bluuebunny/crossref_metadata_2025 huggingface dataset:
A miniature tunable quadrature shadow oscillator with orthogonal control
Authors: Buakaew Seangrawee , Wongtaychatham Chariya
Published: June 2023

This article presents a new design of a quadrature shadow oscillator. The oscillator is realized using one input and two outputs of a second-order filter cell together with external amplifiers in a feedback configuration. The oscillation characteristics are controlled via the external gain without disturbing the internal filter cell, following the concept of the shadow oscillator. The proposed circuit configuration is simple with a small component-count. It consists of, two voltage-different transconductance amplifiers (VDTAs) along with a couple of passive elements. The frequency of oscillation (FO) and the condition of oscillation (CO) are controlled orthogonally via the dc bias current and external gain. Moreover, with the addition of the external gain, the frequency range of oscillation can be further extended. The proposed work is verified by computer simulation with the use of 180 nm complementary metal–oxide–semiconductor (CMOS) model parameters. The simulation gives satisfactory results of two sinusoidal output signals in quadrature with some small total harmonic distortions (THD). In addition, a circuit experiment is performed using the commercial operational transconductance amplifiers LM13700 as the active components. The circuit experiment also demonstrates satisfactory outcome which confirms the validity of the proposed circuit.
Control of 7-phase permanent magnet synchronous motor drive post three failures
Authors: Saleh Kamel , Sumner Mark
Published: June 2023

The article is introducing a new control technique for the 7-phase permanent magnet synchronous motor (PMSM) drive to enhance its robustness against the failure of phases ‘a’ and ‘c’ in addition to the failure of the encoder occurring simultaneously. The article is firstly developing a new multi-dimension space vector pulse width modulation (SVPWM) technique as a part of the fault-tolerant control technique (FTC) to control the magnitudes and angles of the motor’s current after the failures of phases ‘a’ and ‘c’. Moreover, the paper is developing another FTC to obtain a sensorless operation of the 7-phase motor after the failure in the encoder while the phase ‘a’ and ‘c’ are faulted based on the tracking of the saturation saliency. Simulation results prove that the ripple in the speed post the three failures was maintained to be less than 10 rpm compared to 2 rpm when the 7-phase drive is running without faults. In addition to that, the results demonstrated that the motor responded to instant changes in speeds and loads with a dynamic response very close to that obtained when the 7-phase motor ran under healthy operating conditions.
Direct torque control of electric vehicle drives using hybrid techniques
Authors: Marulasiddappa Hallikeri Basappa , Pushparajesh Viswanathan
Published: June 2023

Permanent magnet synchronous motors (PMSM) have the capability of delivering a high torque-to-current ratio, better efficiency and low noise. Because of the above-mentioned factors, PMSMs are commonly employed in variable speed drives, especially in electric vehicle (EV) applications. Without the usage of electromechanical devices, the conventional direct torque control (DTC) can control the speed and torque of PMSM. DTC is highly efficient, fast-tracking and provides smooth torque while limiting its ripple during transient periods. There are many benefits to using a DTC-controlled PMSM drive, including quick and reliable torque reaction, high-performance control speed, and enhanced performance. This research examines the use of the DTC approach to enhance the speed and torque behavior of PMSM. The jellyfish search optimizer (JSO) is used to adjust the DTC's responsiveness and tailor the controller's best gains. In order to train the adaptive neuro-fuzzy inference system (ANFIS) controller, JSO data are utilized. The simulation outcomes demonstrate that the proposed JSO-ANFIS controller achieves a minimal torque ripple of 0.26 Nm and preserves the speed with a harmonic error of 1.21% while contrasted to existing methods.
Fuzzy control of synchronous buck converters utilizing fuzzy inference system for renewable energy applications
Authors: Martinez Fredy , Montiel Holman , Martinez Fernando
Published: June 2023

In the present research, an innovative fuzzy control approach is developed specifically for synchronous buck converters utilized in renewable energy applications. The proposed control strategy effectively manages load changes, nonlinear loads, and input voltage variations while improving both stability and transient response. The method employs a fuzzy inference system (FIS) that integrates adaptive control, feedforward control, and multivariable control to guarantee optimal performance under a wide range of operating conditions. The design of the control scheme involves formulating a rule base connecting input variables to an output variable, which signifies the duty cycle of the switching signal. The rule base is configured to dynamically modify control rules and membership functions in accordance with load conditions, input voltage fluctuations, and other contributing factors. The performance of the control scheme is evaluated in comparison to conventional techniques, such as proportional integral derivative (PID) control. Results indicate that the advanced fuzzy control approach surpasses traditional methods in terms of voltage regulation, stability, and transient response, particularly when faced with variable load conditions and input voltage changes. As a result, this control scheme is highly compatible with renewable energy systems, encompassing solar and wind power installations where input voltage and load conditions may experience considerable fluctuations. This research highlights the potential of the proposed fuzzy control approach to significantly enhance the performance and reliability of renewable energy systems.
Lung sound classification using multiresolution Higuchi fractal dimension measurement
Authors: Rizal Achmad , Hidayat Risanuri , Adi Nugroho Hanung , Anugrah Cahyadi Willy
Published: June 2023

Lung sound is one indicator of abnormalities in the lungs and respiratory tract. Research for automatic lung sound classification has become one of the interests for researchers because lung disease is one of the diseases with the most sufferers in the world. The use of lung sounds as a source of information because of the ease in data acquisition and auscultation is a standard method in examining pulmonary function. This study simulated the potential use of Higuchi fractal dimension (HFD) as a feature extraction method for lung sound classification. HFD calculations were run on a series of k values to generate some HFD values as features. According to the simulation results, the proposed method could produce an accuracy of up to 97.98% for five classes of lung sound data. The results also suggested that the shift in HFD values over the selection of a time interval k can be used for lung sound classification.
Diffusion recursive least squares algorithm based on triangular decomposition
Authors: Prongnuch Sethakarn , Sitjongsataporn Suchada , Wiangtong Theerayod
Published: June 2023

In this paper, diffusion strategies used by QR-decomposition based on recursive least squares algorithm (DQR-RLS) and the sign version of DQR-RLS algorithm (DQR-sRLS) are introduced for distributed networks. In terms of the QR-decomposition method and Cholesky factorization, a modified Kalman vector is given adaptively with the help of unitary rotation that can decrease the complexity from inverse autocorrelation matrix to vector. According to the diffusion strategies, combine-then-adapt (CTA) and adapt-then-combine (ATC) based on DQR-RLS and DQR-sRLS algorithms are proposed with the combination and adaptation steps. To minimize the cost function, diffused versions of CTA-DQR-RLS, ATC-DQR-RLS, CTA-DQR-sRLS and ATC-DiQR-sRLS algorithms are compared. Simulation results depict that the proposed DQR-RLS-based and DQR-sRLS-based algorithms can clearly achieve the better performance than the standard combine-then-adapt-diffusion RLS (CTA-DRLS) and ATC-DRLS mechanisms.
Improved vision-based diagnosis of multi-plant disease using an ensemble of deep learning methods
Authors: Hridoy Rashidul Hasan , Arni Arindra Dey , Haque Aminul
Published: June 2023

Farming and plants are crucial parts of the inward economy of a nation, which significantly boosts the economic growth of a country. Preserving plants from several disease infections at their early stage becomes cumbersome due to the absence of efficient diagnosis tools. Diverse difficulties lie in existing methods of plant disease recognition. As a result, developing a rapid and efficient multi-plant disease diagnosis system is a challenging task. At present, deep learning-based methods are frequently utilized for diagnosing plant diseases, which outperformed existing methods with higher efficiency. In order to investigate plant diseases more accurately, this article addresses an efficient hybrid approach using deep learning-based methods. Xception and ResNet50 models were applied for the classification of plant diseases, and these models were merged using the stacking ensemble learning technique to generate a hybrid model. A multi-plant dataset was created using leaf images of four plants: black gram, betel, Malabar spinach, and litchi, which contains nine classes and 44,972 images. Compared to existing individual convolutional neural networks (CNN) models, the proposed hybrid model is more feasible and effective, which acquired 99.20% accuracy. The outcomes and comparison with existing methods represent that the designed method can acquire competitive performance on the multi-plant disease diagnosis tasks.
Experimental study of four-wave mixing based on a quantum dot semiconductor optical amplifier
Authors: Akylbek Tokhmetov , Akhmet Tussupov , Liliya Tanchenko
Published: June 2023

The article is devoted to the study of four-wave mixing (FWM) in a semiconductor optical amplifier with quantum dots (QD-SOA). Experimental measurements of FWM signals are characterized in nonlinear media of GaAs-based QD-SOA with gain in the 1,550 nm region. Theoretical part of nonlinear effect of FWM is studied and important all-optical communication applications are listed. Experimental studies of a four-wave system are described and an analysis of FWM signals is given for various input powers of pump signals and injection currents. The devices are compared in terms of such parameters as conversion efficiency and signal-to-noise ratio. The results of the study made it possible to reveal the possibility of the effect of FWM signals on useful signals in channels with spectral division multiplexing wavelength division multiplexing (WDM) in the downstream and upstream in a semiconductor optical amplifier. Based on the experimental results, it was concluded that FWM does not affect adjacent channels of WDM signals and does not generate additional optical noise when scaling the WDM gigabit passive optical network (GPON) up to 60 km using semiconductor optical amplifiers (SOAs).
Automotive Ethernet architecture and security: challenges and technologies
Authors: Toghuj Wael , Turab Nidal
Published: June 2023

Vehicle infrastructure must address the challenges posed by today's advances toward connected and autonomous vehicles. To allow for more flexible architectures, high-bandwidth connections and scalability are needed to connect many sensors and electronic control units (ECUs). At the same time, deterministic and low latency is a critical and significant design requirement to support urgent real-time applications in autonomous vehicles. As a recent solution, the time-sensitive network (TSN) was introduced as Ethernet-based amendments in IEEE 802.1 TSN standards to meet those needs. However, it had hurdle to be overcome before it can be used effectively. This paper discusses the latest studies concerning the automotive Ethernet requirements, including transmission delay studies to improve worst-case end-to-end delay and end-to-end jitter. Also, the paper focuses on the securing Ethernet-based in-vehicle networks (IVNs) by reviewing new encryption and authentication methods and approaches.
An effective technique for increasing capacity and improving bandwidth in 5G narrow-band internet of things
Authors: Mohammed Abdulwahid , Mostafa Hassan , Ammar Abdelhady Abdelazim
Published: June 2023

In recent years, the wireless spectrum has become increasingly scarce as demand for wireless services has grown, requiring imaginative approaches to increase capacity within a limited spectral resource. This article proposes a new method that combines modified symbol time compression with orthogonal frequency division multiplexing (MSTC-OFDM), to enhance capacity for the narrow-band internet of things (NB-IoT) system. The suggested method, MSTC-OFDM, is based on the modified symbol time compression (MSTC) technique. The MSTC is a compressed waveform technique that increases capacity by compressing the occupied symbol time without losing bit error rate (BER) performance or data throughput. A comparative analysis is provided between the traditional orthogonal frequency division multiplexing (OFDM) system and the MSTC-OFDM method. The simulation results show that the MSTC-OFDM scheme drastically decreases the symbol time (ST) by 75% compared to a standard OFDM system. As a result, the MSTC-OFDM system offers four times the bit rate of a typical OFDM system using the same bandwidth and modulation but with a little increase in complexity. Moreover, compared to an OFDM system with 16 quadrature amplitude modulation (16QAM-OFDM), the MSTC-OFDM system reduces the signal-to-noise ratio (SNR) by 3.9 dB to transmit the same amount of data.
Virtual data integration for a clinical decision support systems
Authors: Nasir Intedhar Shakir , Mousa Ayad Hameed , Ali Alkhafaji Sakina M. , Abdul Hussein Wafia Shaker , Jasim Zahraa Razzaq , Ali Saeed Qasim
Published: June 2023

Clinical decision support (CDS) supplies clinicians and their patients, and relevant staff with meaningful and timely information intelligently integrated or visualized to enhance health and the health sector. Data is the backbone of decision support systems, especially (clinical) ones. Data integration (either virtual or physical manner) is a powerful technique to manipulate a vast amount of heterogeneous data and prepare it as input for the decision-making process. The difficulties in manipulating data that have a physical data integration technique motivated the decision support developers to tend to data virtualization as a data integration technique. In this paper, a clinical decision support system was developed using the virtual data integration technique. The developed system was evaluated in terms of usability and its capability of providing clinical decision support. The evaluation findings indicate that the proposed system is highly usable and has a positive impact on supporting the clinical decision-making process.
Thermal aware task assignment for multicore processors using genetic algorithm
Authors: Parwez Mohammed , Sulaiman Diary R.
Published: June 2023

Microprocessor power and thermal density are increasing exponentially. The reliability of the processor declined, cooling costs rose, and the processor's lifespan was shortened due to an overheated processor and poor thermal management like thermally unbalanced processors. Thus, the thermal management and balancing of multi-core processors are extremely crucial. This work mostly focuses on a compact temperature model of multicore processors. In this paper, a novel task assignment is proposed using a genetic algorithm to maintain the thermal balance of the cores, by considering the energy expended by each task that the core performs. And expecting the cores’ temperature using the hotspot simulator. The algorithm assigns tasks to the processors depending on the task parameters and current cores’ temperature in such a way that none of the tasks’ deadlines are lost for the earliest deadline first (EDF) scheduling algorithm. The mathematical model was derived, and the simulation results showed that the highest temperature difference between the cores is 8 °C for approximately 14 seconds of simulation. These results validate the effectiveness of the proposed algorithm in managing the hotspot and reducing both temperature and energy consumption in multicore processors.
An approach of ontology and knowledge base for railway maintenance
Authors: Ragala Zaynabe , Retbi Asmaâ , Bennani Samir
Published: June 2023

Maintenance methods have become automated and innovative, especially with the transition to maintenance 4.0. However, social issues such as coronavirus disease of 2019 (COVID-19) and the war in Ukraine have caused significant departures of maintenance experts, resulting in the loss of enormous know-how. As part of this work, we will propose a solution by exploring the knowledge and expertise of these experts for the purpose of sharing and conservation. In this perspective, we have built a knowledge base based on experience and feedback. The proposed method illustrates a case study based on the single excitation configuration interaction (SECI) method to optimally capture the explicit and tacit knowledge of each technician, as well as the theoretical basis, the model of Nonaka and Takeuchi.
Intelligent solution for automatic online exam monitoring
Authors: Moukhliss Ghizlane , Filali Hilali Reda , Belhadaoui Hicham
Published: June 2023

E-learning has shown significant growth in recent years due to its unavoidable benefits in unexpected situations such as the coronavirus disease 2019 (COVID-19) pandemic. Indeed, online exam is a very important component of an online learning program. It allows higher education institutions to assess student learning outcomes. However, cheating in exams is a widespread phenomenon worldwide, which creates several challenges in terms of integrity, reliability and security of online examinations. In this study, we propose a continuous authentication system for online exam. Our intelligent inference system based on machine learning algorithms and rules, detects continuously any inappropriate behavior in order to limit and prevent fraud. The proposed model includes several modules to enhance security, namely the registration module, the continuous students’ identity verification and control module, the live video stream and the end-to-end sessions recording.
Energy-efficient device-to-device communication in internet of things using hybrid optimization technique
Authors: M. Balakrishna Yashoda , Shivashetty Vrinda
Published: June 2023

Device-to-device (D2D) communication has grown into notoriety as a critical component of the internet of things (IoT). One of the primary limitations of IoT devices is restricted battery source. D2D communication is the direct contact between the participating devices that improves the data rate and delivers the data quickly by consuming less battery. An energy-efficient communication method is required to enhance the communication lifetime of the network by reducing the node energy dissipation. The clustering-based D2D communication method is maximally acceptable to boom the durability of a network. The oscillating spider monkey optimization (OSMO) and oscillating particle swarm optimization (OPSO) algorithms are used in this study to improve the selection of cluster heads (CHs) and routing paths for D2D communication. The CHs and D2D communication paths are selected depending on the parameters such as energy consumption, distance, end-to-end delay, link quality and hop count. A simulation environment is designed to evaluate and test the performance of the OSMO-OPSO algorithm with existing D2D communication algorithms (such as the GAPSO-H algorithm, adaptive resource-aware split-learning (ARES), bio-inspired cluster-based routing scheme (Bi-CRS), and European society for medical oncology (ESMO) algorithm). The results proved that the proposed technique outperformed with respect to traditional routing strategies regarding latency, packet delivery, energy efficiency, and network lifetime.
PithaNet: A transfer learning-based approach for traditional pitha classification
Authors: Shakil Shahriar , Akash Atik Asif Khan , Nabi Nusrat , Hassan Mahmudul , Haque Aminul
Published: June 2023

Pitha, pithe, or peetha are all Bangla words referring to a native and traditional food of Bangladesh as well as some areas of India, especially the parts of India where Bangla is the primary language. Numerous types of pithas exist in the culture and heritage of the Bengali and Bangladeshi people. Pithas are traditionally prepared and offered on important occasions in Bangladesh, such as welcoming a bride grooms, or bride, entertaining guests, or planning a special gathering of family, relatives, or friends. The traditional pitha celebration and pitha culture are no longer widely practiced in modern civilization. Consequently, the younger generation is unfamiliar with our traditional pitha culture. In this study, an effective pitha image classification system is introduced. convolutional neural network (CNN) pre-trained models EfficientNetB6, ResNet50, and VGG16 are used to classify the images of pitha. The dataset of traditional popular pithas is collected from different parts of Bangladesh. In this experiment, EfficientNetB6 and ResNet50 show nearly 90% accuracy. The best classification result was obtained using VGG16 with 92% accuracy. The main motive of this study is to revive the Bengali pitha tradition among young people and people worldwide, which will encourage many other researchers to pursue research in this domain.
A profiling-based algorithm for exams’ scheduling problem
Authors: Alrawashdeh Tawfiq , Al-Moghrabi Khaldun G. , Al-Ghonmein Ali M.
Published: June 2023

Typically, the problem of scheduling exams for universities aims to determine a schedule that satisfies logistics constraints, including the number of available exam rooms and the exam delivery mode (online or paper-based). The objective of this problem varies according to the university’s requirements. For example, some universities may seek to minimize operational costs, while others may work to minimize the schedule's length. Consequently, the objective imposed by the university affects the complexity of the problem. In this study, we present a grouping-based approach designed to address the problem of scheduling the exam timetable. The approach begins by profiling the courses’ exams based on their requirements, grouping exams with similar requirements to be scheduled at the same time. Then, an insertion strategy is used to obtain the exam schedule while satisfying the imposed constraints of the targeted university. We applied this approach to the problem of exam scheduling at Al-Hussein Bin Talal University in Jordan and achieved a balanced exam schedule that met all the imposed constraints.
Security and risk analysis in the cloud with software defined networking architecture
Authors: Thatha Venkata Nagaraju , Donepudi Swapna , Safali Miriyala Aruna , Praveen Surapaneni Phani , Tung Nguyen Trong , Cuong Nguyen Ha Huy
Published: June 2023

Cloud computing has emerged as the actual trend in business information technology service models, since it provides processing that is both cost-effective and scalable. Enterprise networks are adopting software-defined networking (SDN) for network management flexibility and lower operating costs. Information technology (IT) services for enterprises tend to use both technologies. Yet, the effects of cloud computing and software defined networking on business network security are unclear. This study addresses this crucial issue. In a business network that uses both technologies, we start by looking at security, namely distributed denial-of-service (DDoS) attack defensive methods. SDN technology may help organizations protect against DDoS assaults provided the defensive architecture is structured appropriately. To mitigate DDoS attacks, we offer a highly configurable network monitoring and flexible control framework. We present a dataset shift-resistant graphic model-based attack detection system for the new architecture. The simulation findings demonstrate that our architecture can efficiently meet the security concerns of the new network paradigm and that our attack detection system can report numerous threats using real-world network data.
Identification of monolingual and code-switch information from English-Kannada code-switch data
Authors: Chundi Ramesh , Hulipalled Vishwanath R. , Simha Jay Bharthish
Published: June 2023

Code-switching is a very common occurrence in social media communication, predominantly found in multilingual countries like India. Using more than one language in communication is known as code-switching or code-mixing. Some of the important applications of code-switch are machine translation (MT), shallow parsing, dialog systems, and semantic parsing. Identifying code-switch and monolingual information is useful for better communication in online networking websites. In this paper, we performed a character level n-gram approach to identify monolingual and code-switch information from English-Kannada social media data. We paralleled various machine learning techniques such as naïve Bayes (NB), support vector classifier (SVC), logistic regression (LR) and neural network (NN) on English-Kannada code-switch (EKCS) data. From the proposed approach, it is observed that the character level n-gram approach provides 1.8% to 4.1% of improvement in terms of Accuracy and 1.6% to 3.8% of improvement in F1-score. Also observed that SVC and NN techniques are outperformed in terms of accuracy (97.9%) and F1-score (98%) with character level n-gram.
Ensembling techniques in solar panel quality classification
Authors: Luu Trong Hieu , Ky Phuc Phan Nguyen , Lam Tran , Yu Zhi-qiu , Lam Van Tinh
Published: June 2023

Solar panel quality inspection is a time consuming and costly task. This study tries to develop as reliable method for evaluating the panels quality by using ensemble technique based on three machine learning models namely logistic regression, support vector machine and artificial neural network. The data in this study came from infrared camera which were captured in dark room. The panels are supplied with direct current (DC) power while the infrared camera is located perpendicular with panel surface. Dataset is divided into four classes where each class represent for a level of damage percentage. The approach is suitable for systems which has limited resources as well as number of training images which is very popular in reality. Result shows that the proposed method performs with the accuracy is higher than 90%.
Improved feature selection using a hybrid side-blotched lizard algorithm and genetic algorithm approach
Authors: Abdel-aal Amr , El-Henawy Ibrahim
Published: June 2023

Feature selection entails choosing the significant features among a wide collection of original features that are essential for predicting test data using a classifier. Feature selection is commonly used in various applications, such as bioinformatics, data mining, and the analysis of written texts, where the dataset contains tens or hundreds of thousands of features, making it difficult to analyze such a large feature set. Removing irrelevant features improves the predictor performance, making it more accurate and cost-effective. In this research, a novel hybrid technique is presented for feature selection that aims to enhance classification accuracy. A hybrid binary version of side-blotched lizard algorithm (SBLA) with genetic algorithm (GA), namely SBLAGA, which combines the strengths of both algorithms is proposed. We use a sigmoid function to adapt the continuous variables values into a binary one, and evaluate our proposed algorithm on twenty-three standard benchmark datasets. Average classification accuracy, average number of selected features and average fitness value were the evaluation criteria. According to the experimental results, SBLAGA demonstrated superior performance compared to SBLA and GA with regards to these criteria. We further compare SBLAGA with four wrapper feature selection methods that are widely used in the literature, and find it to be more efficient.
U-Net transfer learning backbones for lesions segmentation in breast ultrasound images
Authors: Bal-Ghaoui Mohamed , El Yousfi Alaoui My Hachem , Jilbab Abdelilah , Bourouhou Abdennacer
Published: June 2023

Breast ultrasound images are highly valuable for the early detection of breast cancer. However, the drawback of these images is low-quality resolution and the presence of speckle noise, which affects their interpretability and makes them radiologists’ expertise-dependent. As medical images, breast ultrasound datasets are scarce and imbalanced, and annotating them is tedious and time-consuming. Transfer learning, as a deep learning technique, can be used to overcome the dataset deficiency in available images. This paper presents the implementation of transfer learning U-Net backbones for the automatic segmentation of breast ultrasound lesions and implements a threshold selection mechanism to deliver optimal generalized segmentation results of breast tumors. The work uses the public breast ultrasound images (BUSI) dataset and implements ten state-of-theart candidate models as U-Net backbones. We have trained these models with a five-fold cross-validation technique on 630 images with benign and malignant cases. Five out of ten models showed good results, and the best U-Net backbone was found to be DenseNet121. It achieved an average Dice coefficient of 0.7370 and a sensitivity of 0.7255. The model’s robustness was also evaluated against normal cases, and the model accurately detected 72 out of 113 images, which is higher than the four best models.
A deep convolutional structure-based approach for accurate recognition of skin lesions in dermoscopy images
Authors: Fawzy Shimaa , Moustafa Hossam El-Din , AbdelHay Ehab H. , Ata Mohamed Maher
Published: June 2023

One-third of all cancer diagnoses worldwide are skin malignancies. One of the most common tumors, skin cancer can develop from a variety of dermatological conditions and is subdivided into different categories based on its textile, color, body, and other morphological characteristics. The most effective strategy to lower the mortality rate of melanoma is early identification because skin cancer incidence has been on the rise recently. In order to categorize dermoscopy images into the four diagnosis classifications of melanoma, benign, malignant, and human against machine (HAM) not melanoma, this research suggests a computer-aided diagnosis (CAD) system. Experimental results show that the suggested approach enabled 97.25% classification accuracy. In order to automate the identification of skin cancer and expedite the diagnosis process in order to save a life, the proposed technique offers a less complex and cutting-edge framework.
Adaptive traffic lights based on traffic flow prediction using machine learning models
Authors: Moumen Idriss , Abouchabaka Jaafar , Rafalia Najat
Published: June 2023

Traffic congestion prediction is one of the essential components of intelligent transport systems (ITS). This is due to the rapid growth of population and, consequently, the high number of vehicles in cities. Nowadays, the problem of traffic congestion attracts more and more attention from researchers in the field of ITS. Traffic congestion can be predicted in advance by analyzing traffic flow data. In this article, we used machine learning algorithms such as linear regression, random forest regressor, decision tree regressor, gradient boosting regressor, and K-neighbor regressor to predict traffic flow and reduce traffic congestion at intersections. We used the public roads dataset from the UK national road traffic to test our models. All machine learning algorithms obtained good performance metrics, indicating that they are valid for implementation in smart traffic light systems. Next, we implemented an adaptive traffic light system based on a random forest regressor model, which adjusts the timing of green and red lights depending on the road width, traffic density, types of vehicles, and expected traffic. Simulations of the proposed system show a 30.8% reduction in traffic congestion, thus justifying its effectiveness and the interest of deploying it to regulate the signaling problem in intersections.
Regional feature learning using attribute structural analysis in bipartite attention framework for vehicle re-identification
Authors: Sherin Cynthia , Jayavel Kayalvizhi
Published: June 2023

Vehicle re-identification identifies target vehicles using images obtained by numerous non-overlapping real-time surveillance cameras. The effectiveness of re-identification is further challenging because of illumination changes, pose differences of captured images, and resolution. Fine-grained appearance changes in vehicles are recognized in addition to the coarse-grained characteristics like color of the vehicle along with model, and other custom features like logo stickers, annual service signs, and hangings to overcome these challenges. To prove the efficiency of our proposed bipartite attention framework, a novel dataset called Attributes27 which has 27 labelled attributes for each class are created. Our framework contains three major sections: The first section where the overall and semantic characteristics of every individual vehicle image are extracted by a double branch convolutional neural network (CNN) layer. Secondly, to identify the region of interests (ROIs) each branch has a self-attention block linked to it. Lastly to extract the regional features from the obtained ROIs, a partition-alignment block is deployed. The results of our proposed system’s evaluation on the Attributes27 and VeRi-776 datasets has highlighted significant regional attributes of each vehicle and improved the accuracy. Attributes27 and VeRi-776 datasets exhibits 98.5% and 84.3% accuracy respectively which are comparatively higher than the existing methods with 78.6% accuracy.
Brain cone beam computed tomography image analysis using ResNet50 for collateral circulation classification
Authors: Ali Nur Hasanah , Abdullah Abdul Rahim , Saad Norhashimah Mohd , Muda Ahmad Sobri
Published: June 2023

Treatment of stroke patients can be effectively carried out with the help of collateral circulation performance. Collateral circulation scoring as it is now used is dependent on visual inspection, which can lead to an inter- and
 intra-rater discrepancy. In this study, a collateral circulation classification using the ResNet50 was analyzed by using cone beam computed tomography (CBCT) images for the ischemic stroke patient. The remarkable performance of deep learning classification helps neuroradiologists with fast image classification. A pre-trained deep network ResNet50 was applied to extract robust features and learn the structure of CBCT images in their convolutional layers. Next, the classification layer of the ResNet50 was performed into binary classification as “good” and “poor” classes. The images were divided by 80:20 for training and testing. The empirical results support the claim that the application of ResNet50 offers consistent accuracy, sensitivity, and specificity values. The performance value of the classification accuracy was 76.79%. The deep learning approach was employed to unveil how biological image analysis could generate incredibly dependable and repeatable outcomes. The experiments performed on CBCT images evidenced that the proposed ResNet50 using convolutional neural network (CNN) architecture is indeed effective in classifying collateral circulation.
Early detection of dysphoria using electroencephalogram affective modelling
Authors: Kamaruddin Norhaslinda , Mohd Nasir Mohd Hafiz , Wahab Abdul , Harris Jr. Frederick C.
Published: June 2023

Dysphoria is a trigger point for maladjusted individuals who cannot cope with disappointments and crushed expectations, resulting in negative emotions if it is not detected early. Individuals who suffer from dysphoria tend to deny their mental state. They try to hide, suppress, or ignore the symptoms, making one feel worse, unwanted, and unloved. Psychologists and psychiatrists identify dysphoria using standardized instruments like questionnaires and interviews. These methods can boast a high success rate. However, the limited number of trained psychologists and psychiatrists and the small number of health institutions focused on mental health limit access to early detection. In addition, the negative connotation and taboo about dysphoria discourage the public from openly seeking help. An alternative approach to collecting ‘pure’ data is proposed in this paper. The brain signals are captured using the electroencephalogram as the input to the machine learning approach to detect negative emotions. It was observed from the experimental results that participants who scored severe dysphoria recorded ‘fear’ emotion even before stimuli were presented during the eyes-close phase. This finding is crucial to further understanding the effect of dysphoria and can be used to study the correlation between dysphoria and negative emotions.
Breast cancer classification with histopathological image based on machine learning
Authors: Leow Jia Rong , Khoh Wee How , Pang Ying Han , Yap Hui Yen
Published: June 2023

Breast cancer represents one of the most common reasons for death in the worldwide. It has a substantially higher death rate than other types of cancer. Early detection can enhance the chances of receiving proper treatment and survival. In order to address this problem, this work has provided a convolutional neural network (CNN) deep learning (DL) based model on the classification that may be used to differentiate breast cancer histopathology images as benign or malignant. Besides that, five different types of pre-trained CNN architectures have been used to investigate the performance of the model to solve this problem which are the residual neural network-50 (ResNet-50), visual geometry group-19 (VGG-19), Inception-V3, and AlexNet while the ResNet-50 is also functions as a feature extractor to retrieve information from images and passed them to machine learning algorithms, in this case, a random forest (RF) and k-nearest neighbors (KNN) are employed for classification. In this paper, experiments are done using the BreakHis public dataset. As a result, the ResNet-50 network has the highest test accuracy of 97% to classify breast cancer images.
Reliable and efficient webserver management for task scheduling in edge-cloud platform
Authors: Sangani Sangeeta , Patil Rudragoud
Published: June 2023

The development in the field of cloud webserver management for the execution of the workflow and meeting the quality-of-service (QoS) prerequisites in a distributed cloud environment has been a challenging task. Though, internet of things (IoT) of work presented for the scheduling of the workflow in a heterogeneous cloud environment. Moreover, the rapid development in the field of cloud computing like edge-cloud computing creates new methods to schedule the workflow in a heterogenous cloud environment to process different tasks like IoT, event-driven applications, and different network applications. The current methods used for workflow scheduling have failed to provide better trade-offs to meet reliable performance with minimal delay. In this paper, a novel web server resource management framework is presented namely the reliable and efficient webserver management (REWM) framework for the edge-cloud environment. The experiment is conducted on complex bioinformatic workflows; the result shows the significant reduction of cost and energy by the proposed REWM in comparison with standard webserver management methodology.
Computer-aided automated detection of kidney disease using supervised learning technique
Authors: Bhaskar Navaneeth , Tupe-Waghmare Priyanka , Nikam Shobha S. , Khedkar Rakhi
Published: June 2023

In this paper, we propose an efficient home-based system for monitoring chronic kidney disease (CKD). As non-invasive disease identification approaches are gaining popularity nowadays, the proposed system is designed to detect kidney disease from saliva samples. Salivary diagnosis has advanced its popularity over the last few years due to the non-invasive sample collection technique. The use of salivary components to monitor and detect kidney disease is investigated through an experimental investigation. We measured the amount of urea in the saliva sample to detect CKD. Further, this article explains the use of predictive analysis using machine learning techniques and data analytics in remote healthcare management. The proposed health monitoring system classified the samples with an accuracy of 97.1%. With internet facilities available everywhere, this methodology can offer better healthcare services, with real-time decision support in remote monitoring platform.
Predicting Stroke in Heart Failure and Preserved Ejection Fraction Without Atrial Fibrillation
Authors: Kondo Toru , Jering Karola S. , Jhund Pardeep S. , Anand Inder S. , Desai Akshay S. , Lam Carolyn S.P. , Maggioni Aldo P. , Martinez Felipe A. , Packer Milton , Petrie Mark C. , Pfeffer Marc A. , Redfield Margaret M. , Rouleau Jean L. , van Veldhuisen Dirk J. , Zannad Faiez , Zile Michael R. , Solomon Scott D. , McMurray John J.V.
Published: June 2023

BACKGROUND:

The rate of stroke in patients with heart failure (HF) and preserved ejection fraction but without atrial fibrillation (AF), is uncertain as is whether it is possible to reliably predict the risk of stroke in these patients.

METHODS:

We validated a previously developed simple risk model for stroke among patients enrolled in the I-Preserve trial (Irbesartan in Heart Failure With Preserved Systolic Function) and PARAGON-HF trial (Efficacy and Safety of LCZ696 Compared to Valsartan, on Morbidity and Mortality in Heart Failure Patients With Preserved Ejection Fraction). The risk model consisted of 3 variables: history of previous stroke, insulin-treated diabetes, and plasma N-terminal pro-B-type natriuretic peptide level.

RESULTS:

Of the 8924 patients included in the pooled trial dataset, 5126 patients did not have AF at baseline. Among patients without AF, 190 (3.7%) experienced a stroke over a median follow-up of 3.6 years (rate 10.5 per 1000 patient-years). The risk for stroke increased with increasing risk score: second tertile hazard ratio, 1.78 (95% CI, 1.17–2.71); third tertile hazard ratio, 3.03 (95% CI, 2.06–4.47), with the first tertile as reference. For patients in the third tertile, the occurrence rate of stroke was 17.7 per 1000 patient-years, similar to that in patients with AF not receiving anticoagulation (20.7 per 1000 patient-years), and those with AF who were receiving anticoagulation (14.5 per 1000 patient-years). Model discrimination was good with a C index of 0.81 (0.68–0.91) and a simple score could be created from the model.

CONCLUSIONS:

A simple risk model can detect a subset of HF and preserved ejection fraction patients without AF who have a higher risk for stroke. The balance of risk-to-benefit in these individuals may justify the use of prophylactic anticoagulation, but this hypothesis needs to be prospectively evaluated.

REGISTRATION:

URL:
https://www.clinicaltrials.gov
; Unique identifiers: NCT00095238 and NCT01920711.
Pathogenic Roles of Cardiac Fibroblasts in Pediatric Dilated Cardiomyopathy
Authors: Tsuru Hirofumi , Yoshihara Chika , Suginobe Hidehiro , Matsumoto Mizuki , Ishii Yoichiro , Narita Jun , Ishii Ryo , Wang Renjie , Ueyama Atsuko , Ueda Kazutoshi , Hirose Masaki , Hashimoto Kazuhisa , Nagano Hiroki , Tanaka Ryosuke , Okajima Takaharu , Ozono Keiichi , Ishida Hidekazu
Published: June 2023

Background

Dilated cardiomyopathy (DCM) is a major cause of heart failure in children. Despite intensive genetic analyses, pathogenic gene variants have not been identified in most patients with DCM, which suggests that cardiomyocytes are not solely responsible for DCM. Cardiac fibroblasts (CFs) are the most abundant cell type in the heart. They have several roles in maintaining cardiac function; however, the pathological role of CFs in DCM remains unknown.

Methods and Results

Four primary cultured CF cell lines were established from pediatric patients with DCM and compared with 3 CF lines from healthy controls. There were no significant differences in cellular proliferation, adhesion, migration, apoptosis, or myofibroblast activation between DCM CFs compared with healthy CFs. Atomic force microscopy revealed that cellular stiffness, fluidity, and viscosity were not significantly changed in DCM CFs. However, when DCM CFs were cocultured with healthy cardiomyocytes, they deteriorated the contractile and diastolic functions of cardiomyocytes. RNA sequencing revealed markedly different comprehensive gene expression profiles in DCM CFs compared with healthy CFs. Several humoral factors and the extracellular matrix were significantly upregulated or downregulated in DCM CFs. The pathway analysis revealed that extracellular matrix receptor interactions, focal adhesion signaling, Hippo signaling, and transforming growth factor‐β signaling pathways were significantly affected in DCM CFs. In contrast, single‐cell RNA sequencing revealed that there was no specific subpopulation in the DCM CFs that contributed to the alterations in gene expression.

Conclusions

Although cellular physiological behavior was not altered in DCM CFs, they deteriorated the contractile and diastolic functions of healthy cardiomyocytes through humoral factors and direct cell–cell contact.
Southern Lights: Octavio Paz's “Glimpses of India” and the Art of Relation
Authors: Khullar Sonal
Published: June 2023

This article analyzes the articulation of south-south relation in Octavio Paz's In Light of India (1995) and A Tale of Two Gardens: Poems from India, 1952-1995 (1997), works of prose and poetry that traverse the antipodes of Mexico and India. These works emphasize partial viewing, repeated comparison, and cultivated sense-perception. They model a poetics of the glimpse, an effect of the play of light and shadow and a privileged mode of seeing for Paz. To glimpse is to see without clarity, control, or complete knowledge. It is to find oneself in the other. Paz's writing anticipates twenty-first-century projects that relate artists and intellectuals in the global south such as the School of the South (2022) programs of the Johannesburg Contemporary Art Foundation inspired by Uruguayan artist Joaquín Torres-García's formulation from 1935. His comparative method suggests new directions for global art history and postcolonial critique.
Color Charts
Authors: Abidi Bani
Published: June 2023

In the days before the arrival of the internet, Western art history education in Pakistan was mostly disseminated through black and white photocopies of original publications. The glossy pages of art books were transformed into rough copies in varying shades of gray. Rather than understanding this as a disadvantage, I propose, conversely, that this practice constituted a form of radical piracy—the blurred, partial and often completely indecipherable nature of this material proved to be, in fact, a kind of liberation.