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29,922 Article Results

Substrate integrated circuits for high frequency of opto electronics

10.11591/ijres.v9.i3.pp224-228
Mounika Punati , R. Yuvaraj
Another age of high-recurrence coordinated circuits is displayed, which is called substrate incorporated circuits (SICS). The current cutting edge of circuit plan and implementation stages dependent on this new idea are assessed and discussed in detail. Various potential outcomes and various favorable circumstances of the SICS are appeared for microwave, millimeter-wave and opto hardware applications. Down to earth models are delineated with hypothetical and trial results for substrate coordinated waveguide (SIW), substrate incorporated chunk waveguide (SISW) and substrate incorporated non-transmitting dielectric (SI") direct circuits. Future innovative work patterns are likewise dis-cussed regarding ease imaginative plan of millimeter-wave and optoelectronic coordinated circuits.
Volume: 9
Issue: 3
Page: 224-228
Publish at: 2020-11-01

A new hybrid conjugate gradient algorithm for unconstrained optimization with inexact line search

10.11591/ijeecs.v20.i2.pp939-947
Fanar N. Jardow , Ghada M. Al-Naemi
Many researchers are interested for developed and improved the conjugate gradient method for solving large scale unconstrained optimization problems. In this work a new parameter  will be presented as a convex combination between RMIL and MMWU. The suggestion method always produces a descent search direction at each iteration. Under strong wolfe powell (SWP) line search conditions, the global convergence of the proposed method is established. The preliminary numerical comparisons with some others CG methods have shown that this new method is efficient and robust in solving all given problems.
Volume: 20
Issue: 2
Page: 939-947
Publish at: 2020-11-01

Modification data attack inside computer systems: a critical review

10.11591/csit.v1i3.p98-105
Vahid Kaviani J , Parvin Ahmadi Doval Amiri , Farsad Zamani Brujeni , Nima Akhlaghi
This paper is a review of types of modification data attack based on computer systems and it explores the vulnerabilities and mitigations. Altering information is a kind of cyber-attack during which intruders interfere, catch, alter, take or erase critical data on the PCs and applications through using network exploit or by running malicious executable codes on victim's system. One of the most difficult and trendy areas in information security is to protect the sensitive information and secure devices from any kind of threats. Latest advancements in information technology in the field of information security reveal huge amount of budget funded for and spent on developing and addressing security threats to mitigate them. This helps in a variety of settings such as military, business, science, and entertainment. Considering all concerns, the security issues almost always come at first as the most critical concerns in the modern time. As a matter of fact, there is no ultimate security solution; although recent developments in security analysis are finding daily vulnerabilities, there are many motivations to spend billions of dollars to ensure there are vulnerabilities waiting for any kind of breach or exploit to penetrate into the systems and networks and achieve particular interests. In terms of modifying data and information, from old-fashioned attacks to recent cyber ones, all of the attacks are using the same signature: either controlling data streams to easily breach system protections or using non-control-data attack approaches. Both methods can damage applications which work on decision-making data, user input data, configuration data, or user identity data to a large extent. In this review paper, we have tried to express trends of vulnerabilities in the network protocols’ applications.
Volume: 1
Issue: 3
Page: 98-105
Publish at: 2020-11-01

Virtual assistant upper respiratory tract infection education based natural language

10.11591/csit.v2i3.p132-146
Wiwin Suwarningsih
The high incidence of upper respiratory tract infection (URTI) in Indonesia requires an efficient healthcare solution to maintain human wellbeing. The e-health education model proposed in this paper is a virtual assistant in the form of an interactive question and answer system assistant virtual interactive question answering (AVIQA) with a natural language approach. AVIQA is a form of problem-solving approach to design some aspects of education and consultation in helping parents to recognize symptoms and dealing with several preventive actions for toddlers when exposed to Upper Respiratory Tract Infection. The technologies proposed for the development of AVIQA include (i) Representation of sentence meanings to build an URTI knowledge base; (ii) Design of dialogue models for interactive consultation using a combination between information state and frame base model and (iii) Development of IQA based on casebase reasoning and semantic role labelling. The purpose of developing this technology is to achieve a system that is capable of assisting the users especially mothers in searching for information, reducing user time compared to reading a document, and providing a good advice for finding the right answers, which then can be constructed from a management model prototype information for the education and independent consultation for users. The final result of this study is e-health education system based Indonesian natural language that has an ability in terms of health consultations especially health of children under five in acute respiratory infection disease. This system is expected to have a significant impact on the ability of a mother to recognize symptoms and deal with children attacked by URTI.
Volume: 2
Issue: 3
Page: 132-146
Publish at: 2020-11-01

An optimized rubber sheet model for normalization phase of IRIS recognition

10.11591/csit.v1i3.p126-134
Selvamuthukumaran S. , Ramkumar T. , Shantharajah Shantharajah
Iris recognition is a promising biometric authentication approach and it is a very active topic in both research and realistic applications because the pattern of the human iris differs from person to person, even between twins. In this paper, an optimized iris normalization method for the conversion of segmented image into normalized form has been proposed. The existing methods are converting the Cartesian coordinates of the segmented image into polar coordinates. To get more accuracy, the proposed method is using an optimized rubber sheet model which converts the polar coordinates into spherical coordinates followed by localized histogram equalization. The experimental result shows the proposed method scores an encouraging performance with respect to accuracy.
Volume: 1
Issue: 3
Page: 126-134
Publish at: 2020-11-01

Genome feature optimization and coronary artery disease prediction using cuckoo search

10.11591/csit.v1i3.p106-115
E. Neelima , M.S. Prasad Babu
Cardiovascular diseases (CVD) is among the major health ailment issue leading to millions of deaths every year. In recent past, analyzing gene expression data, particularly using machine learning strategies to predict and classify the given unlabeled gene expression record is a generous research issue. Concerning this, a substantial requirement is feature optimization, which is since the overall genes observed in human body are closely 25000 and among them 636 are cardio vascular related genes. Hence, it complexes the process of training the machine learning models using these entire cardio vascular gene features. This manuscript uses bidirectional pooled variance strategy of ANOVA standard to select optimal features. Along the side to surpass the constraint observed in traditional classifiers, which is unstable accuracy at k-fold cross validation, this manuscript proposed a classification strategy that build upon the swarm intelligence technique called cuckoo search. The experimental study indicating that the number of optimal features those selected by proposed model is substantially low that compared to the other contemporary model that selects features using forward feature selection and classifies using SVM classifier (FFS and SVM). The experimental study evinced that the proposed model, which selects feature by bidirectional pooled variance estimation and classifies using proposed classification strategy that build on cuckoo search (BPVE and CS) outperformed the selected contemporary model (FFS and SVM).
Volume: 1
Issue: 3
Page: 106-115
Publish at: 2020-11-01

Less computational approach to detect QRS complexes in ECG rhythms

10.11591/csit.v2i3.p113-120
Tariq M. Younes , Mohammad Alkhedher , Mohamad Al Khawaldeh , Jalal Nawash , Ibrahim Al-Abbas
Electrocardiogram (ECG) signals are normally affected by artifacts that require manual assessment or use of other reference signals. Currently, Cardiographs are used to achieve basic necessary heart rate monitoring in real conditions. This work aims to study and identify main ECG features, QRS complexes, as one of the steps of a comprehensive ECG signal analysis. The proposed algorithm suggested an automatic recognition of QRS complexes in ECG rhythm. This method is designed based on several filter structure composes low pass, difference and summation filters. The filtered signal is fed to an adaptive threshold function to detect QRS complexes. The algorithm was validated and results were checked with experimental data based on sensitivity test.
Volume: 2
Issue: 3
Page: 113-120
Publish at: 2020-11-01

Hancitor malware recognition using swarm intelligent technique

10.11591/csit.v2i3.p103-112
Laheeb M. Ibrahim , Maisirreem Atheeed Kamal , AbdulSattar A. Al-Alusi
Malware is a global risk rife designed to destroy computer systems without the owner's knowledge. It is still regarded as the most popular threat that attacks computer systems. Early recognition of unknown malware remains a problem. Swarm intelligence (SI), usually customer societies, communicate locally with their domain and with each other. Clients use very simple rules of behavior and the interactions between them lead to smart appearance, noticeable, individual behavior and optimized solution of problem and SI has been successfully applied in many fields, especially for malware ion tasks. SI also saves a considerable amount of time and enhances the precision of the malware recognition system. This paper introduces a malware recognition system for Hancitor malware using the gray wolf optimization algorithm (GWO) and artificial bee colony algorithm (ABC), which can effectively recognize Hancitor in networks.
Volume: 2
Issue: 3
Page: 103-112
Publish at: 2020-11-01

Low power network on chip architectures: A survey

10.11591/csit.v2i3.p158-168
Muhammad Raza Naqvi
Mostly communication now days is done through system on chip (SoC) models so, network on chip (NoC) architecture is most appropriate solution for better performance. However, one of major flaws in this architecture is power consumption. To gain high performance through this type of architecture it is necessary to confirm power consumption while designing this. Use of power should be diminished in every region of network chip architecture. Lasting power consumption can be lessened by reaching alterations in network routers and other devices used to form that network. This research mainly focusses on state-of-the-art methods for designing NoC architecture and techniques to reduce power consumption in those architectures like, network architecture, network links between nodes, network design, and routers.
Volume: 2
Issue: 3
Page: 158-168
Publish at: 2020-11-01

Hand gesture recognition using machine learning algorithms

10.11591/csit.v1i3.p116-120
Abhishek B. , Kanya Krishi , Meghana M. , Mohammed Daaniyaal , Anupama H. S.
Gesture recognition is an emerging topic in today’s technologies. The main focus of this is to recognize the human gestures using mathematical algorithms for human computer interaction. Only a few modes of Human-Computer Interaction exist, they are: through keyboard, mouse, touch screens etc. Each of these devices has their own limitations when it comes to adapting more versatile hardware in computers. Gesture recognition is one of the essential techniques to build user-friendly interfaces. Usually gestures can be originated from any bodily motion or state, but commonly originate from the face or hand. Gesture recognition enables users to interact with the devices without physically touching them. This paper describes how hand gestures are trained to perform certain actions like switching pages, scrolling up or down in a page.
Volume: 1
Issue: 3
Page: 116-120
Publish at: 2020-11-01

Wheelchair and robotic arm controls using gyro sensor

10.11591/csit.v1i3.p121-125
Asep Sholahuddin , Dessy Novita , Deni Setiana
Someone who could only move his/her head and could not move his/her limbs needs a wheelchair and robotic arms which could be controlled by the head. Gyro sensor is a sensor that could be controlled according to the tilt in the x,y coordinates which then implemented to measure the tilt of the user’s head. This paper would explain the head detector application to control wheelchairs and robotic arms. They could be controlled using Arduino microcontroller which uses C programming language. Gyro sensor that is put on the head could control wheelchair to move left, right, forward and backward according to the tilt of the head. It is the same with controlling robotic arms. It could rotate left, right, and move up and down. The x and y coordinates are used to determine the tilt of the head for controlling the wheelchair as well as robotic arms’ movements. It is discovered that wheelchairs and robotic arms could be controlled by the head by the use of gyro sensor.
Volume: 1
Issue: 3
Page: 121-125
Publish at: 2020-11-01

Classification of mammograms based on features extraction techniques using support vector machine

10.11591/csit.v2i3.p121-131
Enas Mohammed Hussein Saeed , Hayder Adnan Saleh , Enam Azez Khalel
Now mammography can be defined as the most reliable method for early breast cancer detection. The main goal of this study is to design a classifier model to help radiologists to provide a second view to diagnose mammograms. In the proposed system medium filter and binary image with a global threshold have been applied for removing the noise and small artifacts in the pre-processing stage. Secondly, in the segmentation phase, a hybrid bounding box and region growing (HBBRG) algorithm are utilizing to remove pectoral muscles, and then a geometric method has been applied to cut the largest possible square that can be obtained from a mammogram which represents the ROI. In the features extraction phase three method was used to prepare texture features to be a suitable introduction to the classification process are first order (statistical features), local binary patterns (LBP), and gray-level co-occurrence matrix (GLCM), finally, SVM has been applied in two-level to classify mammogram images in the first level to normal or abnormal, and then the classification of abnormal once in the second level to the benign or malignant image. The system was tested on the MAIS the Mammogram image analysis Society (MIAS) database, in addition to the image from the Teaching Oncology Hospital, Medical City in Baghdad, where the results showed achieving an accuracy of 95.454% for the first level and 97.260% for the second level, also, the results of applying the proposed system to the MIAS database alone were achieving an accuracy of 93.105% for the first level and 94.59 for the second level.
Volume: 2
Issue: 3
Page: 121-131
Publish at: 2020-11-01

Performance evaluation of blockchains in the internet of things

10.11591/csit.v1i3.p93-97
Tanweer Alam
The idea of blockchains technology (BT) in the internet of things (IoT) is to allow the physical things to trust in the transactions held within the IoT network. The BT is a distributed, decentralized, publicly shared its digital ledger and secured technology to eternally record the transactions across the shared database. The BT in IoT can be called Trust Machine to eliminate the intermediates and enables the physical things to trust with each other. This research evaluates the performance of BT in IoT. The simulated results are tested and can be used in the sustainable development of the integration of BT and IoT.
Volume: 1
Issue: 3
Page: 93-97
Publish at: 2020-11-01

The effect of optimizers in fingerprint classification model utilizing deep learning

10.11591/ijeecs.v20.i2.pp1098-1102
Farah F. Alkhalid
Fingerprint is the most popular way to identify persons, it is assumed a unique identity, which enable us to return the record of specific person through his fingerprint, and could be useful in many applications; such as military applications, social applications, criminal applications… etc. In this paper, the study of a new model based deep learning is suggested. The focus is directed on how to enhance the training model with the increase of the testing accuracy by applying four scenarios and comparing among them. The effects of two dedicated optimizers are shown and their contrast enhancement is tested. The results prove that the testing accuracy is 85.61% for “Adadelta” optimizer, whereas for “Adam” optimizer, it is 91.73%.
Volume: 20
Issue: 2
Page: 1098-1102
Publish at: 2020-11-01

Earlier stage for straggler detection and handling using combined CPU test and LATE methodology

10.11591/ijece.v10i5.pp4910-4917
Anwar H. Katrawi , Rosni Abdullah , Mohammed Anbar , Ammar Kamal Abasi
Using MapReduce in Hadoop helps in lowering the execution time and power consumption for large scale data. However, there can be a delay in job processing in circumstances where tasks are assigned to bad or congested machines called "straggler tasks"; which increases the time, power consumptions and therefore increasing the costs and leading to a poor performance of computing systems. This research proposes a hybrid MapReduce framework referred to as the combinatory late-machine (CLM) framework. Implementation of this framework will facilitate early and timely detection and identification of stragglers thereby facilitating prompt appropriate and effective actions.
Volume: 10
Issue: 5
Page: 4910-4917
Publish at: 2020-10-01
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