bluuebunny/crossref_metadata_2025 huggingface dataset:
Predictive maintenance of rotational machinery using deep learning
Authors: Ali Mohamed Iyad , Lai Nai Shyan , Abdulla Raed
Published: November 2023

This paper describes an implementation of a deep learning-based predictive maintenance (PdM) system for industrial rotational machinery, built upon the foundation of a long short-term memory (LSTM) autoencoder and regression analysis. The autoencoder identifies anomalous patterns, while the latter, based on the autoencoder’s output, estimates the machine’s remaining useful life (RUL). Unlike prior PdM systems dependent on labelled historical data, the developed system doesn’t require it as it’s based on an unsupervised deep learning model, enhancing its adaptability. The paper also explores a robust condition monitoring system that collects machine operational data, including vibration and current parameters, and transmits them to a database via a Bluetooth low energy (BLE) network. Additionally, the study demonstrates the integration of this PdM system within a web-based framework, promoting its adoption across various industrial settings. Tests confirm the system's ability to accurately identify faults, highlighting its potential to reduce unexpected downtime and enhance machinery reliability.
Synthesis of the neuro-fuzzy regulator with genetic algorithm
Authors: Siddikov Isamiddin , Porubay Oksana , Rakhimov Temurbek
Published: November 2023

Real-acting objects are characterized by the presence of various types of random perturbations, which significantly reduce the quality of the control process, which determines the use of modern methods of intellectual technology to solve the problem of synthesis of control systems of structurally complex dynamic objects, allowing to compensate the influence of external factors with the properties of randomness and partial uncertainty. The article considers issues of synthesis of the automatic control system of dynamic objects by applying the theory of intelligent control. In this case, a neural network based on radial-basis functions is used at each discrete interval for neuro-fuzzy approximation of the control system, allowing real-time adjustment of the regulator parameters. The radial basis function is designed to approximate functions defined in the implicit form of pattern sets. The neuro-fuzzy regulator's parameter configuration is accomplished using a genetic algorithm, enabling more efficient computation to determine the regulator's set parameters. The regulator's parameters are represented as a vector, facilitating their application to multidimensional objects. To determine the optimal tuning parameters of the neuro-fuzzy regulator, characterized by high convergence and the possibility of determining global extrema, a genetic algorithm was used. The effectiveness of the neuro-fuzzy regulator is explained by the possibility of providing quality control of the dynamic object under random perturbations and uncertainty of input data.
Comparison of convolutional neural network models for user’s facial recognition
Authors: Pinzón-Arenas Javier Orlando , Jimenez-Moreno Robinson , Martinez Baquero Javier Eduardo
Published: November 2023

This paper compares well-known convolutional neural networks (CNN) models for facial recognition. For this, it uses its database created from two registered users and an additional category of unknown persons. Eight different base models of convolutional architectures were compared by transfer of learning, and two additional proposed models called shallow CNN and shallow directed acyclic graph with CNN (DAG-CNN), which are architectures with little depth (six convolution layers). Within the tests with the database, the best results were obtained by the GoogLeNet and ResNet-101 models, managing to classify 100% of the images, even without confusing people outside the two users. However, in an additional real-time test, in which one of the users had his style changed, the models that showed the greatest robustness in this situation were the Inception and the ResNet-101, being able to maintain constant recognition. This demonstrated that the networks of greater depth manage to learn more detailed features of the users' faces, unlike those of shallower ones; their learning of features is more generalized. Declare the full term of an abbreviation/acronym when it is mentioned for the first time.
Impact of initialization of a modified particle swarm optimization on cooperative source searching
Authors: Ab. Majid Mad Helmi , Arshad Mohd Rizal , Yahya Mohd Faid , Ibrahim Abu Bakar
Published: November 2023

Swarm robotic is well known for its flexibility, scalability and robustness that make it suitable for solving many real-world problems. Source searching which is characterized by complex operation due to the spatial characteristic of the source intensity distribution, uncertain searching environments and rigid searching constraints is an example of application where swarm robotics can be applied. Particle swarm optimization (PSO) is one of the famous algorithms have been used for source searching where its effectiveness depends on several factors. Improper parameter selection may lead to a premature convergence and thus robots will fail (i.e., low success rate) to locate the source within the given searching constraints. Additionally, target overshooting and improper initialization strategies may lead to a nonoptimal (i.e., take longer time to converge) target searching. In this study, a modified PSO and three different initializations strategies (i.e., random, equidistant and centralized) were proposed. The findings shown that the proposed PSO model successfully reduce the target overshooting by choosing optimal PSO parameters and has better convergence rate and success rate compared to the benchmark algorithms. Additionally, the findings also indicate that the random initialization give better searching success compared to equidistant and centralize initialization.
Unconsidered but influencing interference in unmanned aerial vehicle cabling system
Authors: Setyadewi Imas Tri , Prabowo Yanuar , Wibowo Priyo , Priambodo Purnomo Sidi
Published: November 2023

The increasing complexity of electrical and electronic systems in unmanned aerial vehicles (UAVs) has raised concerns regarding unwanted electromagnetic interference (EMI) due to limited compartment space. Recent studies have highlighted the UAV cabling as the primary pathway for interference. This paper presents a novel approach to investigating the effects of interference power, polarization angle, and distance from the interference source on EMI in UAV cable systems. Measurements and simulations were performed to analyze the influence of these factors on the radiation received by the cable. A linear dipole antenna, operating at a frequency of 905 MHz, served as the radiation source, while a single wire cable pair terminated with a 50-ohm resistor was employed as the victim. The findings reveal that the power transmitted by the source, the distance between the cable and the source, and the polarization angle have a significant impact on the electromagnetic interference received by the cable. Notably, a perpendicular orientation of the cable to the interference source (antenna) in the far-field yielded a reduction of up to 15 dBm in EMI. The results underscore the necessity for more sophisticated models and comprehensive measurements to fully comprehend the diverse factors affecting polarization losses in practical scenarios.
Controlling temperature using proportional integral and derivative control algorithm for hybrid forced convection solar dryer
Authors: Arifin Mohammad Aldrin , Pangaribuan Porman , Pramudita Brahmantya Aji , Megantoro Prisma
Published: November 2023

Drying is one of the crucial processes in agricultural production, especially in grain processing. The drying process can improve grain quality and affect the grain content. However, maintaining the temperature is a challenge in the drying process. Because it can influence the drying performance and produce a low-efficiency reduction of water content, in this study, the hybrid drying system is proposed to improve the performance of the forced convection dryer system. The proposed system used a proportional integral and derivative (PID) control system to obtain the optimal temperature. The proposed system was compared with natural drying and forced convection methods. The experimental result showed that the proposed system performed excellently for three performance evaluations. The average temperature was obtained as the highest of the other methods, with 54.68 °C and 54.55 °C for coffee and cocoa beans. The water content can be reduced by an average of 27.38% and 42.67% for coffee and cocoa beans. Then, the proposed system also had the highest reduction efficiency of water content than the other methods, with 62.71% and 36.94% reductions for coffee and cocoa beans, respectively. The results indicate that the proposed hybrid system performs better than the other methods.
Modified fuzzy rough set technique with stacked autoencoder model for magnetic resonance imaging based breast cancer detection
Authors: Kumar Mamdy Sachin , Petli Vishwanath
Published: November 2023

Breast cancer is the common cancer in women, where early detection reduces the mortality rate. The magnetic resonance imaging (MRI) images are efficient in analyzing breast cancer, but it is hard to identify the abnormalities. The manual breast cancer detection in MRI images is inefficient; therefore, a deep learning-based system is implemented in this manuscript. Initially, the visual quality improvement is done using region growing and adaptive histogram equalization (AHE), and then, the breast lesion is segmented by Otsu thresholding with morphological transform. Next, the features are extracted from the segmented lesion, and a modified fuzzy rough set technique is proposed to reduce the dimensions of the extracted features that decreases the system complexity and computational time. The active features are fed to the stacked autoencoder for classifying the benign and malignant classes. The results demonstrated that the proposed model attained 99% and 99.22% of classification accuracy on the benchmark datasets, which are higher related to the comparative classifiers: decision tree, naïve Bayes, random forest and k-nearest neighbor (KNN). The obtained results state that the proposed model superiorly screens and detects the breast lesions that assists clinicians in effective therapeutic intervention and timely treatment.
Impacts of COVID-19 lockdown period on the Algerian power grid demand
Authors: Draidi Abdellah , Assabaa Mohamed , Bouchahed Adel , Mehimmedetsi Boudjemaa
Published: November 2023

The coronavirus disease-2019 (COVID-19) spread out at the end of 2019 has sadly caused millions of human losses and hundreds of millions of cases and stressful health situations. As a result, governments forced the worldwide population to stay confined and change their social activities and working behaviors. Under such conditions all economic sectors have been impacted, therefore global electricity consumption pattern has changed consequently. The object of this study is to calculate energy drop for such circumstances to make strategies to face such events in the future. The study we conducted during the period of confinement aims to identify the effects of the Corona epidemic on electricity consumption in Algeria by emphasizing four months (March, April, May, and June) for four years (2018, 2019, 2020, and 2021) by comparing monthly load curves and calculating load deviation for each month.
Adversarial sketch-photo transformation for enhanced face recognition accuracy: a systematic analysis and evaluation
Authors: Shetty Kirimanjeshwara Raghavendra Mandara , Narasimha Prasad Sarappadi
Published: November 2023

This research provides a strategy for enhancing the precision of face sketch identification through adversarial sketch-photo transformation. The approach uses a generative adversarial network (GAN) to learn to convert sketches into photographs, which may subsequently be utilized to enhance the precision of face sketch identification. The suggested method is evaluated in comparison to state-of-the-art face sketch recognition and synthesis techniques, such as sketchy GAN, similarity-preserving GAN (SPGAN), and super-resolution GAN (SRGAN). Possible domains of use for the proposed adversarial sketch-photo transformation approach include law enforcement, where reliable face sketch recognition is essential for the identification of suspects. The suggested approach can be generalized to various contexts, such as the creation of creative photographs from drawings or the conversion of pictures between modalities. The suggested method outperforms state-of-the-art face sketch recognition and synthesis techniques, confirming the usefulness of adversarial learning in this context. Our method is highly efficient for photo-sketch synthesis, with a structural similarity index (SSIM) of 0.65 on The Chinese University of Hong Kong dataset and 0.70 on the custom-generated dataset.
An introduction to double stain normalization technique for brain tumour histopathological images
Authors: Akmal Dzulkifli Fahmi , Yusoff Mashor Mohd , A. Raof Rafikha Aliana , Jaafar Hasnan
Published: November 2023

Stain normalization is an image pre-processing method extensively used to standardize multiple variances of staining intensity in histopathology image analysis. Staining variations may occur for several reasons, such as unstandardized protocols while preparing the specimens, using dyes from different manufacturers, and varying parameters set while capturing the digital images. In this study, a double stain normalization technique based on immunohistochemical staining is developed to improve the performance of the conventional Reinhard’s algorithm. The proposed approach began with preparing a target image by applying the contrast-limited adaptive histogram equalization (CLAHE) technique to the targeted cells. Later, the colour distribution of the input image will be matched to the colour distribution of the target image through the linear transformation process. In this study, the power-law transformation was applied to address the over-enhancement and contrast degradation issues in the conventional method. Five quality metrics comprised of entropy, tenengrad criterion (TEN), mean square error (MSE), structural similarity index (SSIM) and correlation coefficient were used to measure the performance of the proposed system. The experimental results demonstrate that the proposed method outperformed all conventional techniques. The proposed method achieved the highest average values of 5.59, 3854.11 and 94.65 for entropy, TEN, and MSE analyses.
Self-steering Yagi-Uda antenna positioning system for television
Authors: Federis Montañez John Joshua , Alipante Vargas James , Francisco Palibino Mary Grace , Esplana Rebedorial Rustom Jim , Bio Borilla Louise Deanna
Published: November 2023

The aim of this study is to develop a prototype that automatically improves the position of a Yagi-Uda antenna using a microcontroller and to illustrate its radiation pattern through the use of MATLAB®. This study is intended for students and professors in the electronics engineering field. This served as their educational materials for teaching antenna system principles and theories. Developmental and experimental methods were used to achieve the objectives. The materials and components generally used in this study are a Yagi-Uda antenna, stepper motor, Arduino Uno, L293D motor shield, USB TV stick tuner, slotted optocoupler, ADS1115, coax cable splitter, speaker stand, and timing belt. The statistical tool used in this study was a Z-test to find out if the experiment results were significant. In testing the effectiveness of the automatic antenna system, the TV display in every increment of 1.8° was taken. It was the basis for the effectiveness of the study. At 5% α/2 level (1.96), the computed z value is 1.76, which is less than 1.96. Therefore, there is no significant difference between the picture quality of the TV display at every angle and the desired angle with maximum reception of the signal with the integration of MATLAB®.
Circulating current suppression and natural voltage balancing using phase-shifted modulation for modular multilevel converter
Authors: Outazkrit Mbarek , EL Aamri Faicel , Jaoide Essaid , Radouane Abdelhadi , Mouhsen Azeddine
Published: November 2023

The challenge of achieving a balanced capacitor voltage is one of the factors affecting the efficient operation of modular multilevel converters (MMC). This paper investigates this challenge through a proposed method that utilizes a high carrier frequency phase-shifted pulse width modulation (PS-PWM) scheme. This method aims to achieve natural balancing without the need for any additional control mechanisms. Moreover, the number of output voltage levels is affected by the phase shift between the carriers of the upper and lower arms. When there is no phase shift, N+1 discrete levels are achieved, but when there is a phase shift, the number of discrete levels increases to 2N+1. The proportional-resonant (PR) controller and moving average filter (MAF) are employed to decrease the capacitor voltage ripples by suppressing the fourth and second harmonics in the circulating currents. The MMC inverter structure is modeled and simulated in the PLECS and MATLAB/Simulink environments to evaluate the impact of this control scheme on the converter’s performance.
An innovative approach for enhancing capacity utilization in point-to-point voice over internet protocol calls
Authors: M. Abualhaj Mosleh , Abu-Shareha Ahmad Adel , Al-Khatib Sumaya Nabil , O. Hiari Mohammad , Al-Mahadeen Layth
Published: November 2023

Voice over internet protocol (VoIP) calls are increasingly transported over computer-based networking due to several factors, such as low call rates. However, point-to-point (P-P) calls, as a division of VoIP, are encountering a capacity utilization issue. The main reason for that is the giant packet header, especially when compared to the runt P-P calls packet payload. Therefore, this research article introduced a method to solve the liability of the giant packet header of the P-P calls. The introduced method is named voice segment compaction (VSC). The VSC method employs the unneeded P-P calls packet header elements to carry the voice packet payload. This, in turn, reduces the size of the voice payload and improves network capacity utilization. The preliminary results demonstrated the importance of the introduced VSC method, while network capacity improved by up to 38.33%.
Wearable with integrated piezoelectric energy harvester for geolocation of people with Alzheimer's
Authors: Linder Rubiños Jimenez Santiago , Herber Grados Gamarra Juan , Nelson Chávez Gallegos Eduardo , Angélica Velasquez Jimenez Linett , Junior Grados Espinoza Herbert , Christian Pesantes Arriola Genaro
Published: November 2023

Alzheimer's is a progressive disease that affects memory, causing disorientation in the patient, which causes them to lose themselves, generating anguish in families who have to resort to expensive searches. The objective of this research was to implement a device that can remotely provide the location of the Alzheimer's patient over a long period to relatives for greater security. For this, in this research, a mobile application was developed that receives information from a wearable that applies the internet of things using ong-range wide area technology to show the patient's real-time location and uses piezoelectrics for greater battery autonomy. The real-time location of the person and the radius of the safe zone in the application were obtained as results, the received signal strength indicator value where the signal was excellent or good had a value of -30 to -89 dB between 0 to 400 meters and the battery discharge time was 11 hours and 44 minutes. It was concluded that the application is interactive, that the piezoelectric system increased the autonomy of the wearable, and that the long-range wide area (LoRa) technology allowed monitoring of the patient's location with great precision at 400 meters.
Transformations for non-destructive evaluation of brix in mango by reflectance spectroscopy and machine learning
Authors: Paiva-Peredo Ernesto , Gonzales-Rodriguez Diego , Trujillo Herrera William , Soria Quijaite Juan Jesús , Quispe-Arpasi Diana , Paulino Christian Ovalle
Published: November 2023

Mango is a very popular climacteric fruit in America and Europe. Among the internal properties of the mango, total soluble solids (TSS) are an adequate indicator to estimate the quality of mango, however, the measurement of this indicator requires destructive tests. Several research have addressed similar issues; they have made use of pre-processing transformations without making it clear which of them is statistically better. Here, we created a new spectral database to build machine learning (ML) models. We analyzed a total of 18 principal component regression (PCR) models and 18 partial least squared regression (PLSR) models, where 4 types of transformations, 3 different feature extractors, and 3 different pre-processing techniques are combined. The research proposes a double cross validation (CV) both to determine the optimal number of components and to obtain the final metrics. The best model had a root mean square error (RMSE) of 1.1382 °Brix and a RMSE on the transformed scale of 0.5140. The best model used 4 components, used y2 transformation, reflectance R as the independent variable and MSC as a pre-processing technique.
Multifactorial Heath-Jarrow-Morton model using principal component analysis
Authors: Garcia Gaona Robinson Alexander , Zapata Quimbayo Carlos Andres
Published: November 2023

In this study, we propose an implementation of the multifactor Heath-Jarrow-Morton (HJM) interest rate model using an approach that integrates principal component analysis (PCA) and Monte Carlo simulation (MCS) techniques. By integrating PCA and MCS with the multifactor HJM model, we successfully capture the principal factors driving the evolution of short-term interest rates in the US market. Additionally, we provide a framework for deriving spot interest rates through parameter calibration and forward rate estimation. For this, we use daily data from the US yield curve from June 2017 to December 2019. The integration of PCA, MCS with multifactor HJM model in this study represents a robust and precise approach to characterizing interest rate dynamics and compared to previous approaches, this method provided greater accuracy and improved understanding of the factors influencing US Treasury Yield interest rates.
A framework for cloud cover prediction using machine learning with data imputation
Authors: Mandal Nabanita , Sarode Tanuja
Published: November 2023

The climatic conditions of a region are affected by multiple factors. These factors are dew point temperature, humidity, wind speed, and wind direction. These factors are closely related to each other. In this paper, the correlation between these factors is studied and an approach has been proposed for data imputation. The idea is to utilize all these features to obtain the prediction of the total cloud cover of a region instead of removing the missing values. Total cloud cover prediction is significant because it affects the agriculture, aviation, and energy sectors. Based on the imputed data which is obtained as the output of the proposed method, a machine learning-based model is proposed. The foundation of this proposed model is the bi-directional approach of the long short-term memory (LSTM) model. It is trained for 8 stations for two different approaches. In the first approach, 80% of the entire data is considered for training and 20% of the data is considered for testing. In the second approach, 90% of the entire data is accounted for training and 10% of the data is accounted for testing. It is observed that in the first approach, the model gives less error for prediction.
A novel smart contract based blockchain with sidechain for electronic voting
Authors: Mullegowda Rakshitha Channarayapatna , Hiremani Nirmala , Birje Mahantesh , Ramaswamy Nataraj Kanathur
Published: November 2023

Several countries have been researching digital voting methods in order to overcome the challenges of paper balloting and physical voting. The recent coronavirus disease 2019 (COVID-19) epidemic has compelled the remote implementation of existing systems and procedures. Online voting will ultimately become the norm just like unified payments interface (UPI) payments and online banking. With digital voting or electronic voting (e-voting) a small bug can cause massive vote rigging. E-voting must be honest, exact, safe, and simple. E-voting is vulnerable to malware, which can disrupt servers. Blockchain’s end-to-end validation solves these problems. Three smart contracts-voter, candidate, and voting-are employed. The problem of fraudulent actions is addressed using vote coins. Vote coins indicate voter status. Sidechain technology complements blockchain. Sidechains improve blockchain functionality by performing operations outside of blockchains and delivering the results to the mainchain. Thus, storing the encrypted vote on the sidechain and using the decrypted result on the mainchain reduces cost. Building access control policies to grant only authorized users’ access to the votes for counting is made simpler by this authorization paradigm. Results of the approach depict the proposed e-voting system improves system security against replay attacks and reduces the processing cost as well as processing time.
A novel multi-biometric technique for verification of secure e-document
Authors: Ali Ammar Mohammed , Farhan Alaa Kadhim
Published: November 2023

Extracting unique and distinctive traits is one of the most important challenges that researchers face, who rely on biometrics to extract exceptional traits for an individual. A large amount of biometric evidence that can be identified and found in various research has been done. In this paper, a biometrics system is proposed that combines the benefits of fingerprinting and uses a novel strategy to combine it with the image-based fingerprint vein feature set. The proposed system is fast and performs effective personal identification by combining both features. The features extracted from the venous print and fingerprint are matched to the nearest neighbors of the authorized person forms to verify the identity of the person. Several experiments have been performed on selected datasets to evaluate the performance of the new biometrics system. The obtained results prove that our proposed system is superior to biometric systems that use the feature of single biometrics. However, our goal is to set up an algorithm that is inexpensive in terms of time complexity while keeping it at the required security levels.
Emoji’s sentiment score estimation using convolutional neural network with multi-scale emoji images
Authors: Kulkongkoon Theerawee , Cooharojananone Nagul , Lipikorn Rajalida
Published: November 2023

Emojis are any small images, symbols, or icons that are used in social media. Several well-known emojis have been ranked and sentiment scores have been assigned to them. These ranked emojis can be used for sentiment analysis; however, many new released emojis have not been ranked and have no sentiment score yet. This paper proposes a new method to estimate the sentiment score of any unranked emotion emoji from its image by classifying it into the class of the most similar ranked emoji and then estimating the sentiment score using the score of the most similar emoji. The accuracy of sentiment score estimation is improved by using multi-scale images. The ranked emoji image data set consisted of 613 classes with 161 emoji images from three different platforms in each class. The images were cropped to produce multi-scale images. The classification and estimation were performed by using convolutional neural network (CNN) with multi-scale emoji images and the proposed voting algorithm called the majority voting with probability (MVP). The proposed method was evaluated on two datasets: ranked emoji images and unranked emoji images. The accuracies of sentiment score estimation for the ranked and unranked emoji test images are 98% and 51%, respectively.
An improved Arabic text classification method using word embedding
Authors: Sabri Tarik , Bahassine Said , El Beggar Omar , Kissi Mohamed
Published: November 2023

Feature selection (FS) is a widely used method for removing redundant or irrelevant features to improve classification accuracy and decrease the model’s computational cost. In this paper, we present an improved method (referred to hereafter as RARF) for Arabic text classification (ATC) that employs the term frequency-inverse document frequency (TF-IDF) and Word2Vec embedding technique to identify words that have a particular semantic relationship. In addition, we have compared our method with four benchmark FS methods namely principal component analysis (PCA), linear discriminant analysis (LDA), chi-square, and mutual information (MI). Support vector machine (SVM), k-nearest neighbors (K-NN), and naive Bayes (NB) are three machine learning based algorithms used in this work. Two different Arabic datasets are utilized to perform a comparative analysis of these algorithms. This paper also evaluates the efficiency of our method for ATC on the basis of performance metrics viz accuracy, precision, recall, and F-measure. Results revealed that the highest accuracy achieved for the SVM classifier applied to the Khaleej-2004 Arabic dataset with 94.75%, while the same classifier recorded an accuracy of 94.01% for the Watan-2004 Arabic dataset.
A novel optimized deep learning method for protein-protein prediction in bioinformatics
Authors: Thareja Preeti , Chillar Rajender Singh
Published: November 2023

Proteins have been shown to perform critical activities in cellular processes and are required for the organism's existence and proliferation. On complicated protein-protein interaction (PPI) networks, conventional centrality approaches perform poorly. Machine learning algorithms based on enormous amounts of data do not make use of biological information's temporal and spatial dimensions. As a result, we developed a sequence-dependent PPI prediction model using an Aquila and shark noses-based hybrid prediction technique. This model operates in two stages: feature extraction and prediction. The features are acquired using the semantic similarity technique for good results. The acquired features are utilized to predict the PPI using hybrid deep networks long short-term memory (LSTM) networks and restricted Boltzmann machines (RBMs). The weighting parameters of these neural networks (NNs) were changed using a novel optimization approach hybrid of aquila and shark noses (ASN), and the results revealed that our proposed ASN-based PPI prediction is more accurate and efficient than other existing techniques.
Device-to-device based path selection for post disaster communication using hybrid intelligence
Authors: Balakrishna Yashoda Mandekolu , Shivashetty Vrinda
Published: November 2023

Public safety network communication methods are concurrence with emerging networks to provide enhanced strategies and services for catastrophe management. If the cellular network is damaged after a calamity, a new-generation network like the internet of things (IoT) is ready to assure network access. In this paper, we suggested a framework of hybrid intelligence to find and re-connect the isolated nodes to the functional area to save life. We look at a situation in which the devices in the hazard region can constantly monitor the radio environment to self-detect the occurrence of a disaster, switch to the device-to-device (D2D) communication mode, and establish a vital connection. The oscillating spider monkey optimization (OSMO) approach forms clusters of the devices in the disaster area to improve network efficiency. The devices in the secluded area use the cluster heads as relay nodes to the operational site. An oscillating particle swarm optimization (OPSO) with a priority-based path encoding technique is used for path discovery. The suggested approach improves the energy efficiency of the network by selecting a routing path based on the remaining energy of the device, channel quality, and hop count, thus increasing network stability and packet delivery.
Energy use and CO2 emissions of the Moroccan transport sector
Authors: Oubnaki Hasnaa , Haouraji Charifa , Mounir Badia , Mounir Ilham , Farchi Abdelmajid
Published: November 2023

In this paper, optimized models based on two different machine learning (ML) methods were developed to forecast the transport energy consumption (TEC) and carbon dioxide (CO2) emissions in Morocco by 2030. More precisely, artificial neural networks (ANN) and support vector regression (SVR) were used for modelling non-linear TEC and CO2 emissions data. This study uses data from 1990 to 2020 and employs various independent parameters, including population, gross domestic product, urbanization rate, evolution of the number of vehicles, and the number of electric vehicle introductions. Four statistical metrics are derived to assess the effectiveness of the ML algorithms used. The forecasts for 2030 were based on six scenarios, including three scenarios for the growth of gross domestic product (GDP) and two scenarios for the evolution of electric cars’ introduction into Moroccan vehicle fleet. The ANN model outputs showed that a decrease in TEC and CO2 emissions is expected until 2030. However, the SVR model predicts outputs values close to those in 2020. The study's results also indicate that: i) TEC and transport CO2 emissions are positively impacted by economic growth in Morocco and ii) electric vehicles will be essential components enabling substantial reductions in overall CO2 emissions in future transport systems.
Reinforcement learning strategies using Monte-Carlo to solve the blackjack problem
Authors: Srinivasaiah Raghavendra , Biju Vinai George , Jankatti Santosh Kumar , Channegowda Ravikumar Hodikehosahally , Jinachandra Niranjana Shravanabelagola
Published: November 2023

Blackjack is a classic casino game in which the player attempts to outsmart the dealer by drawing a combination of cards with face values that add up to just under or equal to 21 but are more incredible than the hand of the dealer he manages to come up with. This study considers a simplified variation of blackjack, which has a dealer and plays no active role after the first two draws. A different game regime will be modeled for everyone to ten multiples of the conventional 52-card deck. Irrespective of the number of standard decks utilized, the game is played as a randomized discrete-time process. For determining the optimum course of action in terms of policy, we teach an agent-a decision maker-to optimize across the decision space of the game, considering the procedure as a finite Markov decision chain. To choose the most effective course of action, we mainly research Monte Carlo-based reinforcement learning approaches and compare them with q-learning, dynamic programming, and temporal difference. The performance of the distinct model-free policy iteration techniques is presented in this study, framing the game as a reinforcement learning problem.
Enhancing COVID-19 forecasting through deep learning techniques and fine-tuning
Authors: López Alba Puelles , Martínez-Béjar Rodrigo , Kusrini Kusrini , Setyanto Arief , Agastya I Made Artha
Published: November 2023

In this study, a comprehensive analysis of classical linear regression forecasting models and deep learning techniques for predicting coronavirus disease of 2019 (COVID-19) pandemic data was presented. Among the deep learning models, the long short-term memory (LSTM) neural network demonstrated superior performance, delivering accurate predictions with minimal errors. The neural network effectively addressed overfitting and underfitting issues through rigorous tuning. However, the diversity of countries and dataset attributes posed challenges in achieving universally optimal predictions. The current study explored the application of the LSTM in predicting healthcare resource demand and optimizing hospital management to provide potential solutions for overcrowding and cost reduction. The results showed the importance of leveraging advanced deep learning techniques for improved COVID-19 forecasting and extending the application of the models to address broader healthcare challenges beyond the pandemic. To further enhance the model performance, future work needed to incorporate additional attributes, such as vaccination rates and immune percentages.
Machine learning for real-time prediction of complications induced by flexible uretero-renoscopy with laser lithotripsy
Authors: Baidada Chafik , Hrimech Hamid , Aatila Mustapha , Lachgar Mohamed , Ommane Younes
Published: November 2023

It is not always easy to predict the outcome of a surgery. Peculiarly, when talking about the risks associated to a given intervention or the possible complications that it may bring about. Thus, predicting those potential complications that may arise during or after a surgery will help minimize risks and prevent failures to the greatest extent possible. Therefore, the objectif of this article is to propose an intelligent system based on machine learning, allowing predicting the complications related to a flexible uretero-renoscopy with laser lithotripsy for the treatment of kidney stones. The proposed method achieved accuracy with 100% for training and, 94.33% for testing in hard voting, 100% for testing and 95.38% for training in soft voting, with only ten optimal features. Additionally, we were able to evaluted the machine learning model by examining the most significant features using the shpley additive explanations (SHAP) feature importance plot, dependency plot, summary plot, and partial dependency plots.
Experimental analysis of intrusion detection systems using machine learning algorithms and artificial neural networks
Authors: Abdulkareem Ademola , Somefun Tobiloba Emmanuel , Mutalub Adesina Lambe , Adeyinka Adewale
Published: November 2023

Since the invention of the internet for military and academic research purposes, it has evolved to meet the demands of the increasing number of users on the network, who have their scope beyond military and academics. As the scope of the network expanded maintaining its security became a matter of increasing importance. With various users and interconnections of more diversified networks, the internet needs to be maintained as securely as possible for the transmission of sensitive information to be one hundred per cent safe; several anomalies may intrude on private networks. Several research works have been released around network security and this research seeks to add to the already existing body of knowledge by expounding on these attacks, proffering efficient measures to detect network intrusions, and introducing an ensemble classifier: a combination of 3 different machine learning algorithms. An ensemble classifier is used for detecting remote to local (R2L) attacks, which showed the lowest level of accuracy when the network dataset is tested using single machine learning models but the ensemble classifier gives an overall efficiency of 99.8%.
Detecting anomalies in security cameras with 3D-convolutional neural network and convolutional long short-term memory
Authors: Mahareek Esraa A. , ElSayed Eman K. , ElDesouky Nahed M. , ElDahshan Kamal A.
Published: November 2023

This paper presents a novel deep learning-based approach for anomaly detection in surveillance films. A deep network that has been trained to recognize objects and human activity in movies forms the foundation of the suggested approach. In order to detect anomalies in surveillance films, the proposed method combines the strengths of 3D-convolutional neural network (3DCNN) and convolutional long short-term memory (ConvLSTM). From the video frames, the 3DCNN is utilized to extract spatiotemporal features,while ConvLSTM is employed to record temporal relationships between frames. The technique was evaluated on five large-scale datasets from the actual world (UCFCrime, XDViolence, UBIFights, CCTVFights, UCF101) that had both indoor and outdoor video clips as well as synthetic datasets with a range of object shapes, sizes, and behaviors. The results further demonstrate that combining 3DCNN with ConvLSTM can increase precision and reduce false positives, achieving a high accuracy and area under the receiver operating characteristic-area under the curve (ROC-AUC) in both indoor and outdoor scenarios when compared to cuttingedge techniques mentioned in the comparison.
Use of Thick Maximum‐Intensity Projection Brain Computed Tomography Angiography for Evaluation of Baseline Collateral Status Improves Interrater Agreement
Authors: Al‐Shamrani Mohamed A. , Bin Amir Hussain , Alotaibi Fawaz F. , Mohamed Gamal , Alokaili Riyadh N. , Al‐Kawi Ammar , Alreshaid Abdulrahman A. , Al‐Zawahmah Mohamed , Al‐Hazzani Adel , Demchuk Andrew M. , Shuaib Ashfaq , Al‐Ajlan Fahad S.
Published: November 2023

Background

In acute ischemic stroke caused by large‐vessel occlusion, tissue viability is dependent on the blood supply from leptomeningeal collaterals until reperfusion is achieved. Rapid and accurate evaluation of baseline collateral status is a key marker of eligibility for endovascular therapy but can be challenging to interpret using source images of the computed tomography angiography (SI‐CTA). Our objective was to assess whether the use of thick maximum‐intensity projection computed tomography angiography (MIP‐CTA) improves interrater agreement for evaluation of baseline collaterals status between stroke trainees and an expert stroke neurologist.

Methods

An expert stroke neurologist and 2 stroke trainees independently reviewed images from 40 brain CTA scans with anterior circulation large‐vessel occlusion and assessed collateral status using the Tan collateral scoring system using SI‐CTA in the first reading and then using MIP‐CTA in the second reading. We calculated interrater agreement and recorded the total time needed in each reading.

Results

Interrater agreement was fair between the 2 stroke fellows and stroke expert when using SI‐CTA (κ=0.45 with 52.5% agreement). After using MIP‐CTA, interrater agreement improved to moderate (κ=0.69 with 70% agreement). The median reading time was 1.89 minutes per scan using SI‐CTA and 1.00 minute per scan using MIP‐CTA (
P
<0.0001).

Conclusions

We show that using MIP‐CTA, when compared with SI‐CTA, shortens interpretation time and improves interrater agreement between stroke trainees and a stroke imaging expert for the evaluation of baseline collaterals in patients presenting with anterior circulation large‐vessel occlusion.
An Issue of Intercultural Communication: An Unknown Letter from the “Sultan of Babylon” to Pope Innocent VIII
Authors: Tafiłowski Piotr
Published: November 2023

In the University of Glasgow Library’s copy of Pius II’s Epistolae familiares (Nuremberg: Anton Koberger, 16 Sept. 1481), recorded on leaf 1–1v one can find a copy of a letter addressed to Pope Innocent VIII that starts with the heading “Soldanus pontifici Romano pro Restauracione Iunioris filii senioris Turchi”. The letter’s sender, who is referred to in the text as the “Sultan of Babylon”, was the Caliph of Cairo, al-Mutawakkil II (Abdul Aziz ibn Yaʿqub ibn Muhammad). The present text discusses the content of the letter and the issues regarding the question of its authorship. To ensure a comprehensive presentation of the argumentation, the paper not only discusses the content of the letter but also explores the wider context in which it was produced, situating it against the wider history of the Mamluk state and Mamluk diplomacy as well as the late medieval tradition of the exchange of correspondence (both real and fictitious) between the Christian and Muslim worlds. It needs underlining that not only has the content of the letter in question hitherto been substantially unknown to scholars, but the letter is furthermore the first discovered correspondent from an ʿAbbasid Caliph sent to the head of Western Christianity.

The paper offers a contribution to research on intercultural communication.

The paper comes with three appendices: a transliteration and a translation of the letter and a facsimile of the original record.
Continuity and Change at Millennial Scale: The Holy Sites of Serindia
Authors: Thum Rian
Published: November 2023

This essay offers an experiment in chronological boundary crossing as way of addressing questions about continuity and change in Central Eurasia. It analyzes the violent transformations of holy sites in Altishahr (more widely known as Eastern Turkistan or southern Xinjiang), examining the 11th-century transition from Buddhist to Muslim rule alongside the 21st-century efforts of the People’s Republic of China to transform sacred Islamic sites into nationalist showpieces and “Silk Road” tourism sites. This juxtaposition calls into question prevailing understandings of the 11th -century transition as a simple refashioning of existing Buddhist sites into Islamic forms, while also placing current Chinese restrictions on Islamic holy sites in a broader historical perspective. Together, these 11th- and 20th century transformations show that shrines act as cultural arbiters, establishing routes by which change has entered Altishahr and stubbornly preserving not just older meanings, but also older ways of knowing. At the same time, they are places where the dynamics of continuous meaning creation come into clear view—where cultural change itself becomes an explicit part of the narratives that bind people together in supposedly stable identity groups such as religions and nations.
The Right to Adequate Housing & brics
Authors: Vols Michel , Hoogenhout Isabella , Schaetzel Max , Schubert Lea
Published: November 2023

This paper analyses the implementation of the right to adequate housing in Brazil, China, and Russia, which are part of the brics group of countries. The study reveals that while Brazil and Russia have legally recognised the right to housing through domestic law and multinational human rights treaties, China has been less willing to do so. Even when international instruments have been implemented, they have not always successfully ensured state accountability for housing rights violations, and few cases have been brought under individual complaint procedures. This study highlights common housing issues faced by each jurisdiction in the practical implementation of the right to housing, which are similar to those encountered by other countries but are often more severe due to the economic, political, and historical circumstances of the countries under review.
Witnessing Colonial Warfare in Early-20th Century Portugal: The Photographic Reportage of the Kwamato Campaign in South Angola (1907)
Authors: Silveira Pereira Hugo
Published: November 2023

In 1907, a Portuguese military expedition was sent to Angola to subdue the Kwamato. Two photographers accompanied the troops and photographed moments of rest, training, and combat. It was the first Portuguese photographic reportage of war. This article analyses a group of photographs of the Kwamato campaign as an example of photography of war. Using a methodology based on discourse analysis in journalism, this article illustrates how photography represented colonial warfare as part of a narrative of imperial dominance where modern technology was crucial, and how this representation was presented and normalised to the Portuguese mainland public in the illustrated press.