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Articles 181 - 210 of 577

Full-Text Articles in Systems and Communications

Faster Multidimensional Data Queries On Infrastructure Monitoring Systems, Yinghua Qin, Gheorghi Guzun Feb 2022

Faster Multidimensional Data Queries On Infrastructure Monitoring Systems, Yinghua Qin, Gheorghi Guzun

Faculty Research, Scholarly, and Creative Activity

The analytics in online performance monitoring systems have often been limited due to the query performance of large scale multidimensional data. In this paper, we introduce a faster query approach using the bit-sliced index (BSI). Our study covers multidimensional grouping and preference top-k queries with the BSI, algorithms design, time complexity evaluation, and the query time comparison on a real-time production performance monitoring system. Our research work extended the BSI algorithms to cover attributes filtering and multidimensional grouping. We evaluated the query time with the single attribute, multiple attributes, feature filtering, and multidimensional grouping. To compare with the existing prior …

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Real-Time Complex Hand Gestures Recognition Based On Multi- Dimensional Features, Isack Bulugu Feb 2022

Real-Time Complex Hand Gestures Recognition Based On Multi- Dimensional Features, Isack Bulugu

Tanzania Journal of Engineering and Technology (TJET)

Gesture recognition is broadly utilized within the field of sensing. There are basically three gesture recognition methods based on computer vision, depth sensor and motion sensor. Motion sensor-based gesture recognition has few input data, fast speed, and direct access to three- dimensional information of the hand. The advantages of traditional motion sensor-based gesture recognition have gradually become a current research hotspot. The essence of traditional motion sensor-based gesture recognition is a pattern recognition problem, and its accuracy depends heavily on the feature dataset extracted from prior experience. Unlike traditional pattern recognition methods, deep learning can be used to a large …

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Complete Neighbourhood Search Heuristic Algorithm For Portfolio Optimization, Collether John Feb 2022

Complete Neighbourhood Search Heuristic Algorithm For Portfolio Optimization, Collether John

Tanzania Journal of Engineering and Technology (TJET)

In portfolio optimization, the fundamental goal of an investor is to optimally allocate investments between different assets. Mean-variance optimization methods make unrealistic assumptions to solve the problem of optimal allocation. On the other hand, when realistic constraints like holding size and cardinality are introduced it leads to optimal asset allocation which differ from the mean variance optimization. The resulting optimization problem become quite complex as it exhibits multiple local extrema and discontinuities. Heuristic algorithms work well for the complex problem. Therefore, a heuristic algorithm is developed which is based on hill climbing complete (HC-C). It is utilized to solve the …

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A Deep Neural Network For Early Detection And Prediction Of Chronic Kidney Disease, Vijendra Singh, Vijayan K. Asari, Rajkumar Rajasekaran Jan 2022

A Deep Neural Network For Early Detection And Prediction Of Chronic Kidney Disease, Vijendra Singh, Vijayan K. Asari, Rajkumar Rajasekaran

Electrical and Computer Engineering Faculty Publications

Diabetes and high blood pressure are the primary causes of Chronic Kidney Disease (CKD). Glomerular Filtration Rate (GFR) and kidney damage markers are used by researchers around the world to identify CKD as a condition that leads to reduced renal function over time. A person with CKD has a higher chance of dying young. Doctors face a difficult task in diagnosing the different diseases linked to CKD at an early stage in order to prevent the disease. This research presents a novel deep learning model for the early detection and prediction of CKD. This research objectives to create a deep …

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A Progressive Learning Strategy For Large-Scale Glacier Mapping, Zhiyuan Xie, Umesh K. Haritashya, Vijayan K. Asari Jan 2022

A Progressive Learning Strategy For Large-Scale Glacier Mapping, Zhiyuan Xie, Umesh K. Haritashya, Vijayan K. Asari

Electrical and Computer Engineering Faculty Publications

In recent years, the worldwide temperature increase has resulted in rapid deglaciation and a higher risk of glacier-related natural hazards such as flooding and debris flow. Due to the severity of these hazards, continuous observation and detailed analysis of glacier fluctuations are crucial. Many such analyses require an accurately delineated glacier boundary. However, the complexity and heterogeneity of glaciers, particularly debris-covered glaciers (DCGs), poses a challenge for glacier mapping when using conventional remote sensing or machine-learning techniques. Some examples exist about small-scale automated glacier mapping, but large or regional-scale mapping is challenging. Previously, a deep-learning-based approach named GlacierNet2 had been …

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Detecting The Presence Of Electronic Devices In Smart Homes Using Harmonic Radar, Beatrice Perez, Gregory Mazzaro, Timothy J. Pierson, David Kotz Jan 2022

Detecting The Presence Of Electronic Devices In Smart Homes Using Harmonic Radar, Beatrice Perez, Gregory Mazzaro, Timothy J. Pierson, David Kotz

Dartmouth Scholarship

Data about users is collected constantly by phones, cameras, Internet websites, and others. The advent of so-called ‘Smart Things' now enable ever-more sensitive data to be collected inside that most private of spaces: the home. The first step in helping users regain control of their information (inside their home) is to alert them to the presence of potentially unwanted electronics. In this paper, we present a system that could help homeowners (or home dwellers) find electronic devices in their living space. Specifically, we demonstrate the use of harmonic radars (sometimes called nonlinear junction detectors), which have also been used in …

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Meltpondnet: A Swin Transformer U-Net For Detection Of Melt Ponds On Arctic Sea Ice, Ivan Sudakow, Vijayan K. Asari, Ruixu Liu, Denis Demchev Jan 2022

Meltpondnet: A Swin Transformer U-Net For Detection Of Melt Ponds On Arctic Sea Ice, Ivan Sudakow, Vijayan K. Asari, Ruixu Liu, Denis Demchev

Electrical and Computer Engineering Faculty Publications

High-resolution aerial photographs of Arctic region are a great source for different sea ice feature recognition, which are crucial to validate, tune, and improve climate models. Melt ponds on the surface of melting Arctic sea ice are of particular interest as they are sensitive and valuable indicators and are proxy to the processes in the Arctic climate system. Manual analysis of this remote sensing data is extremely difficult and time-consuming due to the complex shapes and unpredictable boundaries of the melt ponds, and that leads to the necessity for automatizing the processes. In this study, we propose a robust and …

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Circuit Optimization Techniques For Efficient Ex-Situ Training Of Robust Memristor Based Liquid State Machine, Alex Henderson, Christopher Yakopcic, Cory Merkel, Steven Harbour, Tarek M. Taha, Hananel Hazan Jan 2022

Circuit Optimization Techniques For Efficient Ex-Situ Training Of Robust Memristor Based Liquid State Machine, Alex Henderson, Christopher Yakopcic, Cory Merkel, Steven Harbour, Tarek M. Taha, Hananel Hazan

Electrical and Computer Engineering Faculty Publications

Spiking neural network hardware offers a high performance, power-efficient and robust platform for the processing of complex data. Many of these systems require supervised learning, which poses a challenge when using gradient-based algorithms due to the discontinuous properties of SNNs. Memristor based hardware can offer gains in portability, power reduction, and throughput efficiency when compared to pure CMOS. This paper proposes a memristor-based spiking liquid state machine (LSM). The inherent dynamics of the LSM permit the use of supervised learning without backpropagation for weight updates. To carry out the design space evaluation of the LSM for optimal hardware performance, several …

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Security Concerns On Machine Learning Solutions For 6g Networks In Mmwave Beam Prediction, Ferhat Ozgur Catak, Murat Kuzlu, Evren Catak, Umit Cali, Devrim Unal Jan 2022

Security Concerns On Machine Learning Solutions For 6g Networks In Mmwave Beam Prediction, Ferhat Ozgur Catak, Murat Kuzlu, Evren Catak, Umit Cali, Devrim Unal

Engineering Technology Faculty Publications

6G – sixth generation – is the latest cellular technology currently under development for wireless communication systems. In recent years, machine learning (ML) algorithms have been applied widely in various fields, such as healthcare, transportation, energy, autonomous cars, and many more. Those algorithms have also been used in communication technologies to improve the system performance in terms of frequency spectrum usage, latency, and security. With the rapid developments of ML techniques, especially deep learning (DL), it is critical to consider the security concern when applying the algorithms. While ML algorithms offer significant advantages for 6G networks, security concerns on artificial …

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Effect Of Freestream Noise On Hypersonic Crossflow-Induced Boundary-Layer Transition, Andrew Bustard, Thomas J. Juliano, Harrison B. Yates, Mark Noftz, Joseph Jewell Jan 2022

Effect Of Freestream Noise On Hypersonic Crossflow-Induced Boundary-Layer Transition, Andrew Bustard, Thomas J. Juliano, Harrison B. Yates, Mark Noftz, Joseph Jewell

Publications

Between the windward and leeward rays, boundary-layer transition on cones at angle of attack in hypersonic flow is dominated by the crossflow instability [1, 2], wherein stationary and/or traveling crossflow vortices develop and can breakdown into turbulence [3]. Stationary crossflow modes are caused by surface roughness or any other steady forcing and produce disturbances that are fixed in place on the surface [3–6]. Stationary vortices create a generalized inflection point in the velocity profile that is inviscidly unstable [7]. This distorted mean flow develops secondary instabilities, whose growth and breakdown is one path to turbulence [8–10]. Traveling crossflow modes are …

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Stress Classification Using Deep Learning With 1d Convolutional Neural Networks, Abdulrazak Yahya Saleh, Lau Khai Xian Dec 2021

Stress Classification Using Deep Learning With 1d Convolutional Neural Networks, Abdulrazak Yahya Saleh, Lau Khai Xian

Knowledge Engineering and Data Science

Stress has been a major problem impacting people in various ways, and it gets serious every day. Identifying whether someone is suffering from stress is crucial before it becomes a severe illness. Artificial Intelligence (AI) interprets external data, learns from such data, and uses the learning to achieve specific goals and tasks. Deep Learning (DL) has created an impact in the field of Artificial Intelligence as it can perform tasks with high accuracy. Therefore, the primary purpose of this paper is to evaluate the performance of 1D Convolutional Neural Networks (1D CNNs) for stress classification. A Psychophysiological stress (PS) dataset …

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Evaluating Deep-Learning Models For Debris-Covered Glacier Mapping, Zhiyuan Xie, Vijayan K. Asari, Umesh K. Haritashya Dec 2021

Evaluating Deep-Learning Models For Debris-Covered Glacier Mapping, Zhiyuan Xie, Vijayan K. Asari, Umesh K. Haritashya

Electrical and Computer Engineering Faculty Publications

In recent decades, mountain glaciers have experienced the impact of climate change in the form of accelerated glacier retreat and other glacier-related hazards such as mass wasting and glacier lake outburst floods. Since there are wide-ranging societal consequences of glacier retreat and hazards, monitoring these glaciers as accurately and repeatedly as possible is important. However, the accurate glacier boundary, especially the debriscovered glacier (DCG) boundary, which is one of the primary inputs in many glacier analyses, remains a challenge even after many years of research using conventional remote sensing methods or machine-learning methods. The GlacierNet, a deep-learning-based approach, utilized the …

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A Comprehensive Analysis Of Reward Function For Adaptive Traffic Signal Control, Abu Rafe Md Jamil, Naushin Nower Dec 2021

A Comprehensive Analysis Of Reward Function For Adaptive Traffic Signal Control, Abu Rafe Md Jamil, Naushin Nower

Knowledge Engineering and Data Science

Adaptive traffic control systems (ATCS) can play an essential role in reducing traffic congestion in urban areas. The main challenge for ATSC is to determine the proper signal timing. Recently, Deep Reinforcement Learning (DRL) has been used to determine proper signal timing. However, the success of the DRL algorithm depends on the appropriate reward function design. There exist various reward functions for ATSC in the existing research. This research presents a comprehensive analysis of the widely used reward function. The pros and cons of various reward algorithms were discussed, and experimental analysis shows that the multi-objective reward function enhances the …

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Similarity Identification Of Large-Scale Biomedical Documents Using Cosine Similarity And Parallel Computing, Merlinda Wibowo, Christoph Quix, Nur Syahela Hussien, Herman Yuliansyah, Faisal Dharma Adhinata Dec 2021

Similarity Identification Of Large-Scale Biomedical Documents Using Cosine Similarity And Parallel Computing, Merlinda Wibowo, Christoph Quix, Nur Syahela Hussien, Herman Yuliansyah, Faisal Dharma Adhinata

Knowledge Engineering and Data Science

Document similarity computation is an important research topic in information retrieval, and it is a crucial issue for automatic document categorization. The similarity value is between 0 and 1, then the closest value to 1 is represented both documents is considered more relevant, vice versa. However, the large scale of textual information has created the problem of finding the relevance level between documents. Therefore, the relevance between mesh heading text in the PubMed documents is higher than the relevance of the abstract text in the PubMed documents. Furthermore, parallel computing is implemented to speed up the large-scale documents similarity identification …

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Recognition Of Handwritten Javanese Script Using Backpropagation With Zoning Feature Extraction, Anik Nur Handayani, Heru Wahyu Herwanto, Katya Lindi Chandrika, Kohei Arai Dec 2021

Recognition Of Handwritten Javanese Script Using Backpropagation With Zoning Feature Extraction, Anik Nur Handayani, Heru Wahyu Herwanto, Katya Lindi Chandrika, Kohei Arai

Knowledge Engineering and Data Science

Backpropagation is part of supervised learning, in which the training process requires a target. The resulting error is transmitted back to the units below in its training process. Backpropagation can solve complicated problems because it consumes less memory than other algorithms. In addition, it also can produce solutions with a low error rate while executing less time. In image pattern recognition, backpropagation can be utilized for cultural preservation in many places worldwide, including Indonesia. It is used to recognize picture patterns in Javanese script writings. This study concluded that feature extraction approaches, zoning, and backpropagation could be utilized to distinguish …

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Parallel Approach Of Adaptive Image Thresholding Algorithm On Gpu, Adhi Prahara, Andri Pranolo, Nuril Anwar, Yingchi Mao Dec 2021

Parallel Approach Of Adaptive Image Thresholding Algorithm On Gpu, Adhi Prahara, Andri Pranolo, Nuril Anwar, Yingchi Mao

Knowledge Engineering and Data Science

Image thresholding is used to segment an image into background and foreground using a given threshold. The threshold can be generated using a specific algorithm instead of a pre-defined value obtained from observation or experiment. However, the algorithm involves per pixel operation, histogram calculation, and iterative procedure to search the optimum threshold that is costly for high-resolution images. In this research, parallel implementations on GPU for three adaptive image thresholding methods, namely Otsu, ISODATA, and minimum cross-entropy, were proposed to optimize their computational times to deal with high-resolution images. The approach involves parallel reduction and parallel prefix sum (scan) techniques …

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A Comparative Study Of Machine Learning-Based Approach For Network Traffic Classification, Kien Trang, An Hoang Nguyen Dec 2021

A Comparative Study Of Machine Learning-Based Approach For Network Traffic Classification, Kien Trang, An Hoang Nguyen

Knowledge Engineering and Data Science

Internet usage has increased rapidly and become an essential part of human life, corresponding to the rapid development of network infrastructure in recent years. Thus, protecting users’ confidential information when joining the global network becomes one of the most significant considerations. Even though multiple encryption algorithms and techniques have been applied in different parties, including internet providers, and web hosting, this situation also allows the hacker to attack the network system anonymously. Therefore, the significance of classifying network data streams to improve network system quality and security is attracting increasing study interests. This work introduces a machine learning-based approach to …

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Cnn Based Face Recognition System For Patients With Down And William Syndrome, Endang Setyati, Suharyono Az, Subroto Prasetya Hudiono, Fachrul Kurniawan Dec 2021

Cnn Based Face Recognition System For Patients With Down And William Syndrome, Endang Setyati, Suharyono Az, Subroto Prasetya Hudiono, Fachrul Kurniawan

Knowledge Engineering and Data Science

Down syndrome, also known as trisomy genetic condition, is a genetic disorder that affects many people. Williams syndrome is a hereditary disorder that can affect anyone at birth. It marks medical and cognitive issues, such as cardiovascular illness, developmental delays, and learning impairments. This is accompanied by exceptional verbal abilities, a gregarious attitude, and a passion for music. Down syndrome and William Syndrome are both genetic illnesses. However, it can be distinguished from the arrangement of chromosome 21. Down syndrome and William syndrome can also be identified by recognizing faces, or facial characteristics, such as observing particular facial features. Therefore, …

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Melanoma Classification Based On Simulated Annealing Optimization Neural Network, Edi Jaya Kusuma, Ika Pantiawati, Sri Handayani Dec 2021

Melanoma Classification Based On Simulated Annealing Optimization Neural Network, Edi Jaya Kusuma, Ika Pantiawati, Sri Handayani

Knowledge Engineering and Data Science

Technology development in image processing and artificial intelligence leads to the high demand for smart systems, especially in the health sector. Cancer is one of the diseases with the highest mortality cases worldwide. Melanoma is one of the cancers commonly caused by high exposure to UV light. The earliest the melanoma is identified, the higher the patient's chance of recovering. Therefore, this study proposes melanoma detection based on BPNN optimized by a simulated annealing algorithm. This research utilizes PH2 dermoscopic image data containing 200 color digital images in BMP format. The data is processed using color feature extraction techniques to …

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Analysis Of Deep Learning Methods For Wired Ethernet Physical Layer Security Of Operational Technology, Lucas Torlay Dec 2021

Analysis Of Deep Learning Methods For Wired Ethernet Physical Layer Security Of Operational Technology, Lucas Torlay

All Theses

The cybersecurity of power systems is jeopardized by the threat of spoofing and man-in-the-middle style attacks due to a lack of physical layer device authentication techniques for operational technology (OT) communication networks. OT networks cannot support the active probing cybersecurity methods that are popular in information technology (IT) networks. Furthermore, both active and passive scanning techniques are susceptible to medium access control (MAC) address spoofing when operating at Layer 2 of the Open Systems Interconnection (OSI) model. This thesis aims to analyze the role of deep learning in passively authenticating Ethernet devices by their communication signals. This method operates at …

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Resampling And Super-Resolution Of Hexagonally Sampled Images Using Deep Learning, Dylan Flaute, Russell C. Hardie, Hamed Elwarfalli Oct 2021

Resampling And Super-Resolution Of Hexagonally Sampled Images Using Deep Learning, Dylan Flaute, Russell C. Hardie, Hamed Elwarfalli

Electrical and Computer Engineering Faculty Publications

Super-resolution (SR) aims to increase the resolution of imagery. Applications include security, medical imaging, and object recognition. We propose a deep learning-based SR system that takes a hexagonally sampled low-resolution image as an input and generates a rectangularly sampled SR image as an output. For training and testing, we use a realistic observation model that includes optical degradation from diffraction and sensor degradation from detector integration. Our SR approach first uses non-uniform interpolation to partially upsample the observed hexagonal imagery and convert it to a rectangular grid. We then leverage a state-of-the-art convolutional neural network (CNN) architecture designed for SR …

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A Unified Framework Of Deep Learning-Based Facial Expression Recognition System For Diversified Applications, Sanoar Hossain, Saiyed Umer, Vijayan K. Asari, Ranjeet Kumar Rout Oct 2021

A Unified Framework Of Deep Learning-Based Facial Expression Recognition System For Diversified Applications, Sanoar Hossain, Saiyed Umer, Vijayan K. Asari, Ranjeet Kumar Rout

Electrical and Computer Engineering Faculty Publications

This work proposes a facial expression recognition system for a diversified field of appli- cations. The purpose of the proposed system is to predict the type of expressions in a human face region. The implementation of the proposed method is fragmented into three components. In the first component, from the given input image, a tree-structured part model has been applied that predicts some landmark points on the input image to detect facial regions. The detected face region was normalized to its fixed size and then down-sampled to its varying sizes such that the advantages, due to the effect of multi-resolution …

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On Communication For Distributed Babai Point Computation, Maiara F. Bollauf, Vinay A. Vaishampayan, Sueli I.R. Costa Jul 2021

On Communication For Distributed Babai Point Computation, Maiara F. Bollauf, Vinay A. Vaishampayan, Sueli I.R. Costa

Publications and Research

We present a communication-efficient distributed protocol for computing the Babai point, an approximate nearest point for a random vector X∈Rn in a given lattice. We show that the protocol is optimal in the sense that it minimizes the sum rate when the components of X are mutually independent. We then investigate the error probability, i.e. the probability that the Babai point does not coincide with the nearest lattice point, motivated by the fact that for some cases, a distributed algorithm for finding the Babai point is sufficient for finding the nearest lattice point itself. Two different probability models for X …

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Computational Frameworks For Multi-Robot Cooperative 3d Printing And Planning, Laxmi Prasad Poudel Jul 2021

Computational Frameworks For Multi-Robot Cooperative 3d Printing And Planning, Laxmi Prasad Poudel

Graduate Theses and Dissertations

This dissertation proposes a novel cooperative 3D printing (C3DP) approach for multi-robot additive manufacturing (AM) and presents scheduling and planning strategies that enable multi-robot cooperation in the manufacturing environment. C3DP is the first step towards achieving the overarching goal of swarm manufacturing (SM). SM is a paradigm for distributed manufacturing that envisions networks of micro-factories, each of which employs thousands of mobile robots that can manufacture different products on demand. SM breaks down the complicated supply chain used to deliver a product from a large production facility from one part of the world to another. Instead, it establishes a network …

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Forecasting Stock Exchange Data Using Group Method Of Data Handling Neural Network Approach, Marzieh Faridi Masouleh, Ahmad Bagheri Jul 2021

Forecasting Stock Exchange Data Using Group Method Of Data Handling Neural Network Approach, Marzieh Faridi Masouleh, Ahmad Bagheri

Knowledge Engineering and Data Science

The increasing uncertainty of the natural world has motivated computer scientists to seek out the best approach to technological problems. Nature-inspired problem solving approaches include meta-heuristic methods that are focused on evolutionary computation and swarm intelligence. One of these problems significantly impacting information is forecasting exchange index, which is a serious concern with the growth and decline of stock as there are many reports on loss of financial resources or profitability. When the exchange includes an extensive set of diverse stock, particular concepts and mechanisms for physical security, network security, encryption, and permissions should guarantee and predict its future needs. …

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Backpropagation Neural Network With Combination Of Activation Functions For Inbound Traffic Prediction, Purnawansyah Purnawansyah, Haviluddin Haviluddin, Herdianti Darwis, Huzain Azis, Yulita Salim Jul 2021

Backpropagation Neural Network With Combination Of Activation Functions For Inbound Traffic Prediction, Purnawansyah Purnawansyah, Haviluddin Haviluddin, Herdianti Darwis, Huzain Azis, Yulita Salim

Knowledge Engineering and Data Science

Predicting network traffic is crucial for preventing congestion and gaining superior quality of network services. This research aims to use backpropagation to predict the inbound level to understand and determine internet usage. The architecture consists of one input layer, two hidden layers, and one output layer. The study compares three activation functions: sigmoid, rectified linear unit (ReLU), and hyperbolic Tangent (tanh). Three learning rates: 0.1, 0.5, and 0.9 represent low, moderate, and high rates, respectively. Based on the result, in terms of a single form of activation function, although sigmoid provides the least RMSE and MSE values, the ReLu function …

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Do Missing Link Community Smell Affect Developers Productivity: An Empirical Study, Toukir Ahammed, Sumon Ahmed, Mohammed Shafiul Alam Khan Jul 2021

Do Missing Link Community Smell Affect Developers Productivity: An Empirical Study, Toukir Ahammed, Sumon Ahmed, Mohammed Shafiul Alam Khan

Knowledge Engineering and Data Science

Missing link smell occurs when developers contribute to the same source code without communicating with each other. Existing studies have analyzed the relationship of missing link smells with code smell and developer contribution. However, the productivity of developers involved in missing link smell has not been explored yet. This study investigates how productivity differs between smelly and non-smelly developers. For this purpose, the productivity of smelly and non-smelly developers of seven open-source projects are analyzed. The result shows that the developers not involved in missing link smell have more productivity than the developers involved in smells. The observed difference is …

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Indonesian Sentence Boundary Detection Using Deep Learning Approaches, Joan Santoso, Esther Irawati Setiawan, Christian Nathaniel Purwanto, Fachrul Kurniawan Jul 2021

Indonesian Sentence Boundary Detection Using Deep Learning Approaches, Joan Santoso, Esther Irawati Setiawan, Christian Nathaniel Purwanto, Fachrul Kurniawan

Knowledge Engineering and Data Science

Detecting the sentence boundary is one of the crucial pre-processing steps in natural language processing. It can define the boundary of a sentence since the border between a sentence, and another sentence might be ambiguous. Because there are multiple separators and dynamic sentence patterns, using a full stop at the end of a sentence is sometimes inappropriate. This research uses a deep learning approach to split each sentence from an Indonesian news document. Hence, there is no need to define any handcrafted features or rules. In Part of Speech Tagging and Named Entity Recognition, we use sequence labeling to determine …

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Face Images Classification Using Vgg-Cnn, I Nyoman Gede Arya Astawa, Made Leo Radhitya, I Wayan Raka Ardana, Felix Andika Dwiyanto Jul 2021

Face Images Classification Using Vgg-Cnn, I Nyoman Gede Arya Astawa, Made Leo Radhitya, I Wayan Raka Ardana, Felix Andika Dwiyanto

Knowledge Engineering and Data Science

Image classification is a fundamental problem in computer vision. In facial recognition, image classification can speed up the training process and also significantly improve accuracy. The use of deep learning methods in facial recognition has been commonly used. One of them is the Convolutional Neural Network (CNN) method which has high accuracy. Furthermore, this study aims to combine CNN for facial recognition and VGG for the classification process. The process begins by input the face image. Then, the preprocessor feature extractor method is used for transfer learning. This study uses a VGG-face model as an optimization model of transfer learning …

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Detection Of Disease And Pest Of Kenaf Plant Based On Image Recognition With Vggnet19, Diny Melsye Nurul Fajri, Wayan Firdaus Mahmudy, Titiek Yulianti Jul 2021

Detection Of Disease And Pest Of Kenaf Plant Based On Image Recognition With Vggnet19, Diny Melsye Nurul Fajri, Wayan Firdaus Mahmudy, Titiek Yulianti

Knowledge Engineering and Data Science

One of the advantages of Kenaf fiber as an environmental management product that is currently in the center of attention is the use of Kenaf fiber for luxury car interiors with environmentally friendly plastic materials. The opportunity to export Kenaf fiber raw material will provide significant benefits, especially in the agricultural sector in Indonesia. However, there are problems in several areas of Kenaf's garden, namely plants that are attacked by diseases and pests, which cause reduced yields and even death. This problem is caused by the lack of expertise and working hours of extension workers as well as farmers' knowledge …

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