Iraqi Journal for Computers and Informatics
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SURVEY: AUDIO READING SYSTEM FOR BLIND PERSONS
Get PDFAudio Reading System is used to help blind people to read the text based on camera as input device and speaker as output device. The system used the OCR algorithm to extract the text from input image and Text-to-Speech algorithm to convert text into corresponding voice. In this paper, we review newest research of audio reading system. We discuss the hardware and software, which is used, on system for different types approach. Finally, the result of this paper that is: Raspberry pi, python and tesseract are best tools used in Audio reading system. Also the braille and finger print devices are not efficient and not easy to use
Big Data Analytics: A Survey
Get PDFInternet-based programs and communication techniques have become widely used and respected in the IT industry recently. A persistent source of "big data," or data that is enormous in volume, diverse in type, and has a complicated multidimensional structure, is internet applications and communications. Today, several measures are routinely performed with no assurance that any of them will be helpful in understanding the phenomenon of interest in an era of automatic, large-scale data collection. Online transactions that involve buying, selling, or even investing are all examples of e-commerce. As a result, they generate data that has a complex structure and a high dimension. The usual data storage techniques cannot handle those enormous volumes of data. There is a lot of work being done to find ways to minimize the dimensionality of big data in order to provide analytics reports that are even more accurate and data visualizations that are more interesting. As a result, the purpose of this survey study is to give an overview of big data analytics along with related problems and issues that go beyond technology
CLASSIFICATION BASED ON SEMI-SUPERVISED LEARNING: A REVIEW
Get PDFSemi-supervised learning is the class of machine learning that deals with the use of supervised and unsupervised learning to implement the learning process. Conceptually placed between labelled and unlabeled data. In certain cases, it enables the large numbers of unlabeled data required to be utilized in comparison with usually limited collections of labeled data. In standard classification methods in machine learning, only a labeled collection is used to train the classifier. In addition, labelled instances are difficult to acquire since they necessitate the assistance of annotators, who serve in an occupation that is identified by their label. A complete audit without a supervisor is fairly easy to do, but nevertheless represents a significant risk to the enterprise, as there have been few chances to safely experiment with it so far. By utilizing a large number of unsupervised inputs along with the supervised inputs, the semi-supervised learning solves this issue, to create a good training sample. Since semi-supervised learning requires fewer human effort and allows greater precision, both theoretically or in practice, it is of critical interest
DETECTION OF PNEUMONIA BY USING NINE PRE-TRAINED TRANSFER LEARNING MODELS BASED ON DEEP LEARNING TECHNIQUES
Get PDFPneumonia is a serious chest disease that affects the lungs. This disease has become an important issue that must be taken care of in the field of medicine due to its rapid and intense spread, especially among people who are addicted to smoking. This paper presents an efficient prediction system for detecting pneumonia using nine pre-trained transfer learning models based on deep learning technique (Inception v4, SeNet-154, Xception, PolyNet, ResNet-50, DenseNet-121, DenseNet-169, AlexNet, and SqueezeNet). The dataset in this study consisted of 5856 chest x-rays, which were divided into 5216 for training and 624 for the test. In the training phase, the images were pre-processed by resizing the input images to the same dimensions to reduce complexity and computation. The images are then forwarded to the proposed models (Inception v4, SeNet-154, Xception, PolyNet, ResNet-50, DenseNet-121, DenseNet-169, AlexNet, SqueezeNet) to extract features and classify the images as normal or pneumonia. The results of the proposed models (Inception v4, SeNet-154, Xception, PolyNet, ResNet-50, DenseNet-121 DenseNet-169, AlexNet and SqueezeNet) give accuracies (98.72%, 98.94%, 98.88%, 98.72%, 96.2%, 94.69%, 96.29%, 95.01% and 96.10%) respectively. We found that the SeNet-154 model gave the best result with an accuracy of 98.94% with a validation loss (0.018103). When comparing our results with older studies, it should be noted that the proposed method is superior to other methods
A WEARABLE MEDICAL MONITORING AND ALERT SYSTEM OF COVID-19 PATIENTS
Get PDFCurrently, Corona-virus disease (COVID-19), one of the most infectious diseases in the 21st century, is a highly contagious viral infection with a severe impact on global health. It also affected the global economy very badly. This virus threatens human’s life, and it is necessary to design a monitoring device to monitor the patient’s health remotely to avoid the spread of infection to doctors or nurses. In this paper, a wearable device which contains two sensors are used to measure blood oxygenation, body temperature, and heart rate. Then send these readings to the server to analyze them and send warning notifications to the patient’s assistant phone to inform him of whether the oxygen level is low, the heart rate is irregular or the patient\u27s temperature is high to perform a certain procedure to aid the patient
PERFORMANCE EVALUATION OF INFORMATION RETRIEVAL SYSTEM USING VECTOR SPACE MODEL: A COMPARATIVE ANALYSIS
Get PDFThe increasing use of the internet has created a vast amount of digital information and it is expanding extremely fast. Therefore, Information retrieval becomes a challenging task to fetch relevant information for users. The aim of this paper was to examine and evaluate the performance of the Information retrieval system through eight experiments to test all the features that can be used in a vector space model. These experiments were compared to show the best and the worst implemented features. The features are represented by applying (tf.idf, stop words, stemming), (tf.idf, No- stop words, stemming), (tf.idf, No- stop words, No-stemming), (tf.idf, stop words, No-stemming), (tf, stop words, stemming), (tf, No- stop words, stemming), (tf, No- stop words, No-stemming), (tf, stop words, No-stemming). Results showed that using stop words, stemming approach, and tf.idf improve the performance of the system. However, when tf was used without using stop words and stemming approaches the performance of the system is declined. In addition, results showed that stop words have a significant effect on the system while the stemming approach has no noticeable effect particularly with tf
Computational Intelligence-based Evaluation of a 3-DOF Robotic-arm Forward Kinematics
Get PDFRobotic manipulator- forward Kinematics involves the assurance of end-effector arrangements from connecting joint boundaries. The traditional mathematical calculation of controller forward -Kinematics is monotonous and tedious. Accordingly, it is important to execute a strategy that precisely performs forward energy while wiping out the disadvantages of the mathematical calculation technique. Versatile Neuro-Fuzzy Inference System (ANFIS) is a computational knowledge strategy that has been effectively executed for expectation purposes in assorted logical orders. This present examination\u27s essential goal was to evaluate the productivity of ANFIS in foreseeing 3-levels of opportunity automated controller Cartesian directions from connecting joint boundaries. A speculative 3-level of opportunity automated controller has been considered in this investigation. Model preparing information has been obtained by mathematical forward kinematics calculation of the controller\u27s end effector arrangements. Nine datasets have been utilized for model preparing, while five datasets have been utilized for model testing or approval. The ANFIS model\u27s precision has been surveyed by figuring the Mean outright Percentage Error (MAPE) between the real and anticipated end-effector Cartesian directions. Because of Mean Absolute Percentage Error (MAPE), the created ANFIS model has forecast correctness’s of 63.35% and 80.07% in foreseeing x-directions and y-organizes, separately. Accordingly, ANFIS can be dependably executed as a commendable substitute for the customary arithmetical calculation method in anticipating controller Cartesian directions. It is suggested that the precision of other computational knowledge methods like Particle Swarm Optimization (PSO) and Support Vector Machines (SVM) be evaluated
ISSUES, CHALLENGES AND OPPORTUNITIES IN BLOCKCHAIN-BASED EDUCATIONAL PARADIGMS: A SYSTEMATIC LITERATURE REVIEW PROTOCOL
Get PDFBlockchain is a new technology that provides services of immutability, trust, disintermediation, collaboration, transparency. Nowadays, the use of this new technology is mostly used for Bitcoin and other cryptocurrencies, but apart from this, blockchain technology improved the performance level of other areas of life i.e. Higher Education Institution (HEI) and stakeholders, certificate verification, and many other domains. Students and educational institutions\u27 important data are mostly shared via different networks. The data integrity, privacy, and security are the major issues for these in blockchain technology which cannot be avoided. This paper is a documented plan for to conduct or a protocol based on which a systematic literature review would be conducted focusing on the issues, challenges, and major strengths of blockchain and its educational paradigms. The result of this review will be highly helpful for the new researchers to overcome the proposed issues and challenges, exploring educational paradigms in Blockchain, elaborate the major strengths helping the educational institutes in adapting process and increase the level of satisfaction. This study plans to explore some educational types with some issues and challenges such as scalability, immutability, and easy adaptation
ASSESSING THE IMPACT OF ARTISTIC ARCHITECTURE ON PERCEIVED USABILITY AND WILLINGNESS TO USE MOBILE COMMERCE APPLICATIONS
Get PDFCreating a useful portable browser or program is a demanding challenge for organisations due to the size constraints of the display area, which additionally is contingent on usability, internet connectivity, efficient backup power and mobility. Users also expect performance equivalent to that of microcomputers. Portable display programmers need to surmount these challenges without compromising users’ concerns for safety and confidentiality. Various works in the past couple of years revealed the ability of a strongly perceived artistic architecture of a mobile website as a push to overcome clients’ reduced skill. The investigation conducted in this report sought to determine the distinct impact of eight artistic display on the of a mobile website or program and the corresponding clients’ . Organisations may thus take advantage of this knowledge to improve usage, trust, frequency of adoption and consequently financial rewards on m-commerce applications. This study uses a with a screening objective. Data for the experiment was sourced through a web-based questionnaire program, . At the same time, social network sites were used to disseminate the questionnaires to enable gathering a good number of respondents. The data collection instrument had statements with threshold for Ninety-six participants took part in the inquiry. In contrast, seventy-six participants completed the exercise, signifying an estimated. was used in the study of the association concerning the . package was utilised in the evaluation of the . Findings revealed that artistic architecture bears a substantial impact on of portable websites and programs as well as influences artefacts on such platforms. , as well as the of an application, turned out as two features with the highest impact on the two factors. That the orientation of their effects is a reverse of the conjectured outcome suggests that future work should explore this issue
PAAD: POLITICAL ARABIC ARTICLES DATASET FOR AUTOMATIC TEXT CATEGORIZATION
Get PDFNow day’s text Classification and Sentiment analysis is considered as one of the popular Natural Language Processing (NLP) tasks. This kind of technique plays significant role in human activities and has impact on the daily behaviours. Each article in different fields such as politics and business represent different opinions according to the writer tendency. A huge amount of data will be acquired through that differentiation. The capability to manage the political orientation of an online article automatically. Therefore, there is no corpus for political categorization was directed towards this task in Arabic, due to the lack of rich representative resources for training an Arabic text classifier. However, we introduce political Arabic articles dataset (PAAD) of textual data collected from newspapers, social network, general forum and ideology website. The dataset is 206 articles distributed into three categories as (Reform, Conservative and Revolutionary) that we offer to the research community on Arabic computational linguistics. We anticipate that this dataset would make a great aid for a variety of NLP tasks on Modern Standard Arabic, political text classification purposes. We present the data in raw form and excel file. Excel file will be in four types such as V1 raw data, V2 preprocessing, V3 root stemming and V4 light stemming