Computer Engineering Commons^™

Ionic Conductiviy Of Alginate-Nh4cl Polymer Electrolyte, Nurhasniza Mamajan Khan, Noor Saadiah Mohd Ali, Ahmad Faizrin Ahmad Fuzlin, Ahmad Salihin Samsudin

Makara Journal of Technology

This study aims to produce a solid biopolymer electrolyte (SBE) by doping ammonium chloride (NH4Cl) into alginate. Solution casting was used to prepare the alginate–NH4Cl SBE system. Electrical impedance spectroscopy was performed to analyze the electrical properties of the SBE under the applied frequency range of 50 Hz–1 MHz. The incorporation of 8 wt.% NH4Cl enhances the ionic conductivity of the SBE up to 3.18 × 10-7 S/cm at ambient room temperature. Fourier transform infrared spectroscopy shows that complexation occurs between the hydroxyl (-OH), carboxylate (COO-) and ether linkage (C-O-C) functional groups due to the formation of inter- and intra-molecular …

Use Of Viscoplastic Damper For Improving The Resistance Of Steel Frames To Blast Loading, Gholamreza Abdollahzadeh, Hadi Faghihmaleki, Hamed Hamidi Jamnani, Atefeh Ebrahimi Bardar

Makara Journal of Technology

In this paper, we evaluated the effect of viscoplastic dampers on the response of steel frames under blast loading. We used SAP2000 software and link elements to investigate the responses of nine-story steel frames with and without dampers. The proposed viscoplastic damper is a new type of viscous damper. The application of this damper is based on the availability of its constituent materials. The damper acts as a viscoelastic damper at low levels of vibration, but it acts as a combination of viscoelastic operator and metal-yielding device at extreme levels of vibration. With respect to the height of the structure, …

Influence Of Lithium Bromide On Electrical Properties In Bio-Based Polymer Electrolytes, Ahmad Faizrin Fuzlin, Bouchta Sahraoui, Ahmad Salihin Samsudin

Makara Journal of Technology

This research presents the influence of lithium bromide (LiBr) on the electrical properties of alginate in bio-based polymer electrolytes (BBPEs) system. Bio-based alginate was prepared using the solution casting technique with various LiBr compositions. The ionic conductivity and electrical properties of the prepared BBPEs samples were studied using electrical impedance spectroscopy over a frequency range of 50 Hz–1 MHz. A maximum ionic conductivity of 7.46 × 10−5 S cm-1 was obtained for a sample containing 15 wt. % lithium bromide-doped alginate BBPEs at ambient temperature (303 K). The electrical analysis revealed that the most conductive sample based on alginate-LiBr BBPEs …

Mechanical Alloying-Assisted Coating Of Fe–Al Powders On Steel Substrate, Alfian Noviyanto, Sri Harjanto, Wahyu Bambang Widayatno, Agus Sukarto Wismogroho, Muhamad Ikhlasul Amal, Nurul Taufiqu Rochman

Makara Journal of Technology

The coating layer of Fe–Al powders on the steel substrate was prepared by mechanical alloying at room temperature. Fe, Al, and the steel substrates were milled with high-energy ball milling for 32 h with a ball-to-powder ratio of 8 in an argon atmosphere to prevent oxidation during milling. Although mechanical alloying was performed for 32 h, no new phases were observed after mechanical alloying, as analyzed by X-ray diffraction. However, the crystallite size of the milled powders for 32 h decreased by factor two compared with the initial powders. Scanning electron micrographs showed that the coating layers formed >8 h …

Performance Of Free-Space Optical Communication Systems Using Optical Amplifiers Under Amplify-Forward And Amplify-Received Configurations, Ucuk Darusalam, Arockia Bazil Raj, Fitri Yuli Zulkifli, Purnomo Sidi Priambodo, Eko Tjipto Rahardjo

Makara Journal of Technology

With the growth of digital technology in the stage of industrial revolution 4.0, the demand for broadcasting large amounts of information to last mile users has increased. Free-space optical (FSO) communication is one of the telecommunication platforms that has shown immense potential in meeting the demand for information broadcasting. In this work, the performance of FSO communication based on wavelength division multiplexing with a data rate of 80 Gbps is investigated through simulations. The configuration of optical amplifiers in the FSO system is set up on the basis of the amplify-forward and amplify-received configurations to expand the network. The investigation …

Iso 9001:2015 Risk-Based Thinking: A Framework Using Fuzzy-Support Vector Machine, Ralph Sherwin A. Corpuz

Makara Journal of Technology

Risk-based thinking (RBT) is one of the distinct new features of the International Organization for Standardization 9001:2015. Interestingly, the standard does not prescribe any tools. Hence, organizations are puzzled as to the extent of conformance. Some organizations have adopted formal tools. However, these tools seem insufficient in linking the standard into an evidence-based decision support system. To resolve gaps in RBT implementation, this paper proposes a framework based on fuzzy inference system (FIS) and support vector machine (SVM) to automate risk analysis and evaluation, proposal and verification of action plans, and prediction of the feasibility of risks and opportunities according …

Object Recognition And Voice Assistant With Augmented Reality, Juan Estrella

Publications and Research

Our research project aims to provide a visually impaired person with a superimposed map that will guide the individual to the desired destination through a voice controlled virtual assistant application that integrates Augmented Reality (AR) with Artificial Intelligence (AI) Computer Vision and Natural Language Processing (subfields of AI) will be combined to identify the spatial environment and then create a graphic enhancement that provides the most direct route to the specific destination These technologies will be incorporated into the Microsoft Hololens which will be controlled by the user.

Representational Learning Approach For Predicting Developer Expertise Using Eye Movements, Sumeet Maan

School of Computing: Dissertations, Theses, and Student Research

The thesis analyzes an existing eye-tracking dataset collected while software developers were solving bug fixing tasks in an open-source system. The analysis is performed using a representational learning approach namely, Multi-layer Perceptron (MLP). The novel aspect of the analysis is the introduction of a new feature engineering method based on the eye-tracking data. This is then used to predict developer expertise on the data. The dataset used in this thesis is inherently more complex because it is collected in a very dynamic environment i.e., the Eclipse IDE using an eye-tracking plugin, iTrace. Previous work in this area only worked on …

Bibliometric Analysis Of The Literature In The Field Of Information Technology Relatedness, Ilham M, Anis Eliyana, Praptini Yulianti

Library Philosophy and Practice (e-journal)

This bibliometric describe Information Technology Relateness is defined as the use of information technology infrastructure and information technology management processes betweeWas this submission previously published in a journal? Bepress will automatically create an OpenURL for published articles. Learn more about OpenURLsn business units together. There is not much research on Information Technology Relateness by providing a big picture that is visualized from year to year. This study aims to map research in the field of Information Technology Relateness with data from all international research publications. This study performs a bibliometric method and analyzes research data using the Services Analyze …

Improve The Prototype Of Low-Cost Near-Infrared Diffuse Optical Imaging System, Chen Xu, Mohammed Z. Shakil

Publications and Research

Diffuse Optical Tomography (DOT) and Optical Spectroscopy using near-infrared (NIR) diffused light has demonstrated great potential for the initial diagnosis of tumors and in the assessment of tumor vasculature response to neoadjuvant chemotherapy. The aims of this project are 1) to test the different types of LEDs in the near-infrared range, and design the driving circuit, and test the modulation of LEDs at different frequencies; 2) to test the APDs as a detector, and build the receiver system and compare efficiency with pre-built systems. In this project, we are focusing on creating a low-cost infrared transmission system for tumor and …

Evaluation Of Characteristics Of Wireless Sensor Networks With Analytical Modeling, Halim Khujamatov, Ernazar Reypnazarov, Doston Hasanov, Elaman Nurullaev, Shahzod Sobirov

Bulletin of TUIT: Management and Communication Technologies

In this paper, a mathematical model of the operation process of the ZigBee standard wireless network for remote monitoring systems based on the Markov chain apparatus was developed. Using the model was evaluated of the operation process of the CSMA/CA algorithm of the MAC level of the IEEE 802.15.4 ZigBee standard. The peculiarity of this mathematical model is that it takes into account the level of loading of network elements and potential distortions in the transmitted packets as a result of the influence of interference. Using the developed mathematical model, the main characteristics of the network operation process, such as …

Predicting Residential Energy Consumption Using Wavelet Decomposition With Deep Neural Network, Dagimawi Eneyew, Miriam A M Capretz, Girma Bitsuamlak, London Hydro

Electrical and Computer Engineering Publications

Electricity consumption is accelerating due to economic and population growth. Hence, energy consumption prediction is becoming vital for overall consumption management and infrastructure planning. Recent advances in smart electric meter technology are making high-resolution energy consumption data available. However, many parameters influencing energy consumption are not typically monitored for residential buildings. Therefore, this study’s main objective is to develop a data-driven energy consumption forecasting model (next-hour consumption) for residential houses solely based on analyzing electricity consumption data. This research proposes a deep neural network architecture that combines stationary wavelet transform features and convolutional neural networks. The proposed approach utilizes automatically …

Deep Neural Network For Load Forecasting Centred On Architecture Evolution, Santiago Gomez-Rosero, Miriam A M Capretz, London Hydro

Electrical and Computer Engineering Publications

Nowadays, electricity demand forecasting is critical for electric utility companies. Accurate residential load forecasting plays an essential role as an individual component for integrated areas such as neighborhood load consumption. Short-term load forecasting can help electric utility companies reduce waste because electric power is expensive to store. This paper proposes a novel method to evolve deep neural networks for time series forecasting applied to residential load forecasting. The approach centres its efforts on the neural network architecture during the evolution. Then, the model weights are adjusted using an evolutionary optimization technique to tune the model performance automatically. Experimental results on …

Transfer-To-Transfer Learning Approach For Computer Aided Detection Of Covid-19 In Chest Radiographs, Barath Narayanan Narayanan, Russell C. Hardie, Vignesh Krishnaraja, Christina Karam, Venkata Salini Priyamvada Davuluru

Electrical and Computer Engineering Faculty Publications

The coronavirus disease 2019 (COVID-19) global pandemic has severely impacted lives across the globe. Respiratory disorders in COVID-19 patients are caused by lung opacities similar to viral pneumonia. A Computer-Aided Detection (CAD) system for the detection of COVID-19 using chest radiographs would provide a second opinion for radiologists. For this research, we utilize publicly available datasets that have been marked by radiologists into two-classes (COVID-19 and non-COVID-19). We address the class imbalance problem associated with the training dataset by proposing a novel transfer-to-transfer learning approach, where we break a highly imbalanced training dataset into a group of balanced mini-sets and …

Proportional Voting Based Semi-Unsupervised Machine Learning Intrusion Detection System, Yang G. Kim, Ohbong Kwon, John Yoon

Publications and Research

Feature selection of NSL-KDD data set is usually done by finding co-relationships among features, irrespective of target prediction. We aim to determine the relationship between features and target goals to facilitate different target detection goals regardless of the correlated feature selection. The unbalanced data structure in NSL-KDD data can be relaxed by Proportional Representation (PR). However, adopting PR would deny the notion of winner-take-all by attracting a majority of the vote and also provide a fairly proportional share for any grouping of like-minded data. Furthermore, minorities and majorities would get a fair share of power and representation in data structure …

Docs_On_Blocks – A Defense In Depth Strategy For E-Healthcare, Saad Mohammed

Electronic Theses, Projects, and Dissertations

With the increase in the data breaches and cyber hacks, organizations have come to realize that cyber security alone would not help as the attacks are becoming more sophisticated and complex than ever. E-Healthcare industry has shown a promising improvement in terms of security over the past, but the threat remains. Thus, the E-Healthcare industries are aiming towards a Defense in Depth Strategy approach.

The project here describes how a Defense in Depth Strategy for E-Healthcare system can provide an environment for better security of the data and peer-to-peer interaction with stakeholders. The legacy systems have at some point failed …

Simple Modification For An Apriori Algorithm With Combination Reduction And Iteration Limitation Technique, Adie Wahyudi Oktavia Gama, Ni Made Widnyani

Knowledge Engineering and Data Science

Apriori algorithm is one of the methods with regard to association rules in data mining. This algorithm uses knowledge from an itemset previously formed with frequent occurrence frequencies to form the next itemset. An a priori algorithm generates a combination by iteration methods that are using repeated database scanning process, pairing one product with another product and then recording the number of occurrences of the combination with the minimum limit of support and confidence values. The a priori algorithm will slow down to an expanding database in the process of finding frequent itemset to form association rules. Modification techniques are …

Segmentation Method For Face Modelling In Thermal Images, Albar Albar, Hendrick Hendrick, Rahmat Hidayat

Knowledge Engineering and Data Science

Face detection is mostly applied in RGB images. The object detection usually applied the Deep Learning method for model creation. One method face spoofing is by using a thermal camera. The famous object detection methods are Yolo, Fast Region Based Convolutional Neural Networks (RCNN), Faster RCNN, SSD, and Mask RCNN. We proposed a segmentation Mask RCNN method to create a face model from thermal images. This model was able to locate the face area in images. The dataset was established using 1600 images. The images were created from direct capturing and collecting from the online dataset. The Mask RCNN was …

Generating Javanese Stopwords List Using K-Means Clustering Algorithm, Aji Prasetya Wibawa, Hidayah Kariima Fithri, Ilham Ari Elbaith Zaeni, Andrew Nafalski

Knowledge Engineering and Data Science

Stopword removal necessary in Information Retrieval. It can remove frequently appeared and general words to reduce memory storage. The algorithm eliminates each word that is precisely the same as the word in the stopword list. However, generating the list could be time-consuming. The words in a specific language and domain must be collected and validated by specialists. This research aims to develop a new way to generate a stop word list using the K-means Clustering method. The proposed approach groups words based on their frequency. The confusion matrix calculates the difference between the findings with a valid stopword list created …

Authentication Based On Blockchain, Norah Alilwit

Doctoral Dissertations and Master's Theses

Across past decade online services have enabled individuals and organizations to perform different types of transactions such as banking, government transactions etc. The online services have also enabled more developments of applications, at cheap cost with elastic and scalable, fault tolerant system. These online services are offered by services providers which are use authentication, authorization and accounting framework based on client-server model. Though this model has been used over decades, study shows it is vulnerable to different hacks and it is also inconvenient to use for the end users. In addition, the services provider has total control over user data …

Efficient Scheduling Of Plantation Company Workers Using Genetic Algorithm, Wayan Firdaus Mahmudy, Andreas Pardede, Agus Wahyu Widodo, Muh Arif Rahman

Knowledge Engineering and Data Science

Workers at large plantation companies have various activities. These activities include caring for plants, regularly applying fertilizers according to schedule, and crop harvesting activities. The density of worker activities must be balanced with efficient and fair work scheduling. A good schedule will minimize worker dissatisfaction while also maintaining their physical health. This study aims to optimize workers' schedules using a genetic algorithm. An efficient chromosome representation is designed to produce a good schedule in a reasonable amount of time. The mutation method is used in combination with reciprocal mutation and exchange mutation, while the type of crossover used is one …

A Review Of Accessing Big Data With Significant Ontologies, Jumah Y.J Sleeman, Jehad A.H Hammad

Knowledge Engineering and Data Science

Ontology Based Data Access (OBDA) is a recently proposed approach which is able to provide a conceptual view on relational data sources. It addresses the problem of the direct access to big data through providing end-users with an ontology that goes between users and sources in which the ontology is connected to the data via mappings. We introduced the languages used to represent the ontologies and the mapping assertions technique that derived the query answering from sources. Query answering is divided into two steps: (i) Ontology rewriting, in which the query is rewritten with respect to the ontology into new …

Convolutional Neural Network On Tanned And Synthetic Leather Textures, Faadihilah Ahnaf Faiz, Ahmad Azhari

Knowledge Engineering and Data Science

Tanned leather is an output from complex processes called tanning. Leather tanning is an important step that used to protect the fiber or protein structure of animal’s skin. Another reason of tanning process is to prevent the animal’s skin from any defect or rot. After the tanning is complete, the leather can be applied to produce a wide variety of leather products. Thus, the leather prices usually more expensive because it takes longer time in process. Another way to get cheaper price is make non-animal leather that usually known as synthetic or imitation leather. The purpose of this paper is …

Modern Standard Arabic Speech Recognition: Using Formants Measurements To Extract Vowels From Arabic Words’ Consonant-Vowel-Consonant-Vowel Structure, Mohamed Ali Alshaari

Theses and Dissertations

Arabic texts suffer from missing diacritics (short vowels) which become obstacles for new learners. Speech Recognition is the translation of words spoken to text through intelligent computer programs. As of today, it has been integrated into many computer systems. Arabic Speech Recognition has made progress over the years, but it is still not as good as English speech recognition due to the problem of short vowels not being recognized. This is mainly because the Arabic language is unlike the English language in the nature because it is a Semitic language. This is reflected in different characteristics such as grammar, morphology, …

Medical Knowledge-Enriched Textual Entailment Framework, Shweta Yadav, Vishal Pallagani, Amit P. Sheth

Publications

One of the cardinal tasks in achieving robust medical question answering systems is textual entailment. The existing approaches make use of an ensemble of pre-trained language models or data augmentation, often to clock higher numbers on the validation metrics. However, two major shortcomings impede higher success in identifying entailment: (1) understanding the focus/intent of the question and (2) ability to utilize the real-world background knowledge to capture the context beyond the sentence. In this paper, we present a novel Medical Knowledge-Enriched Textual Entailment framework that allows the model to acquire a semantic and global representation of the input medical text …

Global Privacy Concerns Of Facial Recognition Big Data, Myranda Westbrook

Honors Theses

Facial recognition technology is a system of automatic acknowledgement that recognizes individuals by categorizing specific features of their facial structure to link the scanned information to stored data. Within the past few decades facial recognition technology has been implemented on a large scale to increase the security measures needed to access personal information. This has been specifically used in surveillance systems, social media platforms, and mobile device access control. The extensive use of facial recognition systems has created challenges as it relates to biometric information control and privacy concerns. This concern raises the cost and benefit analysis of an individual’s …

Using Agile Methodology To Create A Working Software Application, Aaron Cuadras

Honors Theses

This thesis explains the importance of Agile in the process of writing a web application. A team of three developers worked for sixteen weeks to create a booking site able to hold important information related to customers and hotel owners. The framework used for the project is Ruby on Rails and we stress the importance of the Models-View-Controller pattern and its relation with the Agile methodology, the problem of creating a new application is the pace in which everything should be written as well as the ability to write features independent from one another. The results that we expect are …

Automatic Target Recognition With Convolutional Neural Networks., Nada Baili

Electronic Theses and Dissertations

Automatic Target Recognition (ATR) characterizes the ability for an algorithm or device to identify targets or other objects based on data obtained from sensors, being commonly thermal. ATR is an important technology for both civilian and military computer vision applications. However, the current level of performance that is available is largely deficient compared to the requirements. This is mainly due to the difficulty of acquiring targets in realistic environments, and also to limitations of the distribution of classified data to the academic community for research purposes. This thesis proposes to solve the ATR task using Convolutional Neural Networks (CNN). We …

Imparting 3d Representations To Artificial Intelligence For A Full Assessment Of Pressure Injuries., Sofia Zahia

Electronic Theses and Dissertations

During recent decades, researches have shown great interest to machine learning techniques in order to extract meaningful information from the large amount of data being collected each day. Especially in the medical field, images play a significant role in the detection of several health issues. Hence, medical image analysis remarkably participates in the diagnosis process and it is considered a suitable environment to interact with the technology of intelligent systems. Deep Learning (DL) has recently captured the interest of researchers as it has proven to be efficient in detecting underlying features in the data and outperformed the classical machine learning …

Towards Sensorimotor Coupling Of A Spiking Neural Network And Deep Reinforcement Learning For Robotics Application, Kashu Yamazaki

Mechanical Engineering Undergraduate Honors Theses

Deep reinforcement learning augments the reinforcement learning framework and utilizes the powerful representation of deep neural networks. Recent works have demonstrated the great achievements of deep reinforcement learning in various domains including finance,medicine, healthcare, video games, robotics and computer vision.Deep neural network was started with multi-layer perceptron (1stgeneration) and developed to deep neural networks (2ndgeneration)and it is moving forward to spiking neural networks which are knownas3rdgeneration of neural networks. Spiking neural networks aim to bridge the gap between neuroscience and machine learning, using biologically-realistic models of neurons to carry out computation. In this thesis, we first provide a comprehensive review …