Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison Performance of C4.5, Naïve Bayes and K-Nearest Neighbor in Determination Drug Rehabilitation

The popular data mining classification algorithm is C4. 5, Naïve Bayes and K-Nearest Neighbor (KNN). To be able to choose the best algorithm one of them can be measured from the level of accuracy, error rate and recall. This can be done by evaluation comparing several algorithms with one model of evaluation techniques. The data used for algorithm evaluation is taken from data of BNN drug rehabilitation of East Kalimantan Province with 550 data, which then divided into training data and data testing. The purpose of this research is to find the best algorithm in determining drug rehabilitation using a K-fold cross-validation algorithm with K = 10 for C4.5, Naïve Bayes and KNN algorithm. The research variables consist of medical status, employment status, work pattern, employment scale, duration of drug use, drug status, legal status, social status, psychiatric status, and rehabilitation status. Based on the results of the analysis by measuring the performance of the three algorithms using the confusion matrix method, with accuracy, error rate and recall, it is known that the best algorithm is Naïve Bayes with an accuracy percentage of 80.55% error rate of 19.45% and 70.10% recall.
Enhancement Objective Q-Learning MAC for Emergency Transmission

The emergency occurrences are unpredictable. In these circumstances, the successful transmission of emergency data is important to wireless sensor networks. We propose enhancement objective Q-Learning MAC (eOQ-MAC) for emergency transmission, which is characterized by its normal-time-slot & emergency-time-slot superframe and enhancement objective Q-Learning. In eOQ-MAC, one node can also use emergency time slots, even when it has no emergency data. However, when one node has emergency data to transmit, then it autonomously uses emergency time slots. If there are data collisions in emergency time slots, the nodes with no emergency data leave their emergency time slots. This previous process is realized by enhancement objective Q-Learning. From a superframe perspective, this is good to improve the reliability of emergency transmission, while simultaneously ensuring each node's fairness and enhancing the utilization rate of emergency time slots. The packet loss rate of eOQ-MAC is lower than CSMA/CA's 79.6%, S-MAC's 79.7%, and ALOHA-Q's 48.9%, only for emergency data transmission. In this paper, the emergency transmission is the enhancement objective of our proposed Q- Learning. Due to various circumstances, the enhancement objective of our Q-Learning should be modified to extend its applications.
Clustering Wireless Sensor Networks Using ImDE Algorithm With LEACH Protocol

Wireless sensor network is an interconnection of devices termed as sensors which are autonomous. The performance of WSN is related to the assignment of sensors in the region of interest. Optimising the energy consumption in wireless sensor networks (WSN) is a prevailing challenge. Such issues of WSN are addressed using evolutionary approaches. In this work, a modification of Differential Evolution approach termed as Improved Differential Evolution (ImDE) algorithm is introduced. This algorithm is then applied to find the cluster head in the network. Using these cluster heads in Low Energy Adaptive Clustering Hierarchy (LEACH) protocol, the WSN is clustered by optimising the overall energy consumed.
A Heart Rate Monitoring and Activities Recognition System for Badminton Training

In this study, we developed an activities recognition system with a heart rate measurement (HRM) module for badminton training. Combining machine learning, data analysis, mobile computing, and wearable devices technology, we expect that through modern technologies, the specific badminton training efficiency can be increased and the performance in the competitions can be further improved.
A Superior Arabic Text Categorization Deep Model (SATCDM)

Categorizing Arabic text documents is considered an important research topic in the field of Natural Language Processing (NLP) and Machine Learning (ML). The number of Arabic documents is tremendously increasing daily as new web pages, news articles, social media contents are added. Hence, classifying such documents in specific classes is of high importance to many people and applications. Convolutional Neural Network (CNN) is a class of deep learning that has been shown to be useful for many NLP tasks, including text translation and text categorization for the English language. Word embedding is a text representation currently used to represent text terms as real-valued vectors in vector space that represent both syntactic and semantic traits of text. Current research studies in classifying Arabic text documents use traditional text representation such as bag-of-words and TF-IDF weighting, but few use word embedding. Traditional ML algorithms have already been used in Arabic text categorization, and good results are achieved. In this study, we present a Multi-Kernel CNN model for classifying Arabic news documents enriched with n-gram word embedding, which we call A Superior Arabic Text Categorization Deep Model (SATCDM). The proposed solution achieves very high accuracy compared to current research in Arabic text categorization using 15 of freely available datasets. The model achieves an accuracy ranging from 97.58% to 99.90%, which is superior to similar studies on the Arabic document classification task.
Characterization and Modeling of Single Charge Trapping in MOS Transistors

Charging and discharging of single defects in MOS transistors seriously affects the performance of integrated devices. To identify the chemical structure of such defects density functional theory calculations can be carried out. For monitoring the impact of the defects on the device behavior the drain-source current through the device is measured and analyzed. The required data is typically recorded by applying voltage sweep measurements, noise/random telegraph noise (RTN) and measure-stress-measure (MSM) experiments. To explain the observed behavior and the impact of single defects on the electrical device characteristics physics-based defect models in combination with device simulators are often employed. After thorough calibration of the models employing detailed experimental data accurate time-to-failure estimations of MOS devices can be made. In this work the most frequently used electrical characterization techniques and advances in physical defect modeling applied to integrated transistors is discussed.
Improved Artificial Bee Colony Using Sine-Cosine Algorithm for Multi-Level Thresholding Image Segmentation

Multilevel-thresholding is an efficient method used in image segmentation. This paper presents a hybrid meta-heuristic approach for multi-level thresholding image segmentation by integrating both the artificial bee colony (ABC) algorithm and the sine-cosine algorithm (SCA). The proposed algorithm, called ABCSCA, is applied to segment images and it utilizes Otsu’s function as the objective function. The proposed ABCSCA uses ABC to optimize the threshold and to reduce the search region. Thereafter, the SCA algorithm uses the output of ABC to determine the global optimal solution, which represents the thresholding values. To evaluate the performance of the proposed ABCSCA, a set of experimental series is performed using nineteen images. In the first experimental series, the proposed ABCSCA is assessed at the low threshold levels and compared with the ABC and SCA as traditional methods. Moreover, the second experimental series aims to evaluate the ABCSCA at high threshold levels and it is compared with six algorithms in addition to the SCA and ABC. Besides, the proposed method is evaluated using the fuzzy entropy. The results demonstrate the effectiveness of the proposed algorithm and showed that it outperforms other algorithms in terms of performance measures, such as Peak Signal-to-Noise Ratio (PSNR) and the Structural Similarity Index (SSIM).
Self Scheduling Based on Hexagonal Chebysev Gaussian and Discrete Time Organized Mapping in Cloud

Cloud Computing (CC) has become an appealing computing criterion in both academic and business establishments. Fault tolerance is the key challenge faced by the CSP to provide guaranteed service to its users. Prior works proposed various algorithms for guaranteeing fault tolerance using job scheduling by assigning deadlines via time sliding (TS) and bandwidth scaling (BS). Job scheduling has proven to be an effective method to reduce fault occurrence and to address scalable user requests by balancing the incoming load. This paper proposes Hexagonal Chebyshev Gaussian and Discrete Time Organized Map-based (HCG-DTOM) job scheduling method which is an adaptive fault tolerance method based on Self organizing map. The HCG-DTOM method involves four steps. They are Hexagonal Lattice Structure Initialization model that performs initialization of cloud users, jobs to be assigned, virtual machines and job scheduler. Second, the virtual manager checks resource availability for a given set of input jobs using Chebyshev Discriminant Competitive model. Third, scheduling is performed by the job scheduler via Gaussian Neighbourhood Cooperative model. Finally, the resources are updated with the corresponding jobs for the appropriate cloud users are performed using the Discrete Time Adaptation model.
Flexible Function Splitting and Resource Allocation in C-RAN for Delay Critical Applications

The concept of cloud radio access network (C-RAN) architecture is being proposed to fully meet the requirements of 5G mobile networks. Thanks to the centralized cloud baseband unit (BBU), C-RAN reduces the energy consumption and cost of deployment significantly. However, it suffers from stringent fronthaul capacity and latency which are substantial in delay critical applications. Splitting up the processing functionalities between the control unit (CU) and distributed units (DUs) can mitigate fronthaul load and relax their requirements with the expense of an increase in power consumption. In this paper, we investigate joint access and fronthaul resource allocation problem for delay critical applications where the objective is minimizing the sum of normalized total power and fronthaul bandwidth consumption. We consider a downlink scenario and incorporate the total end-to-end delay components. For simplicity, linear models are assumed between function splitting (FS) levels and decreased fronhaul load as well as increased processing power. Different delay requirements affect our objective function and enforce a different FS level. We establish a flexible decision about the best FS level, which minimize our objective. Simulation results demonstrate that the delay constraint has a significant impact on the required fronthaul bandwidth and power consumption, which are directly related to the cost of the network. Moreover, flexible selecting function split level can achieve up to 40% gain in reducing the total utility (i.e., the sum of normalized total power and fronthaul required bandwidth).
Investigation on Distributed Joint Optimization of User Association and ICIC Based on PF Criteria in Small Cell Deployments

To support the continuous growth of mobile traffic, small cell deployments are promising. In small cell deployments, combined usage of user association (UA) and inter-cell interference coordination (ICIC) is inevitable, which requires joint optimization of both techniques. Therefore, we propose joint optimization of UA and ICIC via distributed control. In our distributed control method, “distributed” control units (DCUs) were located at the macro base stations (BSs). Each DCU collected achievable data rates of users that were connecting to the macro BS and low-power (LP) BSs within the macro coverage. To obtain the high performance similar to centralized control, achievable data rates of users in the neighboring macro BSs were collected via the backhaul. Furthermore, we showed the effectiveness of our proposed scheme.
Multiple Access Channels with Byzantine Users

Communication over a three-user multiple access channel (MAC) is studied when any one of the users may behave adversarially. The capacity region is characterized for randomized codes (where each user shares an independent secret key with the receiver). The capacity region for deterministic codes is also studied. Necessary conditions including a new non-symmetrizability condition is obtained for this capacity region to be non-trivial. It is shown that when none of the users are symmetrizable, the randomized coding capacity region is also achievable with deterministic codes. This is analogous to the result of Ahlswede and Cai (1991) for arbitrarily varying MAC.
Deep Feature Weighting Based on Genetic Algorithm and Naïve Bayes for Twitter Sentiment Analysis

In recent years, users' have expressed their opinion about certain product or service, especially in microblogging sites like Twitter. The analysis of opinion or sentiment is important for organizations to identify correct measures to improve their products or services. To analyze sentiments, the Naive Bayes (NB) model is one of the more popular methods due to low computational time and understandability. However, NB assumes that each feature does not have dependencies with others, which may not be fulfilled in text analysis. To reduce the effect of this assumption, Genetic Algorithm (GA) is used as a feature weighting process. Features that are highly correlated with the class label and lowly correlated with other features are given more weights. Experiments are conducted on the Twitter Airline database to validate the proposed model. Result shows that average accuracy of weighted NB model is 73.72%, compared to standard NB model's 73.55%. It is concluded that feature weighted NB has minor advantage over standard NB model.
A New Approach for EEG-Based Biometric Authentication Using Auditory Stimulation

In this paper, a new approach is followed for the human recognition task using brainwave responses to auditory stimulation. A system based on this class of brainwaves benefits extra features over conventional traits being more secure, harder to spoof, and cancelable. For this purpose, EEG signals were recorded from 21 subjects while listening to modulated auditory tones in a single- and two-session setups. Three different types of features were evaluated based on the energy and the entropy estimation of the EEG sub-band rhythms using narrow band Gaussian filtering and wavelet packet decomposition. These features are classified using discriminant analysis in identification and verification modes of authentication. Based on the achieved results, high recognition rates up to 97.18% and low error rates down to 4.3% were achieved in single session setup. Moreover, in a two-session setup, the proposed system in this paper is shown to be more time-permanent in comparison to previous works.
Coordination via Shared Randomness

We study a distributed sampling problem where a set of processors want to output correlated sequences of random variables with the help of a coordinator which has access to several independent sources of randomness. Each processor has access to a subset of these sources. When these subsets are pairwise disjoint (individually shared randomness model), we characterize the rate of communication required from the coordinator to the processors over a multicast link. We also give an upper bound on the communication rate for the randomness-on-the-forehead model where each processor observes all but one source of randomness. For the general model, we completely characterize the trade-off region between communication and shared randomness rates when all the processors wish to output the same random sequence.
Action Recognition from Depth Video Sequences Using Microsoft Kinect

This paper provides a fast and effective method for understanding human actions. The object performs various actions such as sitting, hand-wave, theft, and walking. The movements are captured in real time using the Microsoft Kinect sensor where the recorded video is in the mode of depth. Individual frames are taken from the video of specific action and SURF characteristics are extracted based on the frames interest points. A common approach known as the Bag of features method is used for recognition purposes, which uses k-means clustering method to create a visual vocabulary by reducing the number of features by quantizing, which in turn makes the results more accurate. For the classification of the computationally efficient depth features obtained, a multi-class SVM classifier is used. The overall classification accuracy is around 97%
Fano Resonance in Deuterated Silicon Nitride Based on Fabry-Perot Cavity Coupled Microring Resonator

We demonstrate Fano resonance in shallow-etching grating-based Fabry-Perot (FP) cavity coupled microring resonator on a deuterated silicon nitride platform. The measured maximal slope rate (SR) of single Fano resonance is 1542.2 dB/nm. The results are important for many different photonics applications.
An Effective Approach of Extracting Local Documents from the Distributed Representation of Text using Document Embedding and Latent Semantic Analysis

Document retrieval is the process of extracting the relevant documents on the basis of the defined query. The main problem is on finding the related document based on the local representation of the query. Here local representation means, for e.g. if you want to search for documents related to “bank”. “Bank” will have two representation “bank of a river” (distributed representation) or “Saving Bank” (local representation). But we need documents related to “Saving Bank” which belongs to the local representation of the query “bank”. This paper proposes a new model named Latent Semantic Analysis (LSA) and Document Embedding's to find the relevant documents. This is the initial attempt to combine the document embedding's vectors with LSA All neural embedding models learn distributed representation of text and match the results in the latent semantic space on a given query, but searching documents from the distributed representation will lose the relevance of local representation of a given query. We propose a novel information retrieval system, which uses doc2vec model to give top N similar documents with a relevant ranking using Latent Semantic Indexing to give the top K (documents score is greater than a soft threshold) documents which are the local representation of given query. We can use these K documents to find the most similar ones. We can show that this ‘dual’ combination performs better than other traditional information retrieval algorithm or recently developed neural network models
A Novel Method of Detecting Chinese Rendered Text on Tilted Screen of Mobile Devices

Due to the development of the Internet, screen rendered text detection and recognition have wide applications such as screen capture translation, medical text reading, screen text assisted reading and so on. A large number of screen-rendered attentions have been paid to the English text detection, multi-language scene detection and indefinite length recognition. However, these studies mainly focus on getting the screen rendered image from the device itself. In the actual scene, lots of screen rendered text is mainly captured by the mobile device, and text image has a certain capturing angle. Because of the tilted text line, the current method has a severe degradation in detection performance. This paper proposes a Chinese-style scene text detection method for tilting screens capturing by mobile devices. Firstly, in order to obtain robust detection performance, the Connectionist Text Proposal Network (CTPN) method is employed for horizontal text detection. Secondly, to overcome the dependence of detection on horizontal text, the lightweight Oxford Visual Geometry Group network (VGGnet) is used to correct the tilted direction of the whole image. Moreover, the word width of special characters and punctuation characters are far below average value, so this paper uses the Gaussian removal method to delete the narrow word width and designed a more accurate network to recognize characters. Experiments show that based on the comprehensive scheme, this paper achieves excellent results, even in some extreme bad cases.
A brief survey on deep learning based methods for lung cancer classification using computerized tomography scans

In this paper, we present a brief but critic survey of deep learning approaches in solving the remarkable task of lung cancer detection using computerized tomography scans. This is a survey paper that is intended to give the reader the cuttingedge algorithms to solve this task. We reviewed over 20 papers related to this topic to cover the best methods to approach this problem. In addition, our work develops a review not only in the algorithm, but also in the input dataset, the computerized tomography scans. At the end, we conclude with a summary of the current state-of-the-art methods, an overall analysis of the algorithms revised and some considerations to solve the lung cancer classification task in computerized tomography.
Low-Profile Frequency-Reconfigurable LTE-CRLH Antenna for Smartphones

This paper presents a monopole antenna loaded with one frequency-reconfigurable composite right/left-handed (CRLH) unit cell. This antenna is designed by means of two varactor diodes and with changing capacitance operating at LTE700 (698-798 MHz), LTE850 (824-894 MHz), LTE900 (880-960 MHz), LTE1700 (1710-2155 MHz), LTE1800 (1710-1880 MHz), LTE1900 (1850-1990 MHz), LTE2100 (1920-2170 MHz), LTE2300 (2305-2360 MHz), LTE2500 (2496-2690 MHz), LTE2600 (2500-2690 MHz), and GPS (1176 MHz, 1227 MHz) frequencies that cover most of the LTE bands for smartphone handsets, which is a combination of two distributed and compact modes, in which the varactor diodes, as compact elements, play a significant role in reducing the antenna dimensions. By changing the capacitance, the entire frequency band of 700 MHz and 900 MHz was achieved. The CRLH structure, in turn, reduces the dimensions of the antenna and generates resonant frequencies less than the resonance frequency of the monopole. At these frequencies, the radiation patterns are quasi-omnidirectional.
Optimal Segmentation with Back-Propagation Neural Network (BPNN) Based Citrus Leaf Disease Diagnosis

In recent years, plant diseases in agriculture are mainly accountable for production loss, which leads to a huge economical loss. Citrus plants are utilized as an important part of nutrients such as vitamin C. At the same time, ‘Citrus’ disease seriously affected the growth and quality of citrus fruits. Recently, computer vision models have been commonly employed to detect and classify plant diseases. In this study, an optimal segmentation with classification model for citrus plant disease is presented. The presented model involves a set of various sub-processes. The citrus lesion spots undergo extraction using a weighted segmentation approach that takes place on improved test image. Next, different characteristics undergo codebook construction. In addition, the optimal characteristics are chosen by a hybridization model. The chosen features are provided to Back Propagation Neural Network (BPNN) for classifying citrus diseases. The presented method is validated on Citrus Disease Image Gallery Dataset. The presented model shows extraordinary results over the compared methods and also attains superior results on the applied test images.