Pattern Matching based Vehicle Density Estimation Technique for Traffic Monitoring Systems

Sakthidasan Sankaran

Department of Electronics and Communication Engineering,

Hindustan Institute of Technology and Science, India

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Abstract: Due to increase in vehicle density, the road traffic estimation aids in enhancing the traffic management centre’s performance and their applications. The analysis of traffic surveillance based on video is an active research area that has varied range of applications in Intelligent Transport System (ITS). In specific, urban environments are much more challenging on comparing highways due to the placement of cameras, vehicle pose, background clutter, or variation orientations. There were several techniques employed so far for the process of traffic monitoring using pattern matching, however there were some limitations like reduced rate of accuracy and increased error rate. So as to overcome this, an efficient method is proposed. The main intention of this proposed approach is to monitor the density of traffic and to estimate the vehicle density using Pattern Matching for Vehicle Density Estimation (PMVDE) scheme. In this paper, the pattern matching based vehicle density estimation is employed for enhancing the detection of accuracy thereby reducing the rate of error. The region of interest of an image that is extracted from the video input is being analysed by this process. These two processes are employed in region of interest extracted image for decreasing the density detection errors. This approach attains less false positive rates and error rate, however this in turn influences the accuracy, precision, recall, F-score, and true positive rates and offers enhanced outcome on comparing other techniques.

Keywords: Pattern matching, traffic estimation, vehicle density, region of interest, feature extraction.

Received August 25, 2020; accepted June 16, 2021

https://doi.org/10.34028/iajit/19/4/1

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XAPP: An Implementation of SAX-Based Method for Mapping XML Document to and from a Relational Database

Abstract: Extensible Markup Language (XML) is the standard medium for data exchange among businesses over the Internet, hence the need for effective management. However, since XML was not designed for storage and retrieval, its management has become an open research area in the database community. Existing mapping techniques for XML-to-relational database adopt either the structural mapping or the model mapping. Though numerous mapping approaches have been developed, mapping and reconstruction time had been problematic, especially when the document size is large and can hardly fit into main memory. In this research, an application codenamed XAPP, a new lightweight application that adopts a novel model mapping approach was developed using Simple API for XML (SAX) parser. XAPP accepts a document with or without Document Type Definition (DTD). It implements two algorithms: one maps XML data to a relational database and improves mapping time, and the other reconstructs an XML document from a relational database to improve reconstruction time and minimise memory usage. The performance of XAPP was analysed and compared with the Document Object Model (DOM) algorithm. XAPP proves to perform significantly better than the DOM-based algorithm in terms of mapping and reconstruction time, and memory efficiency. The correctness of XAPP was also verified.

Keywords: Extensible markup language, XML document, relational database, reconstruction time, mapping time.

Received July 16, 2020; accepted October 21, 2021

https://doi.org/10.34028/iajit/19/4/2

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Semantic Interoperability Model in Healthcare Internet of Things Using Healthcare Sign Description Framework

Abstract: The healthcare sector has experienced significant technological advances; however, interoperability is one of the biggest challenges. Interoperability in healthcare refers to the capacity to communicate across different healthcare environments. The format, language, syntax, and interpretation of data differ from one healthcare setting to another. Therefore, the lack of interoperability hampers effective communication and data exchange between two healthcare settings. Following the introduction of the Internet of Things (IoT) in healthcare, document-level interoperability is no longer the sole concern; device-level interoperability is also critical. This paper introduces a new Sign Description Framework for healthcare IoT called Healthcare Sign Description Framework (HSDF). Three different signs in healthcare, namely the Vital sign, Medication sign, and Symptom sign, are discussed here. Our proposal demonstrates how interoperability can be achieved using the novel healthcare sign description framework. Implementation of this framework will lead to improved diagnosis and increased cost-effectiveness of treatment.

Keywords: Semantic interoperability, healthcare IoT, healthcare sign description framework, ontology, unified medical language system.

Received July 8, 2021; accepted December 2, 2021

https://doi.org/10.34028/iajit/19/4/3

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Rating the Crisis of Online Public Opinion Using a Multi-Level Index System

Abstract: Online public opinion usually spreads rapidly and widely, thus a small incident probably evolves into a large social crisis in a very short time, and results in a heavy loss in credit or economic aspects. We propose a method to rate the crisis of online public opinion based on a multi-level index system to evaluate the impact of events objectively. Firstly, the dissemination mechanism of online public opinion is explained from the perspective of information ecology. According to the mechanism, some evaluation indexes are selected through correlation analysis and principal component analysis. Then, a classification model of text emotion is created via the training by deep learning to achieve the accurate quantification of the emotional indexes in the index system. Finally, based on the multi-level evaluation index system and grey correlation analysis, we propose a method to rate the crisis of online public opinion. The experiment with the real-time incident show that this method can objectively evaluate the emotional tendency of Internet users and rate the crisis in different dissemination stages of online public opinion. It is helpful to realizing the crisis warning of online public opinion and timely blocking the further spread of the crisis.

Keywords: Online public opinions, index system, emotional classification, crisis level.

Received September 15, 2020; accepted October 14, 2021

https://doi.org/10.34028/iajit/19/4/4

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MiNB: Minority Sensitive Naïve Bayesian Algorithm for Multi-Class Classification of Unbalanced Data

Abstract: The unbalanced nature of data makes it tough to achieve the desire performance goal for classification algorithms. The sub-optimal prediction system isn't a viable solution due to the high misclassification cost of minority events. Thus accurate imbalanced data classification could be a path changer for prediction in domains like medical diagnosis, judiciary, and disaster management systems. To date, most of the existing studies of imbalanced data are for the binary class dataset and supported by data sampling techniques that suffer from loss of information and over-fitting. In this paper, we present the modified naïve Bayesian algorithm for unbalanced data classification that eliminates the requirement of data level sampling. We compared our proposed model with the data sampling technique and cost-sensitive techniques. We use minority sensitive TP Rate, class-specific misclassification rate, and overall performance parameters such as accuracy, f-measure and G-mean. The result shows that our proposed algorithm shows a more optimal result for unbalanced data classification. Results shows reduction in misclassification rate and improve predictive performance for the minority class.

Keywords: Imbalanced data learning, weighted naïve bayesian, cost-sensitive learning, multi-class unbalanced data.

Received October 13, 2020; accepted December 13, 2021
https://doi.org/10.34028/iajit/19/4/5

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A Novel Approach of Clustering Documents: Minimizing Computational Complexities in Accessing Database Systems

Abstract: This study addresses the real-time issue of managing an academic program's documents in a university environment. In practice, document classification from a corpus is challenging when the dataset size is large, and the complexity increases if to meet some specific document management requirements. This study presents a practical approach to grouping documents based on a content similarity measure. The approach analyzes the state-of-the-art clustering algorithms performance, considers Hamiltonian graph properties and a distance function. The distance function measures (1) the content similarity between the documents and (2) the distances between the produced clusters. The proposed algorithm improves clusters’ quality by applying Hamiltonian graph properties. One of the significant characteristics of the proposed function is that it determines document types from the corpus. Hence, this does not require the initial assumption of cluster number before the algorithm execution. This approach omits the arbitrary primordial option of k-centroids of the k-means algorithm, reduces computational complexities, and overcomes some limitations of commonly practicing clustering algorithms. The proposed approach enables an effective way of document organization opportunities to the information systems developers when designing document management systems.

Keywords: Clustering algorithms, document categorization, document clustering, hamiltonian graph, similarity measure.

Received July 11, 2020; accepted February 21, 2021
https://doi.org/10.34028/iajit/19/4/6

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Person-Independent Emotion and Gender Prediction (EGP) System Using EEG Signals

Abstract: This paper presents a person-independent Emotion and Gender Prediction (EGP) system using Electroencephalography (EEG) brain signals. First, Short Time Fourier Transform (STFT) technique is implemented to get the time-frequency information for the selected electrode (Fz Electrode). Then, it is splitted into twenty sequential batches according to the electrode recorded time in seconds, and the maximum EEG activation voltage is located for every frequency level within each batch to create a 2D time-frequency extraction feature. Next, sparse auto encoder is applied to convert the distribution of the extracted feature into more compact and distinguished one instead. For system evaluation, Human-Computer Interaction) database (MAHNOB-HCI) public dataset with five-fold-cross validation classifier are used and implemented. In experiments, the proposed extracted feature improves the results of both emotion and gender prediction. For emotion prediction, the highest average accuracy is 97.07\%, 93.27% and 91.72\% for three, four and six emotions with Convolutional Neural Network (CNN) classifier, respectively. While, for gender prediction, experiments are tested related to neutral, amusement, happy, sad, and the mix of all these emotions, the highest average accuracy is obtained with CNN classifier in all emotion states (>95%) including the state of mixing all emotions together. As well as, the ability to distinguish between genders in case of mixing different emotions together is practically approved.

Keywords: Emotion, gender, EEG, brain signals, STFT.

Received October 17, 2020; accepted July 27, 2021

https://doi.org/10.34028/iajit/19/4/7

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Software Project Duration Estimation Based on COSMIC Method Applied to Data Flow Diagram

Abstract: Business process models are created before the detailed software design, during requirements phase of software development. Even disciplined agile methodology includes business process modeling, before development iterations start. Their use in an estimation of software development duration could be beneficial, because they provide sufficient elements for mapping with software design and development planning. In this paper we propose the method for software project duration estimation based on Common Software Measurement International Consortium (COSMIC) method, applied to data flow diagram. This method is based on data flow diagram analysis and extraction of primitive business procceses,data flows and data stores. The paper contributes with the approach to enhance COSMIC method with calculation of software development process duration based on both data movement-related and data-manipulation-related software functional sub-processes. Data movement-related functional sub-processes are derived from Create, Read, Update, Delete (CRUD) operations assigned to data flows. Calculation of data manipulation-related functional sub-processes duration is derived from number of business processes. The proposed metric enables calculating effort (expressed in Cosmic Functional Points units) and duration (presented with Man/Hours units). An example of the approach application and an empirical study demonstrates the applicability of the approach.

Keywords: Business process model, CRUD operations, software effort estimation, functional process, data movement, data manipulation.

Received September 1, 2020; accepted October 10, 2021
https://doi.org/10.34028/iajit/19/4/8

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An Improved Process Supervision and Control Method for Malware Detection

Abstract: Most modern-day malware detection methods and algorithms are based on prior knowledge of malware specifications. Discovering new malwares by solely relying on computer based automatic solutions with no human intervention currently appears out of reach. Many malwares never decode harmful parts of their code until the triggering of a specific event. Others detect virtual machine or sandbox environments and hide their true nature. Detecting these kinds of malwares-specifically multi evented ones-are nearly impossible for fully automatic detection methods. Previous research found that about 75% of malwares studied did not react in a fully automatic environment without user intervention thus being undetectable. This paper introduces a near automated solution to detect malwares quickly by relying on a supervision and control method based on user level capabilities of the operating system. Improving on previous methods, this research can replace the need for debugging new malwares in almost all aspects. This solution forces malwares in automated environments to activate and be discoverable. Researcher intervention during malware code execution along with the malware’s intent over calling sensitive operating system functions and parameters aid this process. Since operating system functions are virtualized malwares are incapable of physically harming the system during execution. The solution reached 98% overall accuracy in conjunction with reducing code size by 80% in comparison with similar techniques, improving simplicity and reliability.

Keywords: Mid-level code, malware detection, process supervision, microsoft windows, anti-virus, endpoint detection and response.

Received November 12, 2019; accepted February 9, 2021

https://doi.org/10.34028/iajit/19/4/9

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Deep Learning Shape Trajectories for Isolated Word Sign Language Recognition

Abstract: In this paper, we propose an efficient trajectories analysis solution for the recognition of Isolated Word Sign Language (IWSL). The key technique innovation in this work is the shape trajectories analysis based on the deep learning method and achieved impressive results on different IWSL data sets: German: Rheinisch Westfälische Technische Hochschule(RWTH): RWTH-Boston-50 and RWTH-Boston-104(95.83%), Signer-Independent Continuous Sign Language Recognition for Large Vocabulary Using Subunit Models (SIGNUM: 98.21%) and new Tunisian Sign Language database (TunSigns: 98%).

Keywords: Sign language, isolated word recognition, shape trajectory analysis, deep learning, RWTH-Boston dataset and SIGNUM corpora.

Received January 17, 2020; accepted February 9, 2021

https://doi.org/10.34028/iajit/19/4/10

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Off-Line Signature Confirmation based on Cluster Representations of Geometrical and Statistical Features through Vector Distance, Neural Network and Support Vector Machine Classifiers

Abstract: We exploited the geometrical and statistical properties of signature images for offline signature verification and identification in this paper, using signature clustering and classification based on extracted features. The Offline-SVR has been tested on the 2004 Ministerio de Ciencia Tecnología e Innovación (MCTYTDB) OffLineSignSubCorpus dataset, the MCYT-330 online signature dataset, and the MCE-200 dataset, which together are referred to as the MCE-605 dataset. Using a standard data set for experiments, the results of the Vector Distance (VD), Support Vector Machine (SVM) and Neural Network (NN) methods are significantly superior to those of other signature verification and recognition methods. Moreover, the VD method performed better than The SVM and NN methods. The purpose of the study is on clustering signature images using geometric and statistical features, as well as the utilization vector distance, neural networks, and support vector machines for signature image verification and identification. It was decided to use the algorithm for developing geometric and statistical features. The signature images are classified using generated features using k-means clustering, and Offline and Online- Support Vector Regression (SVR) is accomplished using VD, SVM, and NN training and classification with a different number of signatures each time, preceded by verification using recognition statistics. Because of the minimal number of features, the designed mechanism seems to be much faster. Experimenting on a standard dataset reveals that the results obtained from clustering signatures and categorization are effective and simple in comparison to other Offline signature confirmation systems. In this research work, we address the problem of representing handwritten signatures (online/offline) suitable for effective verification and recognition. We propose effective feature extraction for verification and recognition of signatures.

Keywords: Offline signature confirmation, k-means clustering, geometrical feature, statistical feature, vector distance, neural network, support vector machine.

Received July 14, 2020; accepted October 10, 2021

https://doi.org/10.34028/iajit/19/4/11

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A Simplified Alternate Approach to Estimate Software Size of Startups

Abstract: This paper proposes an alternate approach to startups to estimate the size of software product to be built by them using the Software Product Points (SPP). Dataset from 20 software projects of a startup company in India was used to validate the proposed approach and learn lessons out of it. The estimated software product points and the project efforts were found to have a strong positive correlation, thereby indicating the suitability of the proposed approach for its utility by the managers of future software projects of startups. We also briefly outline the implications for project managers of startups and scope for future research.

Keywords: Software size, software projects, quality function deployment, process improvement, optimization.

Received January 19, 2021; accepted September 5, 2021

https://doi.org/10.34028/iajit/19/4/12

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Arabic Quran Verses Authentication Using Deep Learning and Word Embeddings

Abstract: Nowadays, with the developments witnessed by the Internet, algorithms have come to control all aspects of digital content. Due to its Arabic roots, it is ironic to find that Arabic Quranic content is still thirsty to benefit from computer linguistics, especially with the advent of artificial intelligence algorithms. The massive spread of Islamic-typed websites and applications has led to a widespread of digital Quranic content. Unfortunately, such content lacks censorship and can rarely match resourcefulness. It is quite difficult, especially for a non-native speaker of the Arabic language, to distinguish and authenticate the provided Quranic verses from the non-Quranic Arabic texts. Text processing techniques classified outside the field of Natural Language Processing (NLP) give less qualified results, especially with Arabic texts. To address this problem, we propose to explore Word Embeddings (WE) with Deep Learning (DL) techniques to identify Quranic verses in Arabic textual content. The proposed work is evaluated using twelve different word embeddings models with two popular classifiers for binary classification, namely: Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM). The experimental results showed the superiority of the proposed approach over traditional methods in distinguishing between the Quranic verses and the Arabic text with an accuracy of 98.33%.

Keywords: Arabic text, Quranic verse, Authentication, NLP, Word Embeddings, Word2vec, DL, CNN, LSTM.

Received February 3, 2021; accepted October 10, 2021

https://doi.org/10.34028/iajit/19/4/13

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Solving Capacitated Vehicle Routing Problem Using Meerkat Clan Algorithm

Abstract: Capacitated Vehicle Routing Problem (CVRP) can be defined as one of the optimization problems where customers are allocated to vehicles to minimize the combined travel distances regarding all vehicles while serving customers. From the many CVRP approaches, clustering or grouping customers into possible individual vehicles' routes and identifying their optimal routes effectively. Sweep is considered a well-studied clustering algorithm to group customers, while various Traveling Salesman Problem (TSP) solving approaches are mainly applied to generate optimal individual vehicle routes. The Meerkat Clan Algorithm (MCA) can be defined as a swarm intelligence algorithm derived from careful observations regarding Meerkat (Suricata suricatta) in southern Africa's the Kalahari Desert. The animal demonstrates tactical organizational skills, excellent intelligence, and significant directional cleverness when searching for food in the desert. In comparison to the other swarm intelligence, MCA was suggested for solving optimization problems via reaching the optimal solution effects. MCA demonstrates its ability to resolve CVRP. It divides the solutions into subgroups based on meerkat behavior, providing a wide range of options for finding the best solution. Compared to present swarm intelligence algorithms for resolving CVRP, it was demonstrated that the size of the solved issues can be increased by using the algorithm suggested in this work.

Keywords: Capacitated vehicle routing problem, ant colony optimization, genetic algorithm, meerkat clan algorithm, sweep clustering.

Received June 29, 2021; accepted August 9, 2021

https://doi.org/10.34028/iajit/19/4/14

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Smoke Detection Algorithm based on Negative Sample Mining

Abstract: Forest fire is one of the most dangerous disasters that threaten the safety of human life and property. In order to detect fire in time, we detect the smoke when the fire breaks out. However, it is still a challenging task due to the variations of smoke in color, texture, shape and the disturbances of smoke-like objects. Therefore, the accuracy of smoke detection is not high, and it is accompanied by a high false positive rate, especially in the real environment. To tackle this problem, this paper proposes a novel model based on Faster Region-based Convolutional Network (R-CNN) which utilizes negative sample mining method. The proposed method allows the model to learn more negative sample features, thereby reducing false positives in smoke detection. The experiments are performed on self-created dataset containing 11958 images which are collected from cameras placed in villages or towns and existing datasets. Compared to other smoke datasets, the self-created dataset is larger and contains complex scenes. The proposed method achieves 94.59% accuracy, 94.35% precision and 5.76% false positive rate on self-created dataset. The results show that the proposed network is better and more robust than previous works.

Keywords: Smoke detection, negative sample mining, false positives, convolutional neural network.

Received August 15, 2020; accepted October 10, 2021

https://doi.org/10.34028/iajit/19/4/15

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