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Different application like image segmentation, moving objects detection and objects tracking, required an object detection and segmentation techniques. Recently, these techniques become so important especially when only a specific part of image is important. This research paper presents an efficient algorithm that employed for objects detection and extraction process. This algorithm consists of a several steps and the validity of this algorithm is measured based on different denomination of Iraqi currency. These steps arrange as following: image conversion, deleting small and unnecessary objects from images, extraction of interest objects boundary, finally calculating the rotation angle automatically, and rotate image based on the calculated angle. The validity of algorithm measured on the seven denominations of Iraqi currency (250, 500, 1000, 5000, 10000, 25000, and 50000). This image saved in a database that contain 40 images for each denomination and the total images for all denominations are 280 images. After testing the validity of designed algorithm on all captured image, the algorithm shows high accuracy which equally to 99.6%. [ABSTRACT FROM AUTHOR]

Important paper-based documents exposed to forgery such as: official certificates, birth, marriage, death certificates, selling and buying documents and other legal documents is more and more serious and sophisticated. With the purposes of fraud, appropriation of property, job application and assignment in order to swindle public authorities, this forgery has led to material loss, belief deterioration as well as social instability. There are many techniques has been proposed to overcome this issue such as: ink stamps, live signatures, documented the transaction in third party like the court or notary. In this paper, it's proposed a feasible solution for forgery prevention for paper-based documents using cloud computing application. With the application of quick response bidirectional barcode and the usage of hash algorithm. The study aims at developing an electronic verification system for official and issued books (documents, endorsements, and other official books) to/from different sections of the Institute using QR technology. [ABSTRACT FROM AUTHOR]

This research presents an innovative solution to address the bandwidth limitation of microstrip reflectarray antennas. Organic substrate materials with controlled compositions have been characterized to be employed as substrate materials for microstrip reflectarrays. The three proposed materials show low dielectric permittivity values of 1.81, 1.64 and 1.84 along with loss tangents of 0.053, 0.047 and 0.057 respectively. The proposed substrate materials have been verified by modelling reflectarray unit elements in CST MWS and measured using a waveguide simulator technique. The comparison between measured and simulated results show a good agreement with promising broadband performance of 312, 340 and 207 MHz for S1, S2 and S3 substrate materials respectively. [ABSTRACT FROM AUTHOR]

In this work, we demonstrated that enhancement of electrochemical current due to redox cycling could be accomplished by paper-based biosensor without any expensive microfabrication process. The paper-based sensor had layered structure to generate higher current than a conventional one. We took advantage of the fact that the paper thickness was micrometer-sized (180um), and it defined the distance between two electrochemical electrodes on both sides of the paper. Experimental results showed signatures of the redox cycling, where the electrochemical current from low concentration molecules could be arbitrarily increased by decreasing the distance between electrodes. Such a structure was advantageous for detecting target molecules at very low concentration, proposing a low-cost highly-sensitive biochemcal sensor. [ABSTRACT FROM AUTHOR]

This paper described the new system of biosensing using CMOS chip. The system was expected to be used in various circumstances because it was suitable for miniaturization compared to the conventional system. To conduct electrochemical measurements, the new system used paper fluidic channel set on the CMOS chip to transport solution to the on-chip electrodes. The materials of paper fluidic channel were only paper and silicone resin, and these were biocompatible. In experiment, we carried out simultaneous detection of glucose and ethanol in liquid sample solutions on the 5mm square CMOS chip and paper fluidic channel. Furthermore, this system can detect various target molecules in addition to glucose and ethanol, and increase number of simultaneous measurement by adding some more process to the paper and CMOS chip. [ABSTRACT FROM AUTHOR]

The trade business license certificate (SIUP) is a paper-based license to conduct trade businesses in Indonesia issued by the government. Until today, there is no mechanism for verifying the validity of document unless to verify it manually. The current paper presents a design that allows paperbased verification of the printed trade business license. It aims to provide the verification mechanism and ensure the document validity. Our design implemented digital signature with QR Code image that placed into the document and the digital certificate issued by a certification authority (CA). The proposed scheme generated 442 Bytes of data and version 14 of QR Code to scan easily by a reader device. The experimental result indicates that our scheme is easy and feasible to implement in Indonesia with guaranteed the document integrity, authentication, and nonrepudiation. [ABSTRACT FROM AUTHOR]

The price of electrocardiograph (ECG) machine on the market is very high. Currently, the technology used is still very complicated and ineffective, and the ECG machine cannot be connected to other devices. A new development of a low-cost ECG machine with a customized design was needed to integrate the machine with other devices. Therefore, the purpose of this study is to develop a low-cost ECG machine which can be connected to other devices and equipped with sensitivity and paper speed setting. So that portable ECG machines can be produced and used at small clinics in the society. In this study, the main controller of the 12 channels ECG machines was supported by ATMEGA16 microcontroller, that is available on the market at low prices. The main part of the ECG amplifier is built using a high common mode rejection ratio (CMRR) instrumentation amplifier (AD620) and a bandpass filter which the cutoff frequency for highpass filter and lowpass filter are 0.05 Hz and 100 Hz, respectively. In order to complement the previous study, some features were introduced such as selectivity and motor speed option. In this study, 10 participants are involved for data acquisition, and an ECG phantom was used to calibrate the machine. The performance of the ECG machine was evaluated using standard measurement namely relative percentage error (% error) and uncertainty (UA). The result shows that %error from all of the feature is less than 2% and the UA is 0.0 which shows that the ECG machine is feasible for diagnostic purposes. [ABSTRACT FROM AUTHOR]

An unconventional portable electrochemical gas sensor composed of a smartphone, a fingersized sensing chip and a single use paper-based enzyme electrode was proposed to detect a particular target gaseous inclusion for self-breath-analysis with ease. This attempt allowed us to monitor our physical status immediately and continuously regardless of a time, place or person due to the improved convenience, immediacy, and affordability. The custom CMOS chip with the capability of performing an amperometric determination when the power voltage supplied from the earphone jack of a smartphone was designed as an analytical device. A disposable enzyme electrode was prepared simply from a chromatography paper and a commercial carbon pencil instead of the conventional indisposable material and complex manufacturing process. The quantification of ethanol in gaseous samples was demonstrated in range from 50 to 500ppm (V/V) in accord with concentrations in exhaled breath. The response current increased linearly with increasing vapor ethanol concentration. [ABSTRACT FROM AUTHOR]

This paper presents a low cost portable medical device for biochemical sensor using CMOS chip and paper-based fluidic channel. We measured a potential produced by enzyme activity of glucose between the working and reference electrode on CMOS chip. A liquid sample is transported by paperbased fluidic channel, which is made of chromatography paper and silicone resin, and consists of the area for filtering a sample (filter layer) and that for reacting enzyme (enyzme layer). The paper-based fluidic channel is used by combining CMOS chip, and the solution with glucose is dropped from top of the paperbased fluidic channel. The concentrations of glucose are detected by potentiometry (open circuit potential time). The experimental results show that the glucose concentration is measured by CMOS chip and paper-based fluidic channel. [ABSTRACT FROM AUTHOR]

Software defined network (SDN) made with basic concepts that are different from traditional networks in controlling the network, the separation between the control layer and forwarding layer on different devices allows the administrator to adjust the control plan for all devices centralized in one action, while in traditional network, the control and forwarding layers are located in the infrastructure making network administrator must manage devices one by one. Research using single board computers on network technology provides an opportunity to implement SDN architecture. Raspberry Pi has sufficient ability. QoS results meet the ITU-T G.1010 reference which indicates that Raspberry Pi can be used on designed networks. [ABSTRACT FROM AUTHOR]

Our world today relies heavily on informatics and the internet, as computers and communications networks have increased day by day. In fact, the increase is not limited to portable devices such as smartphones and tablets, but also to home appliances such as: televisions, refrigerators, and controllers. It has made them more vulnerable to electronic attacks. The denial of service (DoS) attack is one of the most common attacks that affect the provision of services and commercial sites over the internet. As a result, we decided in this paper to create a smart model that depends on the swarm algorithms to detect the attack of denial of service in internet networks, because the intelligence algorithms have flexibility, elegance and adaptation to different situations. The particle swarm algorithm and the bee colony algorithm were used to detect the packets that had been exposed to the DoS attack, and a comparison was made between the two algorithms to see which of them can accurately characterize the DoS attack. [ABSTRACT FROM AUTHOR]

An image through the digitization process is referred to as a digital image. The quality of the digital image may be degenerating due to interferences on the acquisition, transmission, extraction, etc. This attracted the attention of many researchers to study the causes of damage to the information in the image. In addition to finding cause of image damage, the researchers also looking for ways to overcome this problem. There are many filtering techniques that have been introduced to deal the damage to the information in the image. In addition to eliminating noise from the image, filtering techniques also aims to maintain the originality of the features in the image. Among the many research papers on image filtering there is a lack of review papers which are an important to facilitate researchers in understanding the differences in each filtering technique. Additionally, it helps researchers determine the direction of research conducted based on the results of previous research. Therefore, this paper presents a review of several filtering techniques that have been developed so far. [ABSTRACT FROM AUTHOR]

This paper reports the development and switching controller design of an inverted pendulum system (IPS) platform. The euler-lagrange approach is first used to model the dynamics of the IPS which takes into account the impact of friction forces during its movements. The paper then derives a switching control method to swing the pendulum rod into the neighborhood of and stabilizing it at the equilibrium point. The implemented switching controller consists of: i) a nonlinear swing up control which brings the pendulum to the vertical position and ii) a linear stabilizing control which maintains the pendulum rod to remain in a vertical position around the neighborhood of the vertical axis. The nonlinear controller is constructed using lyapunov’s method while the linear controller is designed using linear quadratic regulator (LQR) method framework. Simulation and experimental results are presented to show the effectiveness of the proposed switching controller. [ABSTRACT FROM AUTHOR]

Frequency reconfigurable antennas are very attractive for many wireless applications. They offer many advantages such as simplicity and compactness. In this electronic paper, we propose a reconfigurable antenna operating in the S and C bands. The proposed antenna uses a BAP65-02 RF diode to switch between the ultra wideband from 2.92 to 6.19 GHz to the narrowband from 2.92 to 3.93 GHz. The ultra wideband is obtained by a partial rectangular ground plane with a symmetrical rectangular slot and the narrowband is obtained by adding a parasitic element electrically connected to the ground plane by the PIN diode when it is positively biased. This patch antenna operates in the Federal Communications Commission band (FCC) and can be used for biomedical applications such as radiometry imaging. The numerical simulation results based on the finite element method and the finite integral method show a very good agreement between them. [ABSTRACT FROM AUTHOR]

One of the most important difficulties which doctors face in diagnosing is the analysis and diagnosis of brain stroke in magnetic resonance imaging (MRI) images. Brain stroke is the interruption of blood flow to parts of the brain that causes cell death. To make the diagnosis easier for doctors, many researchers have treated MRI images with some filters by using Matlab program to improve the images and make them more obvious to facilitate diagnosis by doctors. This paper introduces a digital system using hardware concepts to clarify the brain stroke in MRI image. Field programmable gate arrays (FPGA) is used to implement the system which is divided into four phases: preprocessing, adjust image, median filter, and morphological filters alternately. The entire system has been implemented based on Zynq FPGA evaluation board. The design has been tested on two MRI images and the results are compared with the Matlab to determine the efficiency of the proposed system. The proposed hardware system has achieved an overall good accuracy compared to Matlab where it ranged between 90.00% and 99.48%. [ABSTRACT FROM AUTHOR]

Visually impaired has the limitation in interacting with another human. They usually use the sense of hearing and touching their face to recognize human. Face recognition is a technology that can be used to solve this problem. This paper develops a smart cane function by integrated face recognition feature on the cane using Haar-Like features and Eigenfaces. This paper proposed a portable, real time, and wearable product. Raspberry Pi supports portability that affects the delay and computing speed of face recognition algorithms. Utilization of Raspi camera on the eyes glasses is for wearable purposes. Voice output provides information on whether the face is caught on camera or not. This prototype works well during the face detection and recognition process. It needs 3 seconds for one-face recognized in range 0.25 until 1.5 meters from the camera, until the sound and information are generated. It needs is me 5 second for two faces recognized and 10 seconds for 3 faces recognized by a system in the same range between face and the camera. The accuracy reaches 91.67% for the up-right position face but for other position the accuracy is only 18% until 32%. [ABSTRACT FROM AUTHOR]

Breast cancer is the most common cancer in women. It has the highest incidence rate and the highest mortality rate. In recent years, the incidence of breast cancer has become more and more important, it is becoming the first tumor killer for women around the world. Early diagnosis is the most important parameter for detecting cancerous tissue to prevent serious consequences. In this electronic paper, wepresent a new design of an ultra-wide-band circular microstrip patch antenna operating in the recommended FCC band ([3.1 GHz - 10.6 GHz]) for the detection of breast tumors. The antenna is printed on an FR4 epoxy substrate with a dielectric permittivity εr = 4.4 and loss tangent tan δ = 0.02. The results obtained are largely satisfying and prove that the proposed antenna is a candidate for biomedical applications. [ABSTRACT FROM AUTHOR]

In software defect prediction, noisy attributes and high-dimensional data remain to be a critical challenge. This paper introduces a novel approach known as multi correlation-based feature selection (MCFS), which seeks to address these challenges. MCFS integrates two feature selection techniques, namely correlation-based feature selection (CFS) and correlation matrix-based feature selection (CMFS), intending to reduce data dimensionality and eliminate noisy attributes. To accomplish this, CFS and CMFS are applied independently to filter the datasets, and a weighted average of their outcomes is computed to determine the optimal feature selection. This approach not only reduces data dimensionality but also mitigates the impact of noisy attributes. To further enhance predictive performance, this paper leverages the particle swarm optimization (PSO) algorithm as a feature selection mechanism, specifically targeting improvements in the area under the curve (AUC). The evaluation of the proposed method is conducted on 12 benchmark datasets sourced from the NASA metrics data program (MDP) corpus, renowned for their noisy attributes, high dimensionality, and imbalanced class records. The research findings demonstrate that MCFS outperforms CFS and CMFS, yielding an average AUC value of 0.891, thereby emphasizing it is efficacy in advancing classification performance in the context of software defect prediction using k-nearest neighbors (KNN) classification. [ABSTRACT FROM AUTHOR]

Agile development methods have been catering the need of faster delivery of the ever-demanding domain of software engineering. These methods are able to deliver value to users and businesses via fast, reliable, and repeatable process. Planning requirements and processes takes the driving seat in a dynamic environment because the value proposition rapidly changes. This paper exhibits a systematic literature review of planning processes implemented by various agile methods in order to find the best suited agile method in terms of robust planning. It was found that Scrum is the best suited agile method for planning processes [ABSTRACT FROM AUTHOR]

An application of moving-horizon strategy for nonlinear systems with possible outliers in measurements is addressed. With the increased success of movinghorizon strategy in the state estimation for linear systems with outliers acting on the measurement, investigating the nonlinear approach is highly required. In this paper we applied the nonlinear version which has been presented in the literature in term of discrete-time linear time-invariant systems, where the applied strategy considers minimizing a least-squares functions in which each measure possibly contaminated by outlier is left out in turn and the lowest cost is propagated. The moving horizon filter effectiveness as compared with the extended Kalman filter is shown by means of simulation example and estimation error comparison. The moving horizon filter shows the feature of resisting outliers with robust estimation. [ABSTRACT FROM AUTHOR]

The rise of social media platforms has led to an increase in the flow and dissemination of information, but it has also made generating and spreading rumors easier. Rumor detection requires understanding the context and semantics of text, dealing with the evolving nature of rumors, and processing vast amounts of data in real-time. Deep learning (DL)-based techniques exhibit a higher accuracy in detecting rumors on social media compared to many traditional machine learning approaches. This study presents a systematic review of DL approaches in rumor detection, analyzing datasets, pre-processing methods, feature taxonomy, and frequently used DL methods. In the context of feature selection, we categorize features into three areas: text-based, user-based, and propagation-based. Besides, we surveyed the trends in DL models for rumor detection and classified them into convolutional neural networks (CNN), recurrent neural networks (RNN), graph neural networks (GNN), and other methods based on the model structure. It offers insights into effective algorithms and strategies, aiming to guide researchers, developers, social media users, and governments in detecting and preventing the spread of false information. The study contributes to enhancing research in this field and identifies potential areas for future exploration. [ABSTRACT FROM AUTHOR]

This paper discusses software metrics and their impact on software defect prediction values in the NASA metric data program (MDP) dataset. The NASA MDP dataset consists of four categories of software metrics: halstead, McCabe, LoC, and misc. However, there is no study showing which metrics participate in increasing the area under the curve (AUC) value of the NASA MDP dataset. This study utilizes 12 modules from the NASA MDP dataset, where these 12 modules are being tested into 14 relationships of software metrics derived from the four existing metric categories. Subsequently, classification is performed using the k-nearest neighbor (kNN) method. The research concludes that software metrics have a significant impact on the AUC value, with the LoC+McCabe+misc metrics relationship influencing the improvement of the AUC value. However, the metrics relationship that has the most impact on achieving less optimal AUC values is McCabe. Halstead metric also plays a role in decreasing the performance of other metrics. [ABSTRACT FROM AUTHOR]

The security of wireless communication systems has been the focal subject of research for more than a century. It is a topic of vital importance due to its effectiveness in many fields. Besides, the importance of sending data from one point to another without intercepting by unwanted third-party has become an essential requirement in conjunction with the widespread hacking technologies. This research paper is set out to explore a new technique used in internet of things applications that will ensure a secure wireless communication system. Additionally, it clarifies five distinct cryptographic methods: advanced encryption standard, elliptic curve cryptography, transposition, substitution, and Rivest, Shamir, and Adleman. In addition, the secure wireless communication system combines four of the prementioned techniques to build a highly secure system. Moreover, we managed to elucidate the critical points of our work on the long-range systems into a short yet informative and detailed summary. This paper will extensively cover the following topics: cryptography, cryptographic methods and techniques, software implementation, applications, and advantages of the system. [ABSTRACT FROM AUTHOR]

This article provides a detailed evaluation of cutting-edge artificial intelligence (AI) approaches and metaheuristic algorithms for optimizing wind turbine location inside wind farms. The growing need for renewable energy sources has fueled an increase in research towards efficient and sustainable wind farm designs. To address this challenge, various AI techniques, including genetic algorithms (GA), particle swarm optimization (PSO), simulated annealing, artificial neural networks (ANNs), convolutional neural networks (CNNs), and reinforcement learning, have been explored in combination with metaheuristic algorithms. The goal is to discover optimal sites for turbine placement based on a variety of parameters such as energy output, cost-effectiveness, environmental impact, and geographical restrictions. The paper examines the advantages and disadvantages of each strategy and highlights current breakthroughs in the area. This assessment adds to continuing efforts to optimize wind farm design and promote the use of clean and sustainable energy sources by offering significant insights into current advances. [ABSTRACT FROM AUTHOR]

In this paper, a proposal is made for a cryptographic algorithm designed for passive ultra-high-frequency (UHF) radio frequency identification systems. The algorithm relies on the advanced encryption standard (AES) as its fundamental encryption technique, augmented by two supplementary steps: the initial step involves generating a random key and the second is the randomization of data, this introduces an extra level of security to encryption process against attacks. The developed architecture has been optimized to minimize hardware resource consumption with faster execution speed. The algorithm has been simulated, synthesized and implemented in an xtreme digital signal processing (DSP) starter kit equipped with xilinx’s spartan-3A DSP 1800A edition and it serves the purpose of encrypting and decrypting user data on a radio frequency identification (RFID) passive tag. The main objective is to make it difficult to break the algorithm because of its multiple steps. The experimental results showed that the speed, functionality and cost of encryption and decryption make this a perfectly practical solution, providing a satisfactory level of security for today’s communications systems, or other electronic data transfer processes where security is required. [ABSTRACT FROM AUTHOR]

There is a tremendous growth in fabrication from small scale integration (SSI) to giant scale integration (GSI). It however raises a question of sustainability of Moore's law due to almost intolerable levels of power consumption. Researchers have invented a lot of methods to reduce power consumption and recent technologies are switching to reversible logic. Reversible logic has various applications in fields of computer graphics, optical information processing, quantum computing, DNA computing, ultra low power CMOS design and communication. ALU is considered to be the basic building block of a CPU in the computing environment and portability in computing system highly demands reversible logic based ALU. Modern processors usually have a word length of 32 or 64 bits. Divide and conquer approach principle cascades n number of 1 bit ALU to implement n bit ALU. Several researchers have proposed 1-bit ALU design using various reversible logic gates. This paper aims at categorizing various ways of implementation in VHDL using Xilinx ISE design suit 14.2 tool and comparative analysis of existing 1 bit ALU designs in terms of optimization metrics like power consumption, number of gates, number of constant inputs, number of garbage outputs and quantum cost. ALU realized using carry save adder block is found to be most optimum design in terms of gate count and quantum cost. [ABSTRACT FROM AUTHOR]

A comprehensive systematic study was carried out in order to identify various deep learning methods developed and used for predicting student academic performance. Predicting academic performance allows for the implementation of various preventive and supportive measures earlier in order to improve academic performance and reduce failure and dropout rates. Although machine learning schemes were once popular, deep learning algorithms are now being investigated to solve difficult predictions of student performance in larger datasets with more data attributes. Deep neural network prediction methods with clear modelling and parameter measurements formulated on publicly available and recognised datasets are the focus of the research. Widely used for academic performance prediction, backpropagation algorithms have been trained and tested with various datasets, especially those related to learning management systems (LMS) and massive open online courses (MOOC). The most widely used prediction method appears to be the standard artificial neural network approach. The long-short-term memory (LSTM) approach has been reported to achieve an accuracy of around 87 percent for temporal student performance data. The number of papers that study and improve this method shows that there is a clear rise in deep learning-based academic performance prediction over the last few years. [ABSTRACT FROM AUTHOR]

Genetic algorithm (GA) is one of popular heuristic-based optimization methods that attracts engineers and scientists for many years. With the advancement of multiand many-core technologies, GAs are transformed into more powerful tools by parallelising their core processes. This paper describes a feasibility study of implementing parallel GAs (pGAs) on a SpiNNaker. As a many-core neuromorphic platform, SpiNNaker offers a possibility to scale-up a parallelised algorithm, such as a pGA, whilst offering low power consumption on its processing and communication overhead. However, due to its small packets distribution mechanism and constrained processing resources, parallelising processes of a GA in SpiNNaker is challenging. In this paper we show how a pGA can be implemented on SpiNNaker and analyse its performance. Due to inherently numerous parameter and classification of pGAs, we evaluate only the most common aspects of a pGA and use some artificial benchmarking test functions. The experiments produced some promising results that may lead to further developments of massively parallel GAs on SpiNNaker. [ABSTRACT FROM AUTHOR]

The internet of medical things (IoMT) has transformed the healthcare sector by facilitating real-time monitoring, remote patient care, and tailored healthcare solutions. However, the challenge of upholding a high standard quality of service (QoS) in IoMT implementations remains a pressing issue. This article delves into the possibilities of utilizing 5G networks and smart techniques to optimize QoS within IoMT systems. By capitalizing on the capabilities of 5G networks, including substantial bandwidth, minimal latency, and extensive connectivity, in conjunction with intelligent methods such as machine learning and predictive analytics, this paper introduces novel strategies to enhance QoS in IoMT environments. The summary underscores the advantages of these methods in elevating network dependability, diminishing latency, enhancing data transmission efficiency, and enabling resource allocation efficiency in IoMT deployments. Additionally, it explores the potential ramifications of these developments on healthcare outcomes, patient contentment, and the overall effectiveness of the healthcare system. The conclusions propose that by maximizing QoS through 5G networks and intelligent techniques, IoMT holds the potential to significantly enhance the delivery of healthcare services, fostering a more interconnected and efficient healthcare ecosystem in the process innovation. [ABSTRACT FROM AUTHOR]

Numerous technologies and systems, including autonomous vehicles, surveillance systems, and robotic applications, rely on the capability to accurately detect pedestrians to ensure their safety. As the demand for realtime object detection continues to rise, many researchers have dedicated their efforts to developing effective and trustworthy algorithms for pedestrian recognition. By integrating learning complexity-aware cascades with an enhanced you only look once (YOLO) algorithm, the paper presents a real-time system for identifying both items and pedestrians. The performance of the proposed approach is evaluated using the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) pedestrian dataset across both the v4 and v8 versions of the YOLO framework. Prioritizing both speed and accuracy, the enhanced YOLO algorithm outperforms its baseline counterpart. The demonstrated superiority of the suggested technique on the KITTI pedestrian dataset underscores its effectiveness in real-world contexts. Furthermore, the complexity-aware learning cascades contribute to a streamlined detection model without compromising performance. When applied to scenarios requiring real-time identification of objects and individuals, the proposed method consistently delivers promising outcomes. [ABSTRACT FROM AUTHOR]

Deep learning has gained high popularity in the field of image processing and computer vision applications due to its unique feature extraction property. For this characteristic, deep learning networks used to solve different issues in computer vision applications. In this paper the issue has been raised is classification of logo of formal directors in Iraqi government. The paper proposes a multi-layer convolutional neural network (CNN) to classify and recognize these official logos by train the CNN model on several logos. The experimental show the effectiveness of the proposed method to recognize the logo with high accuracy rate about 99.16%. The proposed multi-layers CNN model proves the effectiveness to classify different logos with various conditions. [ABSTRACT FROM AUTHOR]

In the issues of signal de-noising, utilize K-SVD and other classical dictionary learning algorithm for sparse decomposition and reconstruction of signal, which cannot effectively eliminate influence of noise. The method suggested by this paper makes some improvement to the classical dictionary learning. Firstly, utilize K-SVD algorithm to make the dictionary learning; then, utilize the method of non-linear least squares to fit each atom in the dictionary and get the revised dictionary; finally, utilize the method of Particle Swarm Optimization to solve the spare representation of signal and get the reconstructed signal at last. It is proved through the experience that the de-noising effect of this paper is obvious superior to the conventional dictionary learning algorithm and is close to the effect of wavelet analytical approach. [ABSTRACT FROM AUTHOR]

Health monitoring systems based on the internet of things (IoT) improve patient well-being and reduce mortality risks. Machine learning techniques are most helpful in early fall prediction and detection. In this paper, fall prediction analysis and decision-making are done with existing benchmark clinical records. Classification techniques are incorporated to track the consistency and precision of data acquired by the IoT-based remote health monitoring for elderly people, especially those who are living alone. This work undertakes two approaches to early predicting a patient’s acute illness. The first approach has analyzed the existing benchmark patient activity data with different features. This approach builds the classification model for fall incidence with the help of machine learning models. In second approach, we collect real-time sensor data such as blood pressure and heart rate from IoT sensor gadgets which are transmitted to the prediction model for early prediction. Experimental results prove that the random forest (RF) classifiers and XGBoost provides the maximum accuracy. [ABSTRACT FROM AUTHOR]

This effort develops and uses a smart stick to help blind individuals walk more safely and avoid hazards. This research paper involved to develop smart stick for blind people to help them to walk safely by avoiding obstructions, and at emergency conditions sent their location to person in charge like a doctor or relatives to help them accordingly. Three ultrasonic sensors are used in the proposed device’s architecture to detect obstructions at three different heights, low, mid, and high obstructions using speaker to alert the blind person. An emergency message with the location of the blind person’s phone is sent through a mobile app to the doctor or person in charge whenever the designed smart stick’s emergency button is touched, or the stick is dropped down. The Arduino Uno platform, Bluetooth model, MPU-6050 3-axis gyroscope as a position sensor, and microSd card module has been used to effectively implement the stick. At the testing stage, the device gave good results. The user received notifications in the form of voice messages and vibrations from the smart stick when it detects things or obstacles in front of them. Additionally, even though they cannot directly activate it, the automated emergency condition has been detection and activated. [ABSTRACT FROM AUTHOR]

Smart cities today can utilize vehicular delay tolerant networks (VDTN) to collect data from connected-objects in the environment for various delay-tolerant applications. They can take advantage of the available intelligent transportation systems (ITS) infrastructures to deliver data to the central server. The system can also exploit multiple and diverse mobility patterns found in cities, such as privately owned cars, taxis, public buses, and trams, along with their vehicle-toeverything (V2X) communications capabilities. In the envisioned convergence between the ITS and V2X, we believe that a simple and efficient routing protocol can be deployed for the delay-tolerant data delivery, contrary to the implementation of optimized solutions that might be resource-demanding and difficult to standardize. In this paper, we analyzed the performances of four baseline VDTN routing protocols, namely: direct delivery, first contact, epidemic, and spray and wait, to understand their strengths and weaknesses. Our simulation results highlighted the trade-off between distinct approaches used by those protocols and pointed out some gaps that can be refined. This study provides new interesting ideas and arguments towards developing a simple, efficient, and high-performing routing protocol for data collection in smart cities. [ABSTRACT FROM AUTHOR]

Human activity tracking using wearable technology is an important research field of study and is employed in many applications such as healthcare, aged care, fitness, and tracking activity. Although this field has been well addressed by researchers in the last decade that most of the research is done with tightly fixed sensors (either to the body or to the tight garment), while in contrast, the majority of our daily clothes are loosely fitting so we must be interested in this fields but the main challenge in tracking/recognizing activities in loose clothing is the higher level of noise generated by the movement of clothing compared fixed sensor. This paper reviews recent research on human activity tracking techniques that use sensors that are attached or embedded in loose-fitted clothes. It is predicted from recent studies that many low-cost, reliable, and socially acceptable wearable devices will be available to aid activity tracking. Survey also predicts an increase in the interest for wearables in loose-fitting clothes due to their wide real-time applications. Most of the reviewed work based heavily on off-shelf microcontroller board and sensor, which are characterized by their moderate performance. Hence most of the reviewed work used machine learning to enhance results classification. [ABSTRACT FROM AUTHOR]

This paper introduces a method to enhance the gain of ultra-wideband (UWB) planar antennas by employing a grounded frequency-selective surface (FSS) as a reflector. The grounded FSS reflector comprises square patch structures (SPSs) of unit cells with a complete ground plane. The designed FSS utilizes miniaturized unit cells that are printed on both sides of an FR4 substrate, making it highly suitable for a wide range of applications, particularly as a reflector to optimize the gain of UWB omnidirectional radiators. This concept is demonstrated by employing a circular monopole UWB antenna as the radiator. The measured outcomes of the suggested antenna with the grounded FSS maintain a broad frequency range of bandwidth of 9.162 GHz, spanning from 2.638 GHz to 11.8 GHz. The antenna, in combination with the FSS, achieves a significant measured gain of 7.6 dBi and a high efficiency of 93% at 9.6 GHz. The attributes of the suggested UWB FSS make it an ideal solution for seamless combining with small-sized broadband circuits, thereby maximizing its effectiveness. [ABSTRACT FROM AUTHOR]

The industrial instruments found in the different industrial processes allow supervisory and control actions to be carried out. These elements are usually composed of an electronic circuit, which varies according to the specifications given by the manufacturer. Another factor that influences the design of a circuit for elements of this type is cost, since, at a lower cost, electronic elements are susceptible to fail for reasons such as electric fields, motor interference or environmental conditions. This problem limits the operation of the instrument and reduces its useful life, which causes unnecessary stops of the automated processes increasing the cost of producing a product. Some partial solutions to this problem have been obtained by means of the implementation of additional circuits such as protections or filters, although the number of unexpected stops of the process is reduced, even this type of failures occur. This paper proposes an alternative solution to the problem through a low cost prototype, which has a mechanism for rejection of disturbances that increases the accuracy and precision of an instrument compared to the low-cost instrument technology developed so far. [ABSTRACT FROM AUTHOR]

Transport sector has great impact on our daily life. Despite the huge number of vehicular models, driving process still faces many challenges due to the lack information about the roads and the surrounding sudden events, which can result in high number of accidents globally and especially in Saudi Arabia. A new technology, vehicular ad hoc networks (VANETs), has emerged to support Intelligent Transport System (ITS) and to offer advanced solutions for drivers to avoid different hazard events that occur on the road. In this paper, we discuss the multicast and broadcast communications in VANETs, Quality of Sevice (QoS) aware group addressing/managing solutions to VANETs which help in classifying different application that explore and design a new cross-layer framework, aware of high mobility and efficiency. [ABSTRACT FROM AUTHOR]

Significant improvements in terms of bandwidth of reflectarray antennas have been achieved by introduction of innovative paper substrate dielectric materials. Three differently custom composed organic dielectric substrates have been characterized for dielectric properties using a broadband technique based on open ended coax cable method. The substrates show low dielectric permittivities of 1.81, 1.63 and 1.84 along with a loss tangent of 0.053, 0.047 and 0.057. Validation of using the proposed substrates for reflectarray antenna was done by modelling and fabricating reflectarray unit elements on the three substrates. Scattering parameter analysis of unit reflectarray elements show encouraging results with a broadband frequency response of 340 MHz at a phase gradient of 0.14°/MHz. Thus the proposed substrate could serve exceptionally to address the narrow bandwidth problem in reflectarray antennas. [ABSTRACT FROM AUTHOR]

This paper presents novel loss quantization of reflectarray elements based on organic substrate materials. Three differently composed substrate materials derived from recycled materials have been characterized for their dielectric properties using a broadband analysis technique. The materials show low dielectric permittivity values of 1.81, 1.62 and 1.84 for X-band frequency range. In order to estimate the reflection loss of for the three substrates a mathematical relation has been established using empirical data generated by computer simulated models. The reliability of the proposed model has been established by simulation and fabrication of unit reflectarray rectangular patch elements on three proposed substrate substrates. A broadband frequency response has been depicted by scattering parameter analysis of unit elements with 10% fractional bandwidth of 312, 340 and 207 MHz for RCP50, RCR75 and RNP50 substrate respectively. [ABSTRACT FROM AUTHOR]

Context-aware services are attracting attention of world as the use of web services are rapidly growing. We designed an architecture of context-aware semantic web which provides on demand flexibility and scalability in extracting and mining the research papers from well-known digital libraries i.e. ACM, IEEE and SpringerLink. This paper proposes a context-aware administrations system, which supports programmed revelation and incorporation of setting dependent on Semantic Web administrations. This work has been done using the python programming language with a dedicated library for the semantic web analysis named as "Cubic-Web" on any defined dataset, in our case as we have used a dataset for extracting and studying several publications to measure the impact of context aware semantic web application on the results. We have found the average recall and averge accuracy for all the context aware research journals in our research work. Moreover, as this study is limited journal documents, other future studies can be approached by examining different types of publications using this advance research. An efficient system has been designed considering the parameters of research article meta-data to find out the papers from the web using semantic web technology. Parameters like year of publication, type of publication, number of contributors, evaluation methods and analysis method used in publication. All this data has been extracted using the designed context-aware semantic web technology. [ABSTRACT FROM AUTHOR]

Student academic accomplishment is the foremost focus of every educational institution. In developing student achievement in educational institutions, the researchers finally created a new research area, namely educational data mining (EDM). How the feature selection (FS) algorithm works is by removing unrelated data from educational datasets; therefore, this algorithm can improve the classification performance managed in EDM techniques. This research presents an analysis of the performance of the FS algorithm from the student dataset. The results received from other FS algorithms and classifiers will help other researchers to gain some best combination regarding FS algorithms and the classification. Selecting features that are relevant for student forecast models is a sensitive problem to stakeholders in education because they must make decisions based on the results of the prediction models. For the future, our paper seeks to play a decisive part while developing quality concerning education, as well as guiding different researchers in conducting educational interventions. [ABSTRACT FROM AUTHOR]

Land registration systems are very essential for property ownership management. The exited land registry systems are less efficient and timeconsuming and expose to human errors. By using blockchain technology, most of the principles of good governance in land administration such as transparency and efficiency can be fulfilled. However, there is a lack of experience in developing blockchain-based land registry systems. This paper proposes a blockchain-based adoption framework for land registry management in Malaysia. It elaborates more on developing a prototype that fulfills the main functions of current land registration by using smart contract functionalities. Also, this paper illustrates the main challenges of adopting this technology such as expertise shortage of software developers, implementation difficulties due to scalability of the land transactions, data sharing with different types of blockchain and lack of security attacks resistance. Therefore, there is a need to form an agreed-upon blockchain development platform that meet such constraints. [ABSTRACT FROM AUTHOR]

The internet of things (IoT) is rapidly expanding and improving operations in a wide range of real-world applications, from consumer IoT and enterprise IoT to manufacturing and industrial IoT (IIoT). Consumer markets, wearable devices, healthcare, smart buildings, agriculture, and smart cities are just a few examples. This paper discusses the current state of the IoT ecosystem, its primary applications and benefits, important architectural stages, some of the problems and challenges it faces, and its future. This paper explains how an appropriate IoT architecture that saves data, analyzes it, and recommends corrective action improves the process's ground reality. The IoT system architecture is divided into three layers: device, gateway, and platform. This then cascades into the four stages of the IoT architectural layout: sensors and actuators; gateways and data acquisition systems; edge IT data processing; and datacenter and cloud, which use high-end apps to collect data, evaluate it, process it, and provide remedial solutions. This elegant combination provides excellent value in automatic action. In the future, IoT will continue to serve as the foundation for many technologies. Machine learning will become more popular in the coming years as IoT networks take center stage in a variety of industries. [ABSTRACT FROM AUTHOR]

Deep residual learning frameworks have achieved great success in image classification. This article presents the use of transfer learning which is applied on mango leaf image dataset for its disease's detection. New methodology and training have been used to facilitate the easy and rapid implementation of the mango leaf disease detection system in practice. Proposed system can be used to identify the mango leaf for whether it is healthy or infected with the diseases like anthracnose or red rust. This paper describes all the steps which are considered during the experimentation and design. These steps include leaf image data collection, its preparation, data assessment by agricultural experts, and selection and training of deep neural network architectures. A deep residual framework, residual neural network (ResNET), was used to perform deep convolutional neural network training. ResNETs are easy to optimize and can achieve better accuracies. The experimental results obtained from "ResNET architectures, such as ResNet18, ResNet34, ResNet50, and ResNet101" show the accuracies from 94% to 98%. ResNET18 architecture selected from above for system design as it gives 98% accuracy for mango leaf disease's detection. System will help farmers to identify leaf diseases in quick and efficient manner and facilitate decision-making in this front. [ABSTRACT FROM AUTHOR]

This research aimed to evaluate the performance of the A Lite BERT (ALBERT), efficiently learning an encoder that classifies token replacements accurately (ELECTRA) and a robust optimized BERT pretraining approach (RoBERTa) models to support the development of the Indonesian language question and answer system model. The evaluation carried out used Indonesian, Malay and Esperanto. Here, Esperanto was used as a comparison of Indonesian because it is international, which does not belong to any person or country and this then make it neutral. Compared to other foreign languages, the structure and construction of Esperanto is relatively simple. The dataset used was the result of crawling Wikipedia for Indonesian and Open Super-large Crawled ALMAnaCH coRpus (OSCAR) for Esperanto. The size of the token dictionary used in the test used approximately 30,000 sub tokens in both the SentencePiece and byte-level byte pair encoding methods (ByteLevelBPE). The test was carried out with the learning rates of 1e-5 and 5e-5 for both languages in accordance with the reference from the bidirectional encoder representations from transformers (BERT) paper. As shown in the final result of this study, the ALBERT and RoBERTa models in Esperanto showed the results of the loss calculation that were not much different. This showed that the RoBERTa model was better to implement an Indonesian question and answer system. [ABSTRACT FROM AUTHOR]

Routing protocols are extremely important incredibly significant in data communication and computer networks. The high performance, reliability, stability, and security of the networks depend primarily on choosing the best type of dynamic routing protocol. In this paper, we evaluate and investigate the network performance for routing information protocol (RIP), enhanced interior gateway routing protocol (EIGRP), open shortest path first (OSPF), and intermediate system-to-intermediate system (IS-IS) routing protocols with three different scenarios of routes failure using the optimized network engineering tools (OPNET) simulator to determine which of the protocols is the most appropriate and effective in achieving high network performance. The results show that for large area networks, the EIGRP routing protocol gives the best network performance when all network routers are working with no failing, but when some network routers were failing to work and path failure is happening, the IS-IS link-state routing protocol works efficiently and gives the best performance. The obtained results for IS-IS protocol when failing seven routers is as: the hypertext transfer protocol (HTTP) page response time is (247.8 msec), voice delay variation is (4.19 µsec), video delay variation is (8.83 µsec) and ping request and response time is (115 msec). [ABSTRACT FROM AUTHOR]

This paper presents the design and implementation of a forest fire monitoring and warning system based on long range (LoRa) technology, a novel ultra-low power consumption and long-range wireless communication technology for remote sensing applications. The proposed system includes a wireless sensor network that records environmental parameters such as temperature, humidity, wind speed, and carbon dioxide (CO2) concentration in the air, as well as taking infrared photos. The data collected at each sensor node will be transmitted to the gateway via LoRa wireless transmission. Data will be collected, processed, and uploaded to a cloud database at the gateway. An Android smartphone application that allows anyone to easily view the recorded data has been developed. When a fire is detected, the system will sound a siren and send a warning message to the responsible personnel, instructing them to take appropriate action. Experiments in Tram Chim Park, Vietnam, have been conducted to verify and evaluate the operation of the system. [ABSTRACT FROM AUTHOR]

Recommendation systems (RS) are crucial for social networking sites. Without it, finding precise products is harder. However, existing systems lack adequate efficiency, especially with big data. This paper presents a prototype cloud-based recommendation system for processing big data. The proposed work is implemented by utilizing the matrix factorization method with three approaches. In the first approach, singular value decomposition (SVD) is used, which is an old and traditional recommendation technique. The second recommendation approach is fine-tuned using the alternating least squares (ALS) algorithm with Apache Spark. Finally, the deep neural network (DNN) algorithm is utilized with TensorFlow. This study solves the challenge of handling large-scale datasets in the collaborative filtering (CF) technique after tuning the algorithms by adjusting the parameters in the second approach, which uses machine learning, as well as in the third approach, which uses deep learning. Furthermore, the results of these two approaches outperformed conventional techniques and achieved an acceptable computational time. The dataset size is about 1.5 GB and it is collected from the Goodreads website API. Moreover, the Hadoop distributed file system (HDFS) is used as cloud storage instead of the computer’s local disk for handling larger dataset sizes in the future. [ABSTRACT FROM AUTHOR]