the number of readers and their satisfaction. The main objectives of digital libraries are to ensure efficient retrieval of electronic resources and collaboration between users. While researching various scientific articles on library and information sciences (LIS), we did not encounter approaches using cluster analysis in combination with wiki technologies. To collaborate users in digital libraries and their involvement in organizing electronic resources, we propose using an open database managed by wiki technologies. To effectively search for electronic resources in these open databases, it is proposed to use the clear clustering method. The clear clustering method also allows you to control the quality of clustering. The proposed method is important when creating intelligent (smart) libraries that are easy to manage and automate certain tasks. The research aims to create not just a smart library, but a smart library based on wiki technologies.

[...] Read more.
Machine Learning-based Approaches in Error Detection and Score Prediction for Small Arm Firing Systems in the Military Domain

By Salman Rahman Nusrat Sharmin Tanzil Ahmed

DOI: https://doi.org/10.5815/ijisa.2024.02.03, Pub. Date: 8 Apr. 2024

Error pattern recognition is a routine job in the military to provide corrective guidelines to the shooter. Errors can be recognized with a visual approach based on the spreading pattern of bullets on the target board, which are categorized into four categories: long horizontal error, long vertical error, bi-focal error, and scattered error. Currently, this process is performed manually and requires active human involvement. Similarly, an automated system to predict the future performance of a shooter is not available in the military domain. Moreover, the performance of a shooter depends on several factors, including age, weather, ammunition type, availability of light, previous scores, shooting range, classification of firing, and other factors. The military domain has not addressed the automatic prediction of such performance. While error correction and performance analysis have been extensively explored in the field of sports, their application within the military domain remains an untapped area of research and investigation. Numerous recent endeavors have suggested the utilization of deep learning to tackle this challenge. However, the absence of real-time data poses a significant obstacle, rendering these solutions seemingly impractical. In this paper, we have applied machine- learning approaches and adopted the best algorithm to automate the error pattern recognition system within a military domain. Our proposed methodology has two modules. The first module uses various algorithms and finds a random forest classifier that can do better to recognize the pattern of error and in the second phase, we used the AdaBoost classifier to predict the score and performance of a firer. Several experiments have been conducted, and the results show an average accuracy of 0.968 using Random Forest to recognize the pattern of error and an accuracy of 0.69 using AdaBoost to predict score performance. The data has been collected from the real-time environment of the military domain and experiments have been carried out using real-time scenarios with the military in mind.

[...] Read more.
Quantification of EEG Characteristics for Epileptic Seizure Detection and Monitoring of Anaesthesia Using Spectral Analysis

By Anand Ghuli Anil Kannur Abhishek Mali Aishwarya Mangasuli

DOI: https://doi.org/10.5815/ijisa.2024.02.04, Pub. Date: 8 Apr. 2024

Epilepsy is considered one of the primary neurological disorders, and its treatment requires abundant technological assistance. General Anaesthesia induces distinct patterns in brain activity, with the most common being a gradual increase in low-frequency signals as the level of Anaesthesia deepens. In this instance, a method of validating epileptic seizures and Anaesthesia through the utilization of electroencephalogram (EEG) data, acquired non-invasively, is introduced. Epileptic seizures and detection of the presence of Anaesthesia approaches make use of discrete Laplace Transformation (LT), Discrete Cosine Transformation (DCT), and Fast Fourier Transform (FFT). Here, it is discussed how power spectral analysis (PSA) helps study EEG characteristics in detecting epileptic behavior and the presence of Anaesthesia. A dataset of EEG (Epileptic and Anaesthesia), which is available publicly [1,2], has been used in the propounded technique using FIR filters and LT, DCT, and FFT are used to store and process 16 channel data. Power Spectrum Density (PSD) and its average were contrasted against a specific spectrum and frequency range of a typical EEG signal to obtain the results. This work uses a technique to determine whether the patient being studied is epileptic and awake or anesthetized.

[...] Read more.
Optimized Image Captioning: Hybrid Transformers Vision Transformers and Convolutional Neural Networks: Enhanced with Beam Search

By Sushma Jaiswal Harikumar Pallthadka Rajesh P. Chinchewadi Tarun Jaiswal

DOI: https://doi.org/10.5815/ijisa.2024.02.05, Pub. Date: 8 Apr. 2024

Deep learning has improved image captioning. Transformer, a neural network architecture built for natural language processing, excels at image captioning and other computer vision applications. This paper reviews Transformer-based image captioning methods in detail. Convolutional neural networks (CNNs) extracted image features and RNNs or LSTM networks generated captions in traditional image captioning. This method often has information bottlenecks and trouble capturing long-range dependencies. Transformer architecture revolutionized natural language processing with its attention strategy and parallel processing. Researchers used Transformers' language success to solve image captioning problems. Transformer-based image captioning systems outperform previous methods in accuracy and efficiency by integrating visual and textual information into a single model. This paper discusses how the Transformer architecture's self-attention mechanisms and positional encodings are adapted for image captioning. Vision Transformers (ViTs) and CNN-Transformer hybrid models are discussed. We also discuss pre-training, fine-tuning, and reinforcement learning to improve caption quality. Transformer-based image captioning difficulties, trends, and future approaches are also examined. Multimodal fusion, visual-text alignment, and caption interpretability are challenges. We expect research to address these issues and apply Transformer-based image captioning to medical imaging and distant sensing. This paper covers how Transformer-based approaches have changed image captioning and their potential to revolutionize multimodal interpretation and generation, advancing artificial intelligence and human-computer interactions.

[...] Read more.
Analysis of Cyberbullying Incidence among Filipina Victims: A Pattern Recognition using Association Rule Extraction

By Frederick F. Patacsil

DOI: https://doi.org/10.5815/ijisa.2019.11.05, Pub. Date: 8 Nov. 2019

Cyberbullying is an intentional action of harassment along the complex domain of social media utilizing information technology online. This research experimented unsupervised associative approach on text mining technique to automatically find cyberbullying words, patterns and extract association rules from a collection of tweets based on the domain / frequent words. Furthermore, this research identifies the relationship between cyberbullying keywords with other cyberbullying words, thus generating knowledge discovery of different cyberbullying word patterns from unstructured tweets. The study revealed that the type of dominant frequent cyberbullying words are intelligence, personality, and insulting words that describe the behavior, appearance of the female victims and sex related words that humiliate female victims. The results of the study suggest that we can utilize unsupervised associative approached in text mining to extract important information from unstructured text. Further, applying association rules can be helpful in recognizing the relationship and meaning between keywords with other words, therefore generating knowledge discovery of different datasets from unstructured text.

[...] Read more.
Blockchain with Internet of Things: Benefits, Challenges, and Future Directions

By Hany F. Atlam Ahmed Alenezi Madini O. Alassafi Gary B. Wills

DOI: https://doi.org/10.5815/ijisa.2018.06.05, Pub. Date: 8 Jun. 2018

The Internet of Things (IoT) has extended the internet connectivity to reach not just computers and humans, but most of our environment things. The IoT has the potential to connect billions of objects simultaneously which has the impact of improving information sharing needs that result in improving our life. Although the IoT benefits are unlimited, there are many challenges facing adopting the IoT in the real world due to its centralized server/client model. For instance, scalability and security issues that arise due to the excessive numbers of IoT objects in the network. The server/client model requires all devices to be connected and authenticated through the server, which creates a single point of failure. Therefore, moving the IoT system into the decentralized path may be the right decision. One of the popular decentralization systems is blockchain. The Blockchain is a powerful technology that decentralizes computation and management processes which can solve many of IoT issues, especially security. This paper provides an overview of the integration of the blockchain with the IoT with highlighting the integration benefits and challenges. The future research directions of blockchain with IoT are also discussed. We conclude that the combination of blockchain and IoT can provide a powerful approach which can significantly pave the way for new business models and distributed applications.

[...] Read more.
Data Mining of Students’ Performance: Turkish Students as a Case Study

By Oyebade Kayode Oyedotun Sam Nii Tackie Ebenezer Obaloluwa Olaniyi Khashman Adnan

DOI: https://doi.org/10.5815/ijisa.2015.09.03, Pub. Date: 8 Aug. 2015

Artificial neural networks have been used in different fields of artificial intelligence, and more specifically in machine learning. Although, other machine learning options are feasible in most situations, but the ease with which neural networks lend themselves to different problems which include pattern recognition, image compression, classification, computer vision, regression etc. has earned it a remarkable place in the machine learning field. This research exploits neural networks as a data mining tool in predicting the number of times a student repeats a course, considering some attributes relating to the course itself, the teacher, and the particular student. Neural networks were used in this work to map the relationship between some attributes related to students’ course assessment and the number of times a student will possibly repeat a course before he passes. It is the hope that the possibility to predict students’ performance from such complex relationships can help facilitate the fine-tuning of academic systems and policies implemented in learning environments. To validate the power of neural networks in data mining, Turkish students’ performance database has been used; feedforward and radial basis function networks were trained for this task. The performances obtained from these networks were evaluated in consideration of achieved recognition rates and training time.

[...] Read more.
Predicting Stock Market Behavior using Data Mining Technique and News Sentiment Analysis

By Ayman E. Khedr S.E.Salama Nagwa Yaseen

DOI: https://doi.org/10.5815/ijisa.2017.07.03, Pub. Date: 8 Jul. 2017

Stock market prediction has become an attractive investigation topic due to its important role in economy and beneficial offers. There is an imminent need to uncover the stock market future behavior in order to avoid investment risks. The large amount of data generated by the stock market is considered a treasure of knowledge for investors. This study aims at constructing an effective model to predict stock market future trends with small error ratio and improve the accuracy of prediction. This prediction model is based on sentiment analysis of financial news and historical stock market prices. This model provides better accuracy results than all previous studies by considering multiple types of news related to market and company with historical stock prices. A dataset containing stock prices from three companies is used. The first step is to analyze news sentiment to get the text polarity using naïve Bayes algorithm. This step achieved prediction accuracy results ranging from 72.73% to 86.21%. The second step combines news polarities and historical stock prices together to predict future stock prices. This improved the prediction accuracy up to 89.80%.

[...] Read more.
Machine Learning for Weather Forecasting: XGBoost vs SVM vs Random Forest in Predicting Temperature for Visakhapatnam

By Deep Karan Singh Nisha Rawat

DOI: https://doi.org/10.5815/ijisa.2023.05.05, Pub. Date: 8 Oct. 2023

Climate change, a significant and lasting alteration in global weather patterns, is profoundly impacting the stability and predictability of global temperature regimes. As the world continues to grapple with the far-reaching effects of climate change, accurate and timely temperature predictions have become pivotal to various sectors, including agriculture, energy, public health and many more. Crucially, precise temperature forecasting assists in developing effective climate change mitigation and adaptation strategies. With the advent of machine learning techniques, we now have powerful tools that can learn from vast climatic datasets and provide improved predictive performance. This study delves into the comparison of three such advanced machine learning models—XGBoost, Support Vector Machine (SVM), and Random Forest—in predicting daily maximum and minimum temperatures using a 45-year dataset of Visakhapatnam airport. Each model was rigorously trained and evaluated based on key performance metrics including training loss, Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R2 score, Mean Absolute Percentage Error (MAPE), and Explained Variance Score. Although there was no clear dominance of a single model across all metrics, SVM and Random Forest showed slightly superior performance on several measures. These findings not only highlight the potential of machine learning techniques in enhancing the accuracy of temperature forecasting but also stress the importance of selecting an appropriate model and performance metrics aligned with the requirements of the task at hand. This research accomplishes a thorough comparative analysis, conducts a rigorous evaluation of the models, highlights the significance of model selection.

[...] Read more.
Machine Learning in Cyberbullying Detection from Social-Media Image or Screenshot with Optical Character Recognition

By Tofayet Sultan Nusrat Jahan Ritu Basak Mohammed Shaheen Alam Jony Rashidul Hasan Nabil

DOI: https://doi.org/10.5815/ijisa.2023.02.01, Pub. Date: 8 Apr. 2023

Along with the growth of the Internet, social media usage has drastically expanded. As people share their opinions and ideas more frequently on the Internet and through various social media platforms, there has been a notable rise in the number of consumer phrases that contain sentiment data. According to reports, cyberbullying frequently leads to severe emotional and physical suffering, especially in women and young children. In certain instances, it has even been reported that sufferers attempt suicide. The bully may occasionally attempt to destroy any proof they believe to be on their side. Even if the victim gets the evidence, it will still be a long time before they get justice at that point. This work used OCR, NLP, and machine learning to detect cyberbullying in photos in order to design and execute a practical method to recognize cyberbullying from images. Eight classifier techniques are used to compare the accuracy of these algorithms against the BoW Model and the TF-IDF, two key features. These classifiers are used to understand and recognize bullying behaviors. Based on testing the suggested method on the cyberbullying dataset, it was shown that linear SVC after OCR and logistic regression perform better and achieve the best accuracy of 96 percent. This study aid in providing a good outline that shapes the methods for detecting online bullying from a screenshot with design and implementation details.

[...] Read more.
Sentiment Analysis: A Perspective on its Past, Present and Future

By Akshi Kumar Teeja Mary Sebastian

DOI: https://doi.org/10.5815/ijisa.2012.10.01, Pub. Date: 8 Sep. 2012

The proliferation of Web-enabled devices, including desktops, laptops, tablets, and mobile phones, enables people to communicate, participate and collaborate with each other in various Web communities, viz., forums, social networks, blogs. Simultaneously, the enormous amount of heterogeneous data that is generated by the users of these communities, offers an unprecedented opportunity to create and employ theories & technologies that search and retrieve relevant data from the huge quantity of information available and mine for opinions thereafter. Consequently, Sentiment Analysis which automatically extracts and analyses the subjectivities and sentiments (or polarities) in written text has emerged as an active area of research. This paper previews and reviews the substantial research on the subject of sentiment analysis, expounding its basic terminology, tasks and granularity levels. It further gives an overview of the state- of – art depicting some previous attempts to study sentiment analysis. Its practical and potential applications are also discussed, followed by the issues and challenges that will keep the field dynamic and lively for years to come.

[...] Read more.
Non-Functional Requirements Classification Using Machine Learning Algorithms

By Abdur Rahman Abu Nayem Saeed Siddik

DOI: https://doi.org/10.5815/ijisa.2023.03.05, Pub. Date: 8 Jun. 2023

Non-functional requirements define the quality attribute of a software application, which are necessary to identify in the early stage of software development life cycle. Researchers proposed automatic software Non-functional requirement classification using several Machine Learning (ML) algorithms with a combination of various vectorization techniques. However, using the best combination in Non-functional requirement classification still needs to be clarified. In this paper, we examined whether different combinations of feature extraction techniques and ML algorithms varied in the non-functional requirements classification performance. We also reported the best approach for classifying Non-functional requirements. We conducted the comparative analysis on a publicly available PROMISE_exp dataset containing labelled functional and Non-functional requirements. Initially, we normalized the textual requirements from the dataset; then extracted features through Bag of Words (BoW), Term Frequency and Inverse Document Frequency (TF-IDF), Hashing and Chi-Squared vectorization methods. Finally, we executed the 15 most popular ML algorithms to classify the requirements. The novelty of this work is the empirical analysis to find out the best combination of ML classifier with appropriate vectorization technique, which helps developers to detect Non-functional requirements early and take precise steps. We found that the linear support vector classifier and TF-IDF combination outperform any combinations with an F1-score of 81.5%.

[...] Read more.
Heart Diseases Diagnosis Using Neural Networks Arbitration

By Ebenezer Obaloluwa Olaniyi Oyebade Kayode Oyedotun Khashman Adnan

DOI: https://doi.org/10.5815/ijisa.2015.12.08, Pub. Date: 8 Nov. 2015

There is an increase in death rate yearly as a result of heart diseases. One of the major factors that cause this increase is misdiagnoses on the part of medical doctors or ignorance on the part of the patient. Heart diseases can be described as any kind of disorder that affects the heart. In this research work, causes of heart diseases, the complications and the remedies for the diseases have been considered. An intelligent system which can diagnose heart diseases has been implemented. This system will prevent misdiagnosis which is the major error that may occur by medical doctors. The dataset of statlog heart disease has been used to carry out this experiment. The dataset comprises attributes of patients diagnosed for heart diseases. The diagnosis was used to confirm whether heart disease is present or absent in the patient. The datasets were obtained from the UCI Machine Learning. This dataset was divided into training, validation set and testing set, to be fed into the network. The intelligent system was modeled on feed forward multilayer perceptron, and support vector machine. The recognition rate obtained from these models were later compared to ascertain the best model for the intelligent system due to its significance in medical field. The results obtained are 85%, 87.5% for feedforward multilayer perceptron, and support vector machine respectively. From this experiment we discovered that support vector machine is the best network for the diagnosis of heart disease.

[...] Read more.
The Combined Use of the Wiener Polynomial and SVM for Material Classification Task in Medical Implants Production

By Ivan Izonin Andriy Trostianchyn Zoia Duriagina Roman Tkachenko Tetiana Tepla Nataliia Lotoshynska

DOI: https://doi.org/10.5815/ijisa.2018.09.05, Pub. Date: 8 Sep. 2018

This document presents two developed methods for solving the classification task of medical implant materials based on the compatible use of the Wiener Polynomial and SVM. The high accuracy of the proposed methodology for solving this task are experimentally confirmed. A comparison of the proposed methods with existing ones: Logistic Regression; Linear SVC; Random Forest; SVC (linear kernel); SVC (RBF kernel); Random Forest + Wiener Polynomial is carried out. The duration of training of all methods that described in work is investigated. The article presents the visualization of all method results for solving this task.

[...] Read more.
Analysis of Cyberbullying Incidence among Filipina Victims: A Pattern Recognition using Association Rule Extraction

By Frederick F. Patacsil

DOI: https://doi.org/10.5815/ijisa.2019.11.05, Pub. Date: 8 Nov. 2019

Cyberbullying is an intentional action of harassment along the complex domain of social media utilizing information technology online. This research experimented unsupervised associative approach on text mining technique to automatically find cyberbullying words, patterns and extract association rules from a collection of tweets based on the domain / frequent words. Furthermore, this research identifies the relationship between cyberbullying keywords with other cyberbullying words, thus generating knowledge discovery of different cyberbullying word patterns from unstructured tweets. The study revealed that the type of dominant frequent cyberbullying words are intelligence, personality, and insulting words that describe the behavior, appearance of the female victims and sex related words that humiliate female victims. The results of the study suggest that we can utilize unsupervised associative approached in text mining to extract important information from unstructured text. Further, applying association rules can be helpful in recognizing the relationship and meaning between keywords with other words, therefore generating knowledge discovery of different datasets from unstructured text.

[...] Read more.
Data Mining of Students’ Performance: Turkish Students as a Case Study

By Oyebade Kayode Oyedotun Sam Nii Tackie Ebenezer Obaloluwa Olaniyi Khashman Adnan

DOI: https://doi.org/10.5815/ijisa.2015.09.03, Pub. Date: 8 Aug. 2015

Artificial neural networks have been used in different fields of artificial intelligence, and more specifically in machine learning. Although, other machine learning options are feasible in most situations, but the ease with which neural networks lend themselves to different problems which include pattern recognition, image compression, classification, computer vision, regression etc. has earned it a remarkable place in the machine learning field. This research exploits neural networks as a data mining tool in predicting the number of times a student repeats a course, considering some attributes relating to the course itself, the teacher, and the particular student. Neural networks were used in this work to map the relationship between some attributes related to students’ course assessment and the number of times a student will possibly repeat a course before he passes. It is the hope that the possibility to predict students’ performance from such complex relationships can help facilitate the fine-tuning of academic systems and policies implemented in learning environments. To validate the power of neural networks in data mining, Turkish students’ performance database has been used; feedforward and radial basis function networks were trained for this task. The performances obtained from these networks were evaluated in consideration of achieved recognition rates and training time.

[...] Read more.
Non-Functional Requirements Classification Using Machine Learning Algorithms

By Abdur Rahman Abu Nayem Saeed Siddik

DOI: https://doi.org/10.5815/ijisa.2023.03.05, Pub. Date: 8 Jun. 2023

Non-functional requirements define the quality attribute of a software application, which are necessary to identify in the early stage of software development life cycle. Researchers proposed automatic software Non-functional requirement classification using several Machine Learning (ML) algorithms with a combination of various vectorization techniques. However, using the best combination in Non-functional requirement classification still needs to be clarified. In this paper, we examined whether different combinations of feature extraction techniques and ML algorithms varied in the non-functional requirements classification performance. We also reported the best approach for classifying Non-functional requirements. We conducted the comparative analysis on a publicly available PROMISE_exp dataset containing labelled functional and Non-functional requirements. Initially, we normalized the textual requirements from the dataset; then extracted features through Bag of Words (BoW), Term Frequency and Inverse Document Frequency (TF-IDF), Hashing and Chi-Squared vectorization methods. Finally, we executed the 15 most popular ML algorithms to classify the requirements. The novelty of this work is the empirical analysis to find out the best combination of ML classifier with appropriate vectorization technique, which helps developers to detect Non-functional requirements early and take precise steps. We found that the linear support vector classifier and TF-IDF combination outperform any combinations with an F1-score of 81.5%.

[...] Read more.
Graph Coloring in University Timetable Scheduling

By Swapnil Biswas Syeda Ajbina Nusrat Nusrat Sharmin Mahbubur Rahman

DOI: https://doi.org/10.5815/ijisa.2023.03.02, Pub. Date: 8 Jun. 2023

Addressing scheduling problems with the best graph coloring algorithm has always been very challenging. However, the university timetable scheduling problem can be formulated as a graph coloring problem where courses are represented as vertices and the presence of common students or teachers of the corresponding courses can be represented as edges. After that, the problem stands to color the vertices with lowest possible colors. In order to accomplish this task, the paper presents a comparative study of the use of graph coloring in university timetable scheduling, where five graph coloring algorithms were used: First Fit, Welsh Powell, Largest Degree Ordering, Incidence Degree Ordering, and DSATUR. We have taken the Military Institute of Science and Technology, Bangladesh as a test case. The results show that the Welsh-Powell algorithm and the DSATUR algorithm are the most effective in generating optimal schedules. The study also provides insights into the limitations and advantages of using graph coloring in timetable scheduling and suggests directions for future research with the use of these algorithms.

[...] Read more.
A Conic Radon-based Convolutional Neural Network for Image Recognition

By Dhekra El Hamdi Ines Elouedi Mai K Nguyen Atef Hamouda

DOI: https://doi.org/10.5815/ijisa.2023.01.01, Pub. Date: 8 Feb. 2023

This article presents a new approach for image recognition that proposes to combine Conical Radon Transform (CRT) and Convolutional Neural Networks (CNN).
In order to evaluate the performance of this approach for pattern recognition task, we have built a Radon descriptor enhancing features extracted by linear, circular and parabolic RT. The main idea consists in exploring the use of Conic Radon transform to define a robust image descriptor. Specifically, the Radon transformation is initially applied on the image. Afterwards, the extracted features are combined with image and then entered as an input into the convolutional layers. Experimental evaluation demonstrates that our descriptor which joins together extraction of features of different shapes and the convolutional neural networks achieves satisfactory results for describing images on public available datasets such as, ETH80, and FLAVIA. Our proposed approach recognizes objects with an accuracy of 96 % when tested on the ETH80 dataset. It also has yielded competitive accuracy than state-of-the-art methods when tested on the FLAVIA dataset with accuracy of 98 %. We also carried out experiments on traffic signs dataset GTSBR. We investigate in this work the use of simple CNN models to focus on the utility of our descriptor. We propose a new lightweight network for traffic signs that does not require a large number of parameters. The objective of this work is to achieve optimal results in terms of accuracy and to reduce network parameters. This approach could be adopted in real time applications. It classified traffic signs with high accuracy of 99%.

[...] Read more.
Static Timing Analysis of Different SRAM Controllers

By Jabin Sultana S. M. Shamsul Alam

DOI: https://doi.org/10.5815/ijisa.2023.03.03, Pub. Date: 8 Jun. 2023

Timing-critical path analysis is one of the most significant terms for the VLSI designer. For the formal verification of any kinds of digital chip, static timing analysis (STA) plays a vital role to check the potentiality and viability of the design procedures. This indicates the timing status between setup and holding times required with respect to the active edge of the clock. STA can also be used to identify time sensitive paths, simulate path delays, and assess Register transfer level (RTL) dependability. Four types of Static Random Access Memory (SRAM) controllers in this paper are used to handle with the complexities of digital circuit timing analysis at the logic level. Different STA parameters such as slack, clock skew, data latency, and multiple clock frequencies are investigated here in their node-to-node path analysis for diverse SRAM controllers. Using phase lock loop (ALTPLL), single clock and dual clock are used to get the response of these controllers. For four SRAM controllers, the timing analysis shows that no data violation exists for single and dual clock with 50 MHz and 100 MHz frequencies. Result also shows that the slack for 100MHz is greater than that of 50MHz. Moreover, the clock skew value in our proposed design is lower than in the other three controllers because number of paths, number of states are reduced, and the slack value is higher than in 1st and 2nd controllers. In timing path analysis, slack time determines that the design is working at the desired frequency. Although 100MHz is faster than 50MHz, our proposed SRAM controller meets the timing requirements for 100MHz including the reduction of node to node data delay. Due to this reason, the proposed controller performs well compared to others in terms slack and clock skew.

[...] Read more.
Predicting Stock Market Behavior using Data Mining Technique and News Sentiment Analysis

By Ayman E. Khedr S.E.Salama Nagwa Yaseen

DOI: https://doi.org/10.5815/ijisa.2017.07.03, Pub. Date: 8 Jul. 2017

Stock market prediction has become an attractive investigation topic due to its important role in economy and beneficial offers. There is an imminent need to uncover the stock market future behavior in order to avoid investment risks. The large amount of data generated by the stock market is considered a treasure of knowledge for investors. This study aims at constructing an effective model to predict stock market future trends with small error ratio and improve the accuracy of prediction. This prediction model is based on sentiment analysis of financial news and historical stock market prices. This model provides better accuracy results than all previous studies by considering multiple types of news related to market and company with historical stock prices. A dataset containing stock prices from three companies is used. The first step is to analyze news sentiment to get the text polarity using naïve Bayes algorithm. This step achieved prediction accuracy results ranging from 72.73% to 86.21%. The second step combines news polarities and historical stock prices together to predict future stock prices. This improved the prediction accuracy up to 89.80%.

[...] Read more.
Machine Learning in Cyberbullying Detection from Social-Media Image or Screenshot with Optical Character Recognition

By Tofayet Sultan Nusrat Jahan Ritu Basak Mohammed Shaheen Alam Jony Rashidul Hasan Nabil

DOI: https://doi.org/10.5815/ijisa.2023.02.01, Pub. Date: 8 Apr. 2023

Along with the growth of the Internet, social media usage has drastically expanded. As people share their opinions and ideas more frequently on the Internet and through various social media platforms, there has been a notable rise in the number of consumer phrases that contain sentiment data. According to reports, cyberbullying frequently leads to severe emotional and physical suffering, especially in women and young children. In certain instances, it has even been reported that sufferers attempt suicide. The bully may occasionally attempt to destroy any proof they believe to be on their side. Even if the victim gets the evidence, it will still be a long time before they get justice at that point. This work used OCR, NLP, and machine learning to detect cyberbullying in photos in order to design and execute a practical method to recognize cyberbullying from images. Eight classifier techniques are used to compare the accuracy of these algorithms against the BoW Model and the TF-IDF, two key features. These classifiers are used to understand and recognize bullying behaviors. Based on testing the suggested method on the cyberbullying dataset, it was shown that linear SVC after OCR and logistic regression perform better and achieve the best accuracy of 96 percent. This study aid in providing a good outline that shapes the methods for detecting online bullying from a screenshot with design and implementation details.

[...] Read more.
Classification of Images of Skin Lesion Using Deep Learning

By Momina Shaheen Usman Saif Shahid M. Awan Faizan Ahmad Aimen Anum

DOI: https://doi.org/10.5815/ijisa.2023.02.03, Pub. Date: 8 Apr. 2023

Skin cancer is among common and rapidly increasing human malignancies, which can be diagnosed visually. The diagnosis begins with preliminary medical screening and by dermoscopic examination, histopathological examination, and proceeding to the biopsy. This screening and diagnosis can be automated using machine learning tools and techniques. Artificial neural networks are helping a lot in medical diagnosis applications. In this research, skin images are classified into 7 different classes of skin cancer using deep learning methodology, then analyzed the results w.r.t to their respective precision, recall, support, and accuracy to find its practical applicability. This model is efficient in comparison to the detection of skin cancer with human eyes. Human eyes detection can be 79% accurate at most. Thus, having a scientific method of diagnosis can help the doctors and practitioners to accurately identify the cancer and its type. The model provides 80% accuracy on average for all 7 types of skin diseases, thus being more reliable than human eye examination. It will help the doctors to diagnose the skin diseases more confidently. The model has only 2 misclassified predictions for Basal cell carcinoma and Vascular lesions. However, Actinic keratosis diagnosis is most accurately predicted.

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Indeterminacy Handling of Adaptive Neuro-fuzzy Inference System Using Neutrosophic Set Theory: A Case Study for the Classification of Diabetes Mellitus

By Rajan Prasad Praveen Kumar Shukla

DOI: https://doi.org/10.5815/ijisa.2023.03.01, Pub. Date: 8 Jun. 2023

Early diabetes diagnosis allows patients to begin treatment on time, reducing or eliminating the risk of serious consequences. In this paper, we propose the Neutrosophic-Adaptive Neuro-Fuzzy Inference System (N-ANFIS) for the classification of diabetes. It is an extension of the generic ANFIS model. Neutrosophic logic is capable of handling the uncertain and imprecise information of the traditional fuzzy set. The suggested method begins with the conversion of crisp values to neutrosophic sets using a trapezoidal and triangular neutrosophic membership function. These values are fed into an inferential system, which compares the most impacted value to a diagnosis. The result demonstrates that the suggested model has successfully dealt with vague information. For practical implementation, a single-value neutrosophic number has been used; it is a special case of the neutrosophic set. To highlight the promising potential of the suggested technique, an experimental investigation of the well-known Pima Indian diabetes dataset is presented. The results of our trials show that the proposed technique attained a high degree of accuracy and produced a generic model capable of effectively classifying previously unknown data. It can also surpass some of the most advanced classification algorithms based on machine learning and fuzzy systems.

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