List of Articles
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Open Access Article
1 - Broadband Low-cost Reflectarray based on a New Phase Synthesis Technique and a Class of Cross Bow-Tie Cells
Mahmood Rafaei-Booket Mahdieh Bozorgi Seyed Mostafa MusaviIn this paper, a class of Bow-tie unit cell on FR4 substrate is designed and investigated to be used in implementing a single-layer broadband Reflectarray Antenna (RA). In the analyzing step, two different parameters (length and angle) of the grounded Cross Bow-Tie (CBT MoreIn this paper, a class of Bow-tie unit cell on FR4 substrate is designed and investigated to be used in implementing a single-layer broadband Reflectarray Antenna (RA). In the analyzing step, two different parameters (length and angle) of the grounded Cross Bow-Tie (CBT) are varied to obtain the phase diagrams. Various degrees of freedom in the CBT structure are very helpful in designing a broadband RA. In the antenna design procedure, an efficient phase synthesis technique is applied to minimize the adverse effects of frequency dispersion causing by the differential space phase delay at different frequencies. This technique optimizes the metallic CBTs arrangement on the aperture of RA, and reduces the dependency of RA design to the CBT’s phase variation. Consequently, combination of the CBT’s phase behavior and the phase synthesis technique leads to designing a broadband RA with a good frequency response. In addition to, the Side Lobe Level (SLL) of the resultant RA is reduced remarkably. For validation of the obtained numerical results, an RA is designed and fabricated in 8.7~12.3GHz frequency bandwidth. The measurements show 27.03 dB as a maximum gain value at 10.2 GHz with a 1.5dB gain bandwidth of 34%. It is also shown that the implemented RA exhibits a reduced SLL (<-18dBi) within its operating bandwidth. Manuscript profile -
Open Access Article
2 - Comparative Study of 5G Signal Attenuation Estimation Models
Md Anoarul Islam Manabendra Maiti Judhajit Sanyal Quazi Md AlfredWireless networks functioning on 4G and 5G technology offer a plethora of options to users in terms of connectivity and multimedia content. However, such networks are prone to severe signal attenuation and noise in a number of scenarios. Significant research in recent y MoreWireless networks functioning on 4G and 5G technology offer a plethora of options to users in terms of connectivity and multimedia content. However, such networks are prone to severe signal attenuation and noise in a number of scenarios. Significant research in recent years has consequently focused on establishment of robust and accurate attenuation models to estimate channel noise and subsequent signal loss. The identified challenge therefore is to identify or develop accurate computationally inexpensive models implementable on available hardware for generation of estimates with low error and validate the solutions experimentally. The present work surveys some of the most relevant recent work in this domain, with added emphasis on rain attenuation models and machine learning based approaches, and offers a perspective on the establishment of a suitable dynamic signal attenuation model for high-speed wireless communication in outdoor as well as indoor environments, presenting the performance evaluation of an autoregression-based machine learning model. Multiple versions of the model are compared on the basis of root mean square error (RMSE) for different orders of regression polynomials to find the best-fit solution. The accuracy of the technique proposed in the paper is then compared in terms of RMSE to corresponding moderate and high complexity machine learning techniques implementing adaptive spline regression and artificial neural networks respectively. The proposed method is found to be quite accurate with low complexity, allowing the method to be practically applicable in multiple scenarios. Manuscript profile -
Open Access Article
3 - Hoax Identification of Indonesian Tweeters using Ensemble Classifier
Gus Nanang Syaifuddiin Rizal Arifin Desriyanti Desriyanti Ghulam Asrofi Buntoro Zulkham Umar Rosyidin Ridwan Yudha Pratama Ali SelamatFake information, better known as hoaxes, is often found on social media. Currently, social media is not only used to make friends or socialize with friends online, but some use it to spread hate speech and false information. Hoaxes are very dangerous in social life, es MoreFake information, better known as hoaxes, is often found on social media. Currently, social media is not only used to make friends or socialize with friends online, but some use it to spread hate speech and false information. Hoaxes are very dangerous in social life, especially in countries with large populations and ethnically diverse cultures, such as Indonesia. Although there have been many studies on detecting false information, the accuracy and efficiency still need to be improved. To help prevent the spread of these hoaxes, we built a model to identify false information in Indonesian using an ensemble classifier that combines the n-gram method, term frequency-inverse document frequency, and passive-aggressive classifier method. The evaluation process was carried out using 5000 samples from Twitter social media accounts in this study. The testing process is carried out using four schemes by dividing the dataset into training and test data based on the ratios of 90:10, 80:20, 70:30, and 60:40. The inspection results show that our software can accurately detect hoaxes at 91.8%. We also found an increase in the accuracy and precision of hoax detection testing using the proposed method compared to several previous studies. The results show that our proposed method can be developed and used in detecting hoaxes in Indonesian on various social media platforms. Manuscript profile -
Open Access Article
4 - Computational Model for Image Processing in the Minds of People with Visual Agnosia using Fuzzy Cognitive Map
Elham Askari Sara MotamedThe Agnosia is a neurological condition that leads to an inability to name, recognize, and extract meaning from the visual, auditory, and sensory environment, despite the fact that the receptor organ is perfect. Visual agnosia is the most common type of this disorder. P MoreThe Agnosia is a neurological condition that leads to an inability to name, recognize, and extract meaning from the visual, auditory, and sensory environment, despite the fact that the receptor organ is perfect. Visual agnosia is the most common type of this disorder. People with agnosia have trouble communicating between the mind and the brain. As a result, they cannot understand the images seen. In this paper, a model is proposed that is based on the visual pathway so that it first receives the visual stimulus and then, after understanding, the object is identified. In this paper, a model based on the visual pathway is proposed and using intelligent Fuzzy Cognitive Map will help improve image processing in the minds of these patients. First, the proposed model that is inspired by the visual perception pathway, is designed. Then, appropriate attributes that include the texture and color of the images are extracted and the concept of the seen image is perceived using Fuzzy Cognitive Mapping, the meaning recognition and the relationships between objects. This model reduces the difficulty of perceiving and recognizing objects in patients with visual agnosia. The results show that the proposed model, with 98.1% accuracy, shows better performance than other methods. Manuscript profile -
Open Access Article
5 - Performance Analysis and Activity Deviation Discovery in Event Log Using Process Mining Tool for Hospital System
Shanmuga Sundari M Rudra Kalyan Nayak Vijaya Chandra Jadala Sai Kiran PasupuletiAll service and manufacturing businesses are resilient and strive for a more efficient and better end in today's world. Data mining is data-driven and necessitates significant data to analyze the pattern and train the model. Assume the data is incorrect and was not coll MoreAll service and manufacturing businesses are resilient and strive for a more efficient and better end in today's world. Data mining is data-driven and necessitates significant data to analyze the pattern and train the model. Assume the data is incorrect and was not collected from reliable sources, causing the analysis to be skewed. We introduce a procedure in which the dataset is split into test and training datasets with a specific ratio to overcome this challenge. Process mining will find the traces of actions to streamline the process and aid data mining in producing a more efficient result. The most responsible domain is the healthcare industry. In this study, we used the activity data from the hospital and applied process mining algorithms such as alpha miner and fuzzy miner. Process mining is used to check for conformity in the event log and do performance analysis, and a pattern of accuracy is exhibited. Finally, we used process mining techniques to show the deviation flow and fix the process flow. This study showed that there was a variation in the flow by employing alpha and fuzzy miners in the hospital. Manuscript profile -
Open Access Article
6 - Cache Point Selection and Transmissions Reduction using LSTM Neural Network
Malihe Bahekmat Mohammad Hossein Yaghmaee MoghaddamReliability of data transmission in wireless sensor networks (WSN) is very important in the case of high lost packet rate due to link problems or buffer congestion. In this regard, mechanisms such as middle cache points and congestion control can improve the performance MoreReliability of data transmission in wireless sensor networks (WSN) is very important in the case of high lost packet rate due to link problems or buffer congestion. In this regard, mechanisms such as middle cache points and congestion control can improve the performance of the reliability of transmission protocols when the packet is lost. On the other hand, the issue of energy consumption in this type of networks has become an important parameter in their reliability. In this paper, considering the energy constraints in the sensor nodes and the direct relationship between energy consumption and the number of transmissions made by the nodes, the system tries to reduce the number of transmissions needed to send a packet from source to destination as much as possible by optimal selection of the cache points and packet caching. In order to select the best cache points, the information extracted from the network behavior analysis by deep learning algorithm has been used. In the training phase, long-short term memory (LSTM) capabilities as an example of recurrent neural network (RNN) deep learning networks to learn network conditions. The results show that the proposed method works better in examining the evaluation criteria of transmission costs, end-to-end delays, cache use and throughput. Manuscript profile -
Open Access Article
7 - Representing a Novel Expanded Version of Shor’s Algorithm and a Real-Time Experiment using IBM Q-Experience Platform
Sepehr Goodarzi Afshin Rezakhani Mahdi MalekiThe data are stored on the memory of the classical computer in small units of classical bits, which could be either 0 or 1. However, on a Quantum Computer, The Quantum States of each Quantum Bit (Qbit), would be every possible number between 0 and 1, including themselve MoreThe data are stored on the memory of the classical computer in small units of classical bits, which could be either 0 or 1. However, on a Quantum Computer, The Quantum States of each Quantum Bit (Qbit), would be every possible number between 0 and 1, including themselves. By placing the photons on a special state, which is a spot located at the middle of the two-dimensional space vectors (█(1@0)) and (█(1@1)) on the Unit Circle, which is called Superposition and we can take advantage of properties of this state when we place lots of vectors of N-dimensional spaces in superposition and we can do a parallelization and factorization for getting significant speedup. In fact, in Quantum Computing we are taking advantage of Quantum Dynamic Principles to process the data, which Classical Computers lack on, by considering the limitations of logical concepts behind them. Through this paper, we expand a quantum algorithm for the number of n Qbits in a new way and by implementing circuits using IBM-Q Experience, we are going to have some practical results, which are more obvious to be demonstrable. By expanding the Quantum Algorithms and using Linear Algebra, we can manage to achieve the goals at a higher level, the ones that Classical Computers are unable to perform, as machine learning problems with complicated models and by expanding the subject we can mention majors in different sciences like Chemistry (predicting the Structure of proteins with higher percentage accuracy in less period), Astronomy and so on. Manuscript profile -
Open Access Article
8 - A Novel Elite-Oriented Meta-Heuristic Algorithm: Qashqai Optimization Algorithm (QOA)
Mehdi Khadem Abbas Toloie Eshlaghy Kiamars Fathi HafshejaniOptimization problems are becoming more complicated, and their resource requirements are rising. Real-life optimization problems are often NP-hard and time or memory consuming. Nature has always been an excellent pattern for humans to pull out the best mechanisms and th MoreOptimization problems are becoming more complicated, and their resource requirements are rising. Real-life optimization problems are often NP-hard and time or memory consuming. Nature has always been an excellent pattern for humans to pull out the best mechanisms and the best engineering to solve their problems. The concept of optimization seen in several natural processes, such as species evolution, swarm intelligence, social group behavior, the immune system, mating strategies, reproduction and foraging, and animals’ cooperative hunting behavior. This paper proposes a new Meta-Heuristic algorithm for solving NP-hard nonlinear optimization problems inspired by the intelligence, socially, and collaborative behavior of the Qashqai nomad’s migration who have adjusted for many years. In the design of this algorithm uses population-based features, experts’ opinions, and more to improve its performance in achieving the optimal global solution. The performance of this algorithm tested using the well-known optimization test functions and factory facility layout problems. It found that in many cases, the performance of the proposed algorithm was better than other known meta-heuristic algorithms in terms of convergence speed and quality of solutions. The name of this algorithm chooses in honor of the Qashqai nomads, the famous tribes of southwest Iran, the Qashqai algorithm. Manuscript profile -
Open Access Article
9 - An Analysis of Covid-19 Pandemic Outbreak on Economy using Neural Network and Random Forest
Md. Nahid Hasan Tanvir Ahmed Md. Ashik Md. Jahid Hasan Tahaziba Azmin Jia UddinThe pandemic disease outbreaks are causing a significant financial crisis affecting the worldwide economy. Machine learning techniques are urgently required to detect, predict and analyze the economy for early economic planning and growth. Consequently, in this paper, w MoreThe pandemic disease outbreaks are causing a significant financial crisis affecting the worldwide economy. Machine learning techniques are urgently required to detect, predict and analyze the economy for early economic planning and growth. Consequently, in this paper, we use machine learning classifiers and regressors to construct an early warning model to tackle economic recession due to the cause of covid-19 pandemic outbreak. A publicly available database created by the National Bureau of Economic Research (NBER) is used to validate the model, which contains information about national revenue, employment rate, and workers' earnings of the USA over 239 days (1 January 2020 to 12 May 2020). Different techniques such as missing value imputation, k-fold cross validation have been used to pre-process the dataset. Machine learning classifiers- Multi-layer Perceptron- Neural Network (MLP-NN) and Random Forest (RF) have been used to predict recession. Additionally, machine learning regressors-Long Short-Term Memory (LSTM) and Random Forest (RF) have been used to detect how much recession a country is facing as a result of positive test cases of covid-19 pandemic. Experimental results demonstrate that the MLP-NN and RF classifiers have exhibited average 88.33% and 85% of recession (where 95%, 81%, 89% and 85%, 81%, 89% for revenue, employment rate and workers earnings, respectively) and average 90.67% and 93.67% of prediction accuracy for LSTM and RF regressors (where 92%, 90%, 90%, and 95%, 93%, 93% respectively). Manuscript profile