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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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

Since 2016, the implementation of blockchain in the supply chain has attracted wide interest among researchers. The creation and study of blockchain technology have grown at an exponential rate in the last decade. The supply chain would be a massive technological leap with the combination of blockchain technology that brings major changes to different facets of the industry, such as record immutability, improved traceability, quality assurance, improved knowledge flows, decentralized power structure, and decreased chances of fraud. With the growth in blockchain Internet-of-Thing (IoT) supply chain technologies, the security risk of implementing such technologies and methods to counter it has been largely discussed among researchers. This study analyses peer-reviewed literature attempting to use authentication in the blockchain-IoT supply chain to counter security risks in the technologies and provides a comprehensive analysis of the important feature when creating an authentication technique for the blockchain-IoT supply chain. Finally, the study discusses some of the problems and research objectives in the realm of authentication in the blockchain-IoT supply chain. [ABSTRACT FROM AUTHOR]

The development of mobile apps with augmented reality (AR) would enhance the capability in visualizing the scene or environment. Any apps supported by computer aided design versions with 3D models makes the design more realistic, such as in the form of websites or mobile apps. However, the current features for online platforms for shopping are quite limited and lack 3D visualization features. This paper presents the development of a mobile application, pro-visualizer app called PRO-VAS, that utilizes AR for scanning and visualizing the environment. PRO-VAS acts as a product visualizer that applies visual simultaneous localization and mapping (VSLAM) for localization of the product in AR based systems. The main components of PRO-VAS are ARCore from Google for interactive purposes, and the depth mapping from red green blue depth (RGB-D) phone camera with point plane generator and markerless tracking method. The last component of the app is the set of objects from the unity store, which can be chosen in PRO-VAS for the scanned scene area. The app was tested in various environments involving different objects and has shown competitive results. In the future, more features and products can be added to the apps. [ABSTRACT FROM AUTHOR]

This research designs, analyzes, and studies a 2.45 GHz rectangular microstrip patch antenna (RMPA). The antenna design uses Rogers RT5880 (lossy) substrate material with 2.2 dielectric permittivity, 1.5 mm thickness, and 0.0009 loss tangent. Additionally, the antenna was designed and simulated using computer simulation technology (CST) studio 2019 software. Plot designs were again created using Origin Pro Software. The simulation results showed that the return loss (S11), voltage standing wave ratio (VSWR), gain, directivity, bandwidth, efficiency, and surface current were -45.992 dB, 1.0101, 6.115 dBi, 6.534 dBi, 70.8 MHz, 93.59%, and 49.9 A/m, respectively. This paper aims to increase return loss to a typical VSWR value near 1. Besides boosting antenna gain, directivity, and efficiency, it can be used in future wireless applications, including mobile phones and wireless LANs. The proposed antenna design outperforms earlier experiments, demonstrating that the research has increased performance. [ABSTRACT FROM AUTHOR]

Whenever there is too much information out there, it is desirable to summarize. If humans are trying to create the summary, it will take lot of time. Now to make the problem of summarizing information easier and more effortless one can automate the summarization process which can reduce the time taken in creating summary. This is called as automatic summarization. The two ways of summarization are extractive summarization and abstractive summarization. Extractive summarization and its applications have been the subject of extensive research and have received state of art solution. But abstractive summarization still is a progressive field as it is difficult to create abstractive summary as humans do. Also, it is still a question i.e., how to evaluate the quality of a summary? Therefore, this paper is a comprehensive survey on the dataset used with its details and statistics, analysis of various abstractive summarization techniques and important parameters for evaluating the quality of summary. Deep leaning based models have given new direction in this field. The author also focuses on problems and challenges faced in the generation of summary which are opening the future research scope in this domain. [ABSTRACT FROM AUTHOR]

Within the rapidly evolving landscape of wireless communication technologies, this scientific paper delves into an innovative approach by exploring the integration of the Bluetooth frequency band into a circular antenna design. Leveraging the capabilities of metamaterials. The Bluetooth frequency band a cornerstone for short-range communications, takes center stage in our study. Traditionally associated with wireless connectivity in diverse applications, its integration into a circular antenna structure opens up new avenues for optimizing performance and functionality. Our research aims to not only enhance the efficiency of short-range communication systems but also contribute to the broader discourse on antenna design evolution. The computer simulation technology (CST) studio suite is utilized to show the recommended antenna design. By scrutinizing the symbiotic relationship between the Bluetooth frequency band and the circular antenna augmented with metamaterial, our study endeavors to push the boundaries of current design paradigms. We believe that this exploration will not only address the contemporary challenges associated with wireless communication but also pave the way for the development of more sophisticated, adaptable, and compact devices in the ever-expanding realm of telecommunications. [ABSTRACT FROM AUTHOR]

In the B5G/6G network, the deployment of small cells increased to keep up with the growth of mobile traffic. This deployment will increase the number of handovers (HOs) between cells. Ping-pong handover (PPHO) and radio link failure (RLF) are considered the two major problems in HO that may occur. So, the challenge is to set the handover control parameters (HCPs) carefully to find out the proper HO decision that should be appropriate to the environmental constraints. Therefore, in our paper, we propose an adaptive HCPs algorithm that adapts to environmental constraints. In addition, the proposed algorithm will have immunity to RLF and will significantly minimize the amount of PPHO compared to other workers. In the simulation results, our proposed model is evaluated using two frequency plans. By using frequency plan 1, the user mean throughput increased from 270 Kbps to 281 Kbps when the serving cell was fully loaded. By using frequency plan 2, the user mean throughput increased from 6 Mbps to 20 Mbps when the serving cell was fully loaded. In addition, the amount of ping-pong handover between overlapped small cells decreased and will not exceed one PPHO compared to another literature model. [ABSTRACT FROM AUTHOR]

Present-day advancements in embedded systems have uncovered a wide area of innovation in inexpensive supportive systems for the visually impaired. Right from the uncomplicated white cane up to the most exceptional electronic walking stick, many designs have been proposed aiming at assisting and protecting visually challenged persons. This paper aims at contributing to these assistive aids by designing a bamboo stick sensor-based unit with ultrasonic and water detection sensors which is robust, cheap, and easily operated for the deprived blind person. Thus, improving the usefulness of the current white stick to consolidate both above-knee and below-knee deterrent identifications. The developed bamboo stick simply operates using ultrasound sensors for sensing the impediments before contact and a water detection sensor for water detection on the pathway. It offers vibration and different sound feedbacks to the operator per the spot of the obstacle. The results obtained by trial from a volunteer who walked an obstructed path blindfolded were excellent. The results ensure quick detection, safety and enhance the speed of mobility of the user. The simulations performed were accurate and relevant to the ultimate goal of the paper. The electronic bamboo walking stick developed can be used to guide the visually impaired in an indoor or outdoor environment. [ABSTRACT FROM AUTHOR]

In this paper, we design a secure transmission scheme in uplink and downlink of nonorthogonal multiple access (NOMA) system. In this scenario, two pairs of base stationuser can operate under the presence of eavesdropper. The relay plays an important role to forward signals from group of base stations to serve distant users. However, the eavesdropper can overhear signal which leads to secure performance need be reconsidered. To provide secure performance, we derive the closed-form expressions for strictly positive secrecy capacity (SPSC). In addition, we rely on relay for enlarge coverage area. The channel conditions meet imperfect channel state information (CSI) which shows degraded secure performance. Results confirm the relationship between transmit power at the source and and SPSC how many main parameters affecting secure performance metric. Furthermore, simulation results show that the uplink and downlink NOMA technique improves secure performance in the low SPSC region. We further conduct analysis by using Monte-Carlo simulations. [ABSTRACT FROM AUTHOR]

This paper presents research on a permanent magnet synchronous motor (PMSM) by a 5-level T-type inverter using linear quadratic regulator (LQR) control for speed control. The space vector modulation (SVM) structure for the 5-level T-type inverter to improve the control quality of the PMSM drive system is presented. This paper aims to successfully design a state feedback controller to control motor speed in a permanent magnet synchronous motor drive system. Simulink results are performed with different speed ranges and the same torque load. These results will focus on evaluating quality when the PMSM drive operates at all speeds. MATLAB/Simulink proves the results. [ABSTRACT FROM AUTHOR]

This paper presents the design of highly efficient rectifiers that can operate at WiFi frequencies (2.45 GHz and 5.8 GHz) and match low input power. The designed dual-band rectifiers use multi-stage dickson voltage multipliers (DVM) (from 1 to 6 stages), and the main idea of this paper is to find the number of stages that offers the best performance and that can be used by applications that operate within the same constraints (operating frequencies, input power). The efficiency and the output voltage (Vout) are the parameters studied to analyze the designed rectifiers. The results showed that as the number of DVM stages increased, the efficiency curves for both frequencies shifted to the higher input power range, even when using the same diode (SMS7630) and the same load (5 KO). We concluded that the rectifier with 1-stage DVM is the most suitable to be used for low input power at the selected frequencies since it provides an efficiency of 57.734% at 0 dBm for 2.45 GHz and 36.225% at 1 dBm of input power for 5.8 GHz. [ABSTRACT FROM AUTHOR]

In this paper, we presented two developments, the first deals with generalizing the modernization of the damped Dai-Liao formula in an optimized manner. The second development is by suggesting a monotone line search formula for our new algorithms. These new algorithms with the new monotone line search yielded good numerical results when compared to the standard Dai-Liao (DL) and minimum description length (MDL) algorithms. Through several theorems, the new algorithms proved to have a faster convergence to reach the optimum point. These comparisons are drawn in the results section for the tools (Iter, Eval-F, Time) which are a comprehensive measure of the efficiency of the new algorithms with the basic algorithms that we used in the paper. [ABSTRACT FROM AUTHOR]

The ocean environment monitoring system is of great significance to the researchers because the ocean is the storehouse of natural resources. It is critical to comprehend and assess the ocean's environmental conditions. Several studies have been conducted over the last several decades that use sophisticated information and communication techniques to ensure the ocean ecosystem. Wireless sensor networks (WSNs) are a promising technology to monitor the ocean environment, which delivers significant benefits such as enhanced accuracy and real-time observations. The advancements in sensor technology such as micro electromechanical systems (MEMS), integrated systems, distributed processing, wireless communications, and wireless sensor applications have contributed to the development of WSNs. This paper describes the utilization of WSN and analyzes the previous and existing project works and technologies used for ocean environment monitoring through WSNs, and also includes the MEMS sensor technology used for monitoring various ocean parameters such as ocean wave monitoring, water conductivity, temperature, and depth of ocean. [ABSTRACT FROM AUTHOR]

Vehicular ad hoc network (VANET) is a modern technology that has received great attention in the modern era due to daily road accidents. In VANET network it is difficult to design effective routing protocols due to the speed of movement of nodes and the rapid change in network architecture, and the purpose of routing protocols in the VANET network is to route data between vehicles (V2V), and between vehicles to infrastructure (V2I). Recently researchers have been interested in designing effective routing protocols for the VANET network because not all existing protocols are suitable for all traffic scenarios. Therefore, the focus of this paper will be on the maximum distance on-demand routing algorithm (MDORA) protocol and work on improving the protocol algorithm so that it is compatible with the urban environment. After that, the improved performance of the MDORA-without direction (MDORA-WD) protocol will be compared with the ad-hoc on-demand distance vector (AODV) protocol in terms of communication overhead, packet delivery ratio (PDR) and end to end (E2E) delay. The protocols will be simulated by MATLAB. [ABSTRACT FROM AUTHOR]

One of the ways to detect coronavirus disease of 2019 (COVID-19) is X-rays, computerized tomography (CT). This paper aims to detect COVID-19 from CT images without any user intervention. The proposed algorithm consists of 5 stages. These stages include; the first stage aims to collect data from hospitals and internet websites, the second stage is pre-processing stage to remove noise and convert it from red green blue (RGB) to grayscale and then improve image quality, the third is the segmentation stage which included threshold and region-growing segmentation methods. The fourth stage is used to extract important characteristics, and the last stage is classification CT images using feed forward back propagation network (FFBPN) and support vector machines (SVM) and compare the results between them and see if the person is infected or healthy. This study was implemented in MATLAB software. The results showed that the noise cancellation technology using anisotropic filtering gave the best results. Region-growing method was reliable to separate COVID-19 infected from healthy regions. The FFBPN has given the best results for detecting and classifying COVID-19. The results of the proposed methodology are rapid and accurate in detecting COVID-19. The output from classifier is displayed on the Rasbperry Pi that included weather if patient is infected or not and the severity of COVID-19 infection. [ABSTRACT FROM AUTHOR]

In light of the advanced technological development in the world, this study aims to use alternative energy for generating electric energy at the lowest cost. Solar cells are devices that have special operating conditions. The most important of these conditions are the temperature of cells and the intensity of solar radiation. Sometimes the climate condition does not match the ideal conditions used for operating solar cells in many regions around the world. Although the Middle East and Iraq in particular, is considered one of the sunniest areas in the world, solar energy cannot be relied upon to generate electric energy effectively because of the problem of high temperature in solar cells in the summer as well as the problem of dust. In this paper, a simple and economical method was designed to overcome the main problems in the operation of solar cells represented by high temperature in solar cells as well as the problem of dust accumulated on the surface of solar cells which impedes the entry of sunlight into those cells. The method is conducted by immersing the solar cells in running water at a distance that should be close to the surface of the water. [ABSTRACT FROM AUTHOR]

For some years now, the faceted exploratory search has faced tremendous challenges to devise a simple, instantly understandable, and usable user interface to explore the vast amount of data and information available on the Internet. In our previous research, we have indicated certain burdens with some of the existing approaches. Therefore, in this research, we are trying to uncover the attributes that influence users to use the faceted feature of the exploratory search technique. In this paper, we presented several key elements of existing search engine systems, the best of their major features were explored, and guidelines that will help to improve the usability of faceted exploratory search have been suggested and offers a further understanding of the fundamental characteristics of the faceted search, which shows the motivations to use the search, also presented the main parts of a pluggable search system. [ABSTRACT FROM AUTHOR]

For timely and efficient reactions to disasters, collecting vital and right information is essential. In recent decades, social media platforms such as Twitter, Facebook, Linkedin, Instagram have become valuable sources of information in disaster times. However, the reliability, volume, and velocity of information remain a major concern; this is particular about information issued from disaster locations. This paper proposes an approach for tracking the location of people in danger during times of disaster. The procedure is based on the Twitter application programming interface (API) by using natural language processing (NLP) and big data tools. A number of tweets were analyzed and an accuracy of 86.11% was actualized. [ABSTRACT FROM AUTHOR]

Radiological imaging generates large data volumes for diagnosing. These data volumes still constitute an immense challenge for individual radiology practices, either when complying with the archive, hacker manipulation or swiftly distributing images among specialists as part of the diagnostic process. Analyzing images through illegal distortions may lead to inaccurate medical decisions. Watermarking techniques can be used to authenticate images, detect, and recover illegal changes made to teleradiology images as well. In this paper, a new blind medical image watermarking joint cryptocompression system is proposed to simultaneously achieve watermarking and compression. A discrete wavelet transform is applied to the medical image to locate different frequency sub-bands. The watermark is scrambled using one-dimensional chaotic map generator with uniform distribution modulo-one transformations for generating highly independent and uniformly distributed random chaotic sequences. A particle cryptocompression algorithm is applied for the insertion step: the scrambled watermark is inserted into the highest singular value derived from the compressed low-frequency sub-band, ensuring a delicate balance between imperceptibility and robustness. From the conducted tests and comparison of the obtained results with other techniques, we concluded that the robustness of the algorithm under various attacks was improved by the excellent value of the NC parameter, especially in the case of compression and geometric attacks. [ABSTRACT FROM AUTHOR]

The suitable placement and power of distributed generation (DG) can bring technical benefits to the distribution network. This paper applies the Coot optimization algorithm to the DG placement problem with the goal of minimizing power loss and feeder balancing load (LBF). The weight method is used to combine the membership objectives. The evaluation results on the network of 70 nodes for different weight values of the objective function show that the optimal power and installation location of DGs significantly reduces power loss and improves LBF index. In this study, eleven cases were considered. As the weight of power loss part W1 increases from 0 to 1, the power loss gradually decreases, the LBF index gradually increases, the maximum current gradually decreases, and the minimum voltage amplitude gradually improves. Comparing the results of Coot with particle swarm optimization (PSO) shows that the indicators are improved. In the three cases where W1 is 0, 0.5, and 1, power loss gained by Coot compared to PSO is less than 47.2663 kW, 73.2725 kW, 30.8708 kW, respectively. LBF index of Coot compared to PSO is less than 98.2%, 88.2%, 81.9%, respectively. The maximum, minimum, average, standard deviation, and CPU time of Coot are smaller than those of PSO. So, Coot is one of the promising methods for this problem. [ABSTRACT FROM AUTHOR]

Histopathological image analysis (HIA) plays an essential role in detecting cancer cell development, but it is time-consuming, prone to inaccuracy, and dependent on pathologist competence. This paper proposes an automated HIA that uses deep learning to improve accuracy and efficiency in liver cancer cell growth. The model uses whole slide image (WSI) input, open computer vision (OpenCV) libraries for image preprocessing, ResNet50 for patch-level feature extraction, and multiple instances learning for imagelevel classification. The suggested approach accurately distinguishes liver histopathological pictures as cancerous or non-cancerous. Assisting in the early detection of liver cancer cell development with potential invasion or spread. [ABSTRACT FROM AUTHOR]

Since cloud computing has an abundance of users, the system has to execute a wide range of tasks. Task scheduler methods that are both robust and efficient while delivering the best outcomes are required. The task volume and runtime in the cloud vary rapidly, making task assessment and resource mapping difficult. Security issues, communication delays, and data loss are substantial barriers to scheduling. Furthermore, optimization techniques can be utilized to reduce load and assign tasks so that the user can finish tasks faster. This paper offers a hybrid job scheduling technique for cloud computing using adaptive particle swarm optimization and ant colony optimization particle swarm optimization-ant colony optimization (adaptive PSO-ACO). After rapidly finding the initial solution via particle swarm optimization, the ant colony optimization approach establishes its first pheromone distribution. The suggested hybrid algorithm is compared to standalone PSO and ACO algorithms. Compared to ACO, the percentage decrease is 7.9%. Hybrid method has the lowest total cost, 55% less compared to PSO. Tasks vary when virtual machines (VMs) are constant and VMs vary when tasks are constant. Parameters like final cost, makespan, fitness value, computation time and weighted time are assessed to evaluate the performance of the hybrid task scheduling algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we design and simulate a 28/38 GHz dual-band multiple-input multiple-output (MIMO) patch antenna array that operates in the FR2 frequency band (28 GHz and 38 GHz). This antenna array comprises four similar patch antennas with a rectangular "L minus two slots" shape. In addition, it applies to 5G electronic components such as a smartphone. We used high-frequency structure simulator (HFSS) software to perform the design and simulation for this antenna. In addition, this proposed antenna array provides better performance, such as; bandwidth around 28 GHz is equal to 0.69 GHz and 38 GHz is equal to 0.86 GHz, equal gains 5. 9 dB at 28 GHz and 9 dB at 38 GHz, directivities are 6.3 dB at 28 GHz and 9.4 dB at 38 GHz, the efficiency of 95.38% at 28 GHz, and efficiency of 96.53% at 38 GHz. [ABSTRACT FROM AUTHOR]