bluuebunny/crossref_metadata_2025 huggingface dataset:
Unobtrusive hand gesture recognition using ultra-wide band radar and deep learning
Authors: Korti Djazila Souhila , Slimane Zohra
Published: September 2023

Hand function after stroke injuries is not regained rapidly and requires physical rehabilitation for at least 6 months. Due to the heavy burden on the healthcare system, assisted rehabilitation is prescribed for a limited time, whereas so-called home rehabilitation is offered. It is therefore essential to develop robust solutions that facilitate monitoring while preserving the privacy of patients in a home-based setting. To meet these expectations, an unobtrusive solution based on radar sensing and deep learning is proposed. The multi-input multi-output convolutional eXtra trees (MIMO-CxT) is a new deep hybrid model used for hand gesture recognition (HGR) with impulse-radio ultra-wide band (IR-UWB) radars. It consists of a lightweight architecture based on a multi-input convolutional neural network (CNN) used in a hybrid configuration with extremely randomized trees (ETs). The model takes data from multiple sensors as input and processes them separately. The outputs of the CNN branches are concatenated before the prediction is made by the ETs. Moreover, the model uses depthwise separable convolution layers, which reduce computational cost and learning time while maintaining high performance. The model is evaluated on a publicly available dataset of gestures collected by three IR-UWB radars and achieved an average accuracy of 98.86%.
Performance evaluation of transfer learning based deep convolutional neural network with limited fused spectro-temporal data for land cover classification
Authors: Hasanat Muhammad , Khan Waleed , Minallah Nasru , Aziz Najam , Durrani Awab-Ur-Rashid
Published: September 2023

Deep learning (DL) techniques are effective in various applications, such as parameter estimation, image classification, recognition, and anomaly detection. They excel with abundant training data but struggle with limited data. To overcome this, transfer learning is commonly used, leveraging complex learning abilities, saving time, and handling limited labeled data. This study assesses a transfer learning (TL)-based pre-trained “deep convolutional neural network (DCNN)” for classifying land use land cover using a limited and imbalanced dataset of fused spectro-temporal data. It compares the performance of shallow artificial neural networks (ANNs) and deep convolutional neural networks, utilizing multi-spectral sentinel-2 and high-resolution planet scope data. Both machine learning and deep learning algorithms successfully classified the fused data, but the transfer learning-based deep convolutional neural network outperformed the artificial neural network. The evaluation considered a weighted average of F1-score and overall classification accuracy. The transfer learning-based convolutional neural network achieved a weighted average F1-score of 0.92 and a classification accuracy of 0.93, while the artificial neural network achieved a weighted average F1-score of 0.87 and a classification accuracy of 0.89. These results highlight the superior performance of the transfer learned convolutional neural network on a limited and imbalanced dataset compared to the traditional artificial neural network algorithm.
Survey analysis for optimization algorithms applied to electroencephalogram
Authors: Hakem Ekram , Al-Shammary Dhiah , Mahdi Ahmed M.
Published: September 2023

This paper presents a survey for optimization approaches that analyze and classify Electroencephalogram (EEG) signals. The automatic analysis of EEG presents a significant challenge due to the high-dimensional data volume. Optimization algorithms seek to achieve better accuracy by selecting practical features and reducing unwanted features. Forty-seven reputable research papers are provided in this work, emphasizing the developed and executed techniques divided into seven groups based on the applied optimization algorithm particle swarm optimization (PSO), ant colony optimization (ACO), artificial bee colony (ABC), grey wolf optimizer (GWO), Bat, Firefly, and other optimizer approaches). The main measures to analyze this paper are accuracy, precision, recall, and F1-score assessment. Several datasets have been utilized in the included papers like EEG Bonn University, CHB-MIT, electrocardiography (ECG) dataset, and other datasets. The results have proven that the PSO and GWO algorithms have achieved the highest accuracy rate of around 99% compared with other techniques.
Compact optimized deep learning model for edge: a review
Authors: Naveen Soumyalatha , Kounte Manjunath R.
Published: September 2023

Most real-time computer vision applications, such as pedestrian detection, augmented reality, and virtual reality, heavily rely on convolutional neural networks (CNN) for real-time decision support. In addition, edge intelligence is becoming necessary for low-latency real-time applications to process the data at the source device. Therefore, processing massive amounts of data impact memory footprint, prediction time, and energy consumption, essential performance metrics in machine learning based internet of things (IoT) edge clusters. However, deploying deeper, dense, and hefty weighted CNN models on resource-constraint embedded systems and limited edge computing resources, such as memory, and battery constraints, poses significant challenges in developing the compact optimized model. Reducing the energy consumption in edge IoT networks is possible by reducing the computation and data transmission between IoT devices and gateway devices. Hence there is a high demand for making energy-efficient deep learning models for deploying on edge devices. Furthermore, recent studies show that smaller compressed models achieve significant performance compared to larger deep-learning models. This review article focuses on state-of-the-art techniques of edge intelligence, and we propose a new research framework for designing a compact optimized deep learning (DL) model deployment on edge devices.
Tiny-YOLO distance measurement and object detection coordination system for the BarelangFC robot
Authors: Susanto Susanto , Ricardo Silitonga Jony Arif , Analia Riska , Jamzuri Eko Rudiawan , Pamungkas Daniel Sutopo
Published: September 2023

A humanoid robot called BarelangFC was designed to take part in the Kontes Robot Indonesia (KRI) competition, in the robot coordination division. In this division, each robot is expected to recognize its opponents and to pass the ball towards a team member to establish coordination between the robots. In order to achieve this team coordination, a fast and accurate system is needed to detect and estimate the other robot’s position in real time. Moreover, each robot has to estimate its team members’ locations based on its camera reading, so that the ball can be passed without error. This research proposes a Tiny-YOLO deep learning method to detect the location of a team member robot and presents a real-time coordination system using a ZED camera. To establish the coordinate system, the distance between the robots was estimated using a trigonometric equation to ensure that the robot was able to pass the ball towards another robot. To verify our method, real-time experiments was carried out using an NVDIA Jetson NX Xavier, and the results showed that the robot could estimate the distance correctly before passing the ball toward another robot.
Design and development of a fuzzy explainable expert system for a diagnostic robot of COVID-19
Authors: Beggar Omar El , Ramdani Mohammed , Kissi Mohamed
Published: September 2023

Expert systems have been widely used in medicine to diagnose different diseases. However, these rule-based systems only explain why and how their outcomes are reached. The rules leading to those outcomes are also expressed in a machine language and confronted with the familiar problems of coverage and specificity. This fact prevents procuring expert systems with fully human-understandable explanations. Furthermore, early diagnosis involves a high degree of uncertainty and vagueness which constitutes another challenge to overcome in this study. This paper aims to design and develop a fuzzy explainable expert system for coronavirus disease-2019 (COVID-19) diagnosis that could be incorporated into medical robots. The proposed medical robotic application deduces the likelihood level of contracting COVID-19 from the entered symptoms, the personal information, and the patient's activities. The proposal integrates fuzzy logic to deal with uncertainty and vagueness in diagnosis. Besides, it adopts a hybrid explainable artificial intelligence (XAI) technique to provide different explanation forms. In particular, the textual explanations are generated as rules expressed in a natural language while avoiding coverage and specificity problems. Therefore, the proposal could help overwhelmed hospitals during the epidemic propagation and avoid contamination using a solution with a high level of explicability.
Hybrid chaos-based image encryption algorithm using Chebyshev chaotic map with deoxyribonucleic acid sequence and its performance evaluation
Authors: Sekar Jai Ganesh , Periyathambi Ezhumalai , Chokkalingam Arun
Published: September 2023

The media content shared on the internet has increased tremendously nowadays. The streaming service has major role in contributing to internet traffic all over the world. As the major content shared are in the form of images and rapid increase in computing power a better and complex encryption standard is needed to protect this data from being leaked to unauthorized person. Our proposed system makes use of chaotic maps, deoxyribonucleic acid (DNA) coding and ribonucleic acid (RNA) coding technique to encrypt the image. As videos are nothing but collection of images played at the rate of minimum 30 frames/images per second, this methodology can also be used to encrypt videos. The complexity and dynamic nature of chaotic systems makes decryption of content by unauthorized personal difficult. The hybrid usage of chaotic systems along with DNA and RNA sequencing improves the encryption efficiency of the algorithm and also makes it possible to decrypt the images at the same time without consuming too much of computation power.
Enhancing speaker verification accuracy with deep ensemble learning and inclusion of multifaceted demographic factors
Authors: Palsapure Pranita Niraj , Rajeswari Rajeswari , Kempegowda Sandeep Kumar
Published: September 2023

Effective speaker identification is essential for achieving robust speaker recognition in real-world applications such as mobile devices, security, and entertainment while ensuring high accuracy. However, deep learning models trained on large datasets with diverse demographic and environmental factors may lead to increased misclassification and longer processing times. This study proposes incorporating ethnicity and gender information as critical parameters in a deep learning model to enhance accuracy. Two convolutional neural network (CNN) models classify gender and ethnicity, followed by a Siamese deep learning model trained with critical parameters and additional features for speaker verification. The proposed model was tested using the VoxCeleb 2 database, which includes over one million utterances from 6,112 celebrities. In an evaluation after 500 epochs, equal error rate (EER) and minimum decision cost function (minDCF) showed notable results, scoring 1.68 and 0.10, respectively. The proposed model outperforms existing deep learning models, demonstrating improved performance in terms of reduced misclassification errors and faster processing times.
A deep learning-based mobile app system for visual identification of tomato plant disease
Authors: Wang Aurelius Ryo , Shabrina Nabila Husna
Published: September 2023

Tomato is one of many horticulture crops in Indonesia which plays a vital role in supplying public food needs. However, tomato is a very susceptible plant to pests and diseases caused by bacteria and fungus. The infected diseases should be isolated as soon as it was detected. Therefore, developing a reliable and fast system is essential for controlling tomato pests and diseases. The deep learning-based application can help to speed up the identification of tomato disease as it can perform direct identification from the image. In this research, EfficientNetB0 was implemented to perform multi-class tomato plant disease classification. The model was then deployed to an android-based application using machine learning (ML) kit library. The proposed system obtained satisfactory results, reaching an average accuracy of 91.4%.
Best-worst northern goshawk optimizer: a new stochastic optimization method
Authors: Kusuma Purba Daru , Hasibuan Faisal Candrasyah
Published: September 2023

This study introduces a new metaheuristic method: the best-worst northern goshawk optimizer (BW-NGO). This algorithm is an enhanced version of the northern goshawk optimizer (NGO). Every BW-NGO iteration consists of four phases. First, each agent advances toward the best agent and away from the worst agent. Second, each agent moves relatively to the agent selected at random. Third, each agent conducts a local search. Fourth, each agent traces the space at random. The first three phases are mandatory, while the fourth phase is optional. Simulation is performed to assess the performance of BW-NGO. In this simulation, BW-NGO is confronted with four algorithms: particle swarm optimization (PSO), pelican optimization algorithm (POA), golden search optimizer (GSO), and northern goshawk optimizer (NGO). The result exhibits that BW-NGO discovers an acceptable solution for the 23 benchmark functions. BW-NGO is better than PSO, POA, GSO, and NGO in consecutively optimizing 22, 20, 15, and 11 functions. BW-NGO can discover the global optimal solution for three functions.
A simplified predictive framework for cost evaluation to fault assessment using machine learning
Authors: Rai Deepti , Prashant Jyothi Arcot
Published: September 2023

Software engineering is an integral part of any software development scheme which frequently encounters bugs, errors, and faults. Predictive evaluation of software fault contributes towards mitigating this challenge to a large extent; however, there is no benchmarked framework being reported in this case yet. Therefore, this paper introduces a computational framework of the cost evaluation method to facilitate a better form of predictive assessment of software faults. Based on lines of code, the proposed scheme deploys adopts a machine-learning approach to address the perform predictive analysis of faults. The proposed scheme presents an analytical framework of the correlation-based cost model integrated with multiple standards machine learning (ML) models, e.g., linear regression, support vector regression, and artificial neural networks (ANN). These learning models are executed and trained to predict software faults with higher accuracy. The study considers assessing the outcomes based on error-based performance metrics in detail to determine how well each learning model performs and how accurate it is at learning. It also looked at the factors contributing to the training loss of neural networks. The validation result demonstrates that, compared to logistic regression and support vector regression, neural network achieves a significantly lower error score for software fault prediction.
Feature selection for sky image classification based on self adaptive ant colony system algorithm
Authors: Petwan Montha , Ku-Mahamud Ku Ruhana
Published: September 2023

Statistical-based feature extraction has been typically used to purpose obtaining the important features from the sky image for cloud classification. These features come up with many kinds of noise, redundant and irrelevant features which can influence the classification accuracy and be time consuming. Thus, this paper proposed a new feature selection algorithm to distinguish significant features from the extracted features using an ant colony system (ACS). The informative features are extracted from the sky images using a Gaussian smoothness standard deviation, and then represented in a directed graph. In feature selection phase, the self-adaptive ACS (SAACS) algorithm has been improved by enhancing the exploration mechanism to select only the significant features. Support vector machine, kernel support vector machine, multilayer perceptron, random forest, k-nearest neighbor, and decision tree were used to evaluate the algorithms. Four datasets are used to test the proposed model: Kiel, Singapore whole-sky imaging categories, MGC Diagnostics Corporation, and greatest common divisor. The SAACS algorithm is compared with six bio-inspired benchmark feature selection algorithms. The SAACS algorithm achieved classification accuracy of 95.64% that is superior to all the benchmark feature selection algorithms. Additionally, the Friedman test and Mann-Whitney U test are employed to statistically evaluate the efficiency of the proposed algorithms.
Optimized stacking ensemble for early-stage diabetes mellitus prediction
Authors: Aman Aman , Chhillar Rajender Singh
Published: September 2023

This paper presents an optimized stacking-based hybrid machine learning approach for predicting early-stage diabetes mellitus (DM) using the PIMA Indian diabetes (PID) dataset and early-stage diabetes risk prediction (ESDRP) dataset. The methodology involves handling missing values through mean imputation, balancing the dataset using the synthetic minority over-sampling technique (SMOTE), normalizing features, and employing a stratified train-test split. Logistic regression (LR), naïve Bayes (NB), AdaBoost with support vector machines (AdaBoost+SVM), artificial neural network (ANN), and k-nearest neighbors (k-NN) are used as base learners (level 0), while random forest (RF) meta-classifier serves as the level 1 model to combine their predictions. The proposed model achieves impressive accuracy rates of 99.7222% for the ESDRP dataset and 94.2085% for the PID dataset, surpassing existing literature by absolute differences ranging from 10.2085% to 16.7222%. The stacking-based hybrid model offers advantages for early-stage DM prediction by leveraging multiple base learners and a meta-classifier. SMOTE addresses class imbalance, while feature normalization ensures fair treatment of features during training. The findings suggest that the proposed approach holds promise for early-stage DM prediction, enabling timely interventions and preventive measures.
Classification of cervical spine fractures using 8 variants EfficientNet with transfer learning
Authors: Bayangkari Karno Adhitio Satyo , Hastomo Widi , Surawan Tri , Lamandasa Serlia Raflesia , Usuli Sudarto , Kapuy Holmes Rolandy , Digdoyo Aji
Published: September 2023

A part of the nerves that govern the human body are found in the spinal cord, and a fracture of the upper cervical spine (segment C1) can cause major injury, paralysis, and even death. The early detection of a cervical spine fracture in segment C1 is critical to the patient’s life. Imaging the spine using contemporary medical equipment, on the other hand, is time-consuming, costly, private, and often not available in mainstream medicine. To improve diagnosis speed, efficiency, and accuracy, a computer-assisted diagnostics system is necessary. A deep neural network (DNN) model was employed in this study to recognize and categorize pictures of cervical spine fractures in segment C1. We used EfficientNet from version B0 to B7 to detect the location of the fracture and assess whether a fracture in the C1 region of the cervical spine exists. The patient data group with over 350 picture slices developed the most accurate model utilizing the EfficientNet architecture version B6, according to the findings of this experiment. Validation accuracy is 99.4%, whereas training accuracy is 98.25%. In the testing method using test data, the accuracy value is 99.25%, the precision value is 94.3%, the recall value is 98%, and the F1-score value is 96%.
IoT-based smart irrigation management system using real-time data
Authors: Hafian Asmae , Benbrahim Mohammed , Kabbaj Mohammed Nabil
Published: September 2023

An adequate water supply is essential for the growth and development of crops. When rainfall is insufficient, irrigation is necessary to meet crop water needs. It is a crucial and strategic aspect of economic and social development. To combat climate change, there is a need to adopt irrigation management techniques that increase and stabilize agricultural production while saving water, using intelligent agricultural water technologies. Internet of things (IoT) based technologies can achieve optimal use of water resources. This article introduces a smart realtime irrigation management system based on the internet of things. It provides optimal management of irrigation decisions using real-time weather and soil moisture data, as well as data from precipitation forecasts. The proposed algorithm is developed in real-time based on the IoT, enabling us to guide irrigation and control the amount of water in agricultural applications. The system uses real-time data analysis of climate, soil, and crop data to provide flexible planning of the irrigation system’s use. A case study from the Fez-Meknes region in Morocco is presented to demonstrate the proposed system’s effectiveness
A module placement scheme for fog-based smart farming applications
Authors: Mohamed Baghrous , Abdellatif Ezzouhairi , Mouhajir Manal , Zerifi Manare , Rabah Yahya , Youssef Errafik
Published: September 2023

As in Industry 4.0 era, the impact of the internet of things (IoT) on the advancement of the agricultural sector is constantly increasing. IoT enables automation, precision, and efficiency in traditional farming methods, opening up new possibilities for agricultural advancement. Furthermore, many IoT-based smart farming systems are designed based on fog and edge architecture. Fog computing provides computing, storage, and networking services to latency-sensitive applications (such as Agribots-agricultural robots-drones, and IoT-based healthcare monitoring systems), instead of sending data to the cloud. However, due to the limited computing and storage resources of fog nodes used in smart farming, designing a modules placement scheme for resources management is a major challenge for fog based smart farming applications. In this paper, our proposed module placement algorithm aims to achieve efficient resource utilization of fog nodes and reduce application delay and network usage in Fog-based smart farming applications. To evaluate the efficacy of our proposal, the simulation was done using iFogSim. Results show that the proposed approach is able to achieve significant reductions in latency and network usage.
Design and build an underwater fishing light based on internet of thing
Authors: Nugraha I Made Aditya , Ngurah Desnanjaya I Gusti Made
Published: September 2023

The utilization of fishing gear has been growing until now. One of these tools is an underwater fishing light that has been used by fishermen. However, until now this device still works conventionally and only provides one color. Based on these problems and the rapid development of the internet of things (IoT), an underwater fishing light was created and designed that can work with the IoT system and provide various colors of light according to the needs of fishermen. The design of this tool uses several tools and materials, such as generators, power adapters, controller modules, generators, and the Magic Home Pro application. This tool operates at 12 V DC and consumes 144 W of electric power. Utilization of this tool can also indirectly reduce the consumption of electrical and fuel energy on board and support the blue economy. The results of testing the device show that the IoT system can work well, provide light colors according to orders, is waterproof, and provides a fairly good light intensity with an average of 6.89 Lux (0-8 Lux).
Visualization of hyperspectral images on parallel and distributed platform: Apache Spark
Authors: Zbakh Abdelali , Bennani Mohamed Taj , Souri Adnan , Hichami Outman El
Published: September 2023

The field of hyperspectral image storage and processing has undergone a remarkable evolution in recent years. The visualization of these images represents a challenge as the number of bands exceeds three bands, since direct visualization using the trivial system red, green and blue (RGB) or hue, saturation and lightness (HSL) is not feasible. One potential solution to resolve this problem is the reduction of the dimensionality of the image to three dimensions and thereafter assigning each dimension to a color. Conventional tools and algorithms have become incapable of producing results within a reasonable time. In this paper, we present a new distributed method of visualization of hyperspectral image based on the principal component analysis (PCA) and implemented in a distributed parallel environment (Apache Spark). The visualization of the big hyperspectral images with the proposed method is made in a smaller time and with the same performance as the classical method of visualization.
Deep learning-based Ipoh driving cycle prediction
Authors: Ibrahim Nurru Anida , Subramaniam Arunkumar , Jabar Siti Norbakyah , Rahman Salisa Abdul
Published: September 2023

The driving cycle is a series of driving behaviours, such as acceleration, braking, and cruising, that occur over a set length of time. Predicting the driving cycle can help to improve vehicle performance or anticipate the range of an electric car. Based on prior data, long short-term memory (LSTM) networks can be used to forecast a vehicle's driving cycle. This paper studies a driving cycle prediction based on LSTM by recurrent neural network (RNN) using developed driving cycle data. The objectives of this paper are; to develop an Ipoh driving cycle (IDC), to develop a prediction of future IDC and to analyze the prediction of IDC. Firstly, the driving data is collected in three different routes in Ipoh city at back-from-work times. Then the data is divided into micro-trips and the driving features are extracted. The features are used to develop a driving cycle using k-means clustering approach. The prediction is developed after the training of neural networks by using LSTM network approach with root mean square error (RSME) of 6.2252%.
Quality of service management in telecommunication network using machine learning technique
Authors: Ayan Zhunussov , Alimjan Baikenov , Olga Manankova , Timur Zheltayev , Toktalyk Ziyekenov
Published: September 2023

Designing and implementing a fail-safe, real-time telecommunications network is complex. In modern networks, traditional quality of service (QoS) methods for monitoring and analyzing data have some problems, such as accuracy and efficient processing of big data in real time. To solve this problem, should use an appropriate intelligent crash classification system to detect and diagnose run-time errors. The article proposes to use a comprehensive fault detection system that includes QoS and machine learning technologies using information about the state of a point-to-point protocol over ethernet (PPPoE) session on PPPoE active discovery termination (PADT) virtual local area network (VLAN) routes. This intelligent system is built using the machine learning method and is independent of the main real-time system. Demonstrated the operation of seven machine learning algorithms and presented the results of training and fault detection. Based on the received information about the state of the PPPoE session, the PADT type allows you to control the behavior of the real-time system.
Arabic cyberbullying detecting using ensemble deep learning technique
Authors: Ahmed Mohammed Shihab , Maher Saif Muhannad , Khudhur Mokhalad Eesee
Published: September 2023

There has been a huge growth in recent years interest in studies on abusive language and cyberbullying detection due to its effects on both individual victims and societies. Hate speech, bullying, racism, aggressive content, harassment and other forms of abuse have all significantly increased as a result of Facebook, Instagram, and other social media platforms (SMPs). Since there is a significant need to detect, control, and prohibit the circulation of offensive content on social networking sites, we undertook this study to automate the identification of abusive language or cyberbullying. Arabic data set is balanced and will be used in the offensive detection process. Recently, ensemble machine learning has been used to increase the effectiveness of categorization models. Arabic detection is more precise given that each spatial feature text can make references to every other contextual piece of information. The authors utilized a model that merged convolutional neural network (CNN) with bidirectional long short-term memory (Bi-LSTM) and inverse document frequency gated recurrent unit (GRU) in a hybrid fashion without any post-processing. Our work outperformed every other publicly released cutting-edge ensemble model in the specifications of the official deep learning challenge. The findings indicate that the three-layer inverse document frequency long short-term memory (LSTM) classifier surpassed other classifiers in accuracy with a score of 92.75% compared to different algorithms.
Emergency vehicles classification for traffic signal system using optimized transfer DenseNet201 model
Authors: Manguri Kamaran H. , Mohammed Aree Ali
Published: September 2023

As a result of the rapid growth of the world population, traffic signaling systems for monitoring and controlling the roads have turned to be an important issue facing humanity. To effectively overcome this problem, an accurate method for congestion reduction on the roads should be used which has a direct relation between the population and the cars’ usage. Various approaches derived from deep and transfer learning have been investigated in this context. This research implemented an optimized transfer learning approach for densely connected convolutional neural network (DenseNet201) models for multiple classifications (non-emergency cars, ambulance, police, and firefighter). Due to the non-availability of public datasets, a customized dataset has been created. This paper aims to improve the performance accuracy of vehicle classification using certain preprocessing algorithms on the input images and testing various optimization methods. The performance accuracy of the proposed model is evaluated using k-folds cross-validation 20:80 for the test and training, respectively. The metrics which are used for comparison with other deep learning techniques are based on exactness, recall, accuracy and F1-score. Test outcomes specify that the proposed transfer model-based optimization outperforms alternative deep learning algorithms regarding vehicle accuracy in classifying and reaches 98.6%.
Predictive analytics of heart disease presence with feature importance based on machine learning algorithms
Authors: Kolukula Nitalaksheswara Rao , Pothineni Prathap Nayudu , Chinta Venkata Murali Krishna , Boppana Venu Gopal , Kalapala Rajendra Prasad , Duvvi Soujanya
Published: September 2023

Heart failure disease is a complex clinical issue which has more impact on life of human begins. Hospitals and cardiac centers frequently employ electrocardiogram (ECG) tool to assess and to identify heart failure at early stages. Healthcare professionals are very concerned about the early identification of heart disease. In this research paper we have focused on predictive analysis of cardiac disease by using machine learning algorithms. We have developed python-based software for healthcare research in this paper. This research has more significant work for tracking and establishing numerous health monitoring apps. We have demonstrated information handling that requires adjusting clear-cut portions and working with absolute factors. A quick overview of the various machine learning technologies based on heart disease diagnosis is described clearly in this research. A more reliable way for diagnosing cardiac problems is the random forest classification algorithm. This application needs data analysis, which is crucial owing to its about 95% accuracy rate across training data. We have discussed the tests and findings of the random forest classifier method, which improves the accuracy of heart disease research diagnosis.
An improved YOLOv5 for real-time human detection in infrared images
Authors: Khalfaoui Aicha , Badri Abdelmajid , El Mourabit Ilham
Published: September 2023

The recent prominence of using infrared technology for human detection has garnered attention, particularly for applications in intelligent video surveillance and self-driving systems. This technology offers advantages in adverse weather conditions and night vision. However, within deep learning, the challenge of variable illumination during human detection persists. This study presents a novel method for identifying individuals in thermal images by enhancing the you only look once (YOLOv5s) algorithm. The approach incorporates the Bi-directional feature pyramid network (BiFPN) and the convolutional block attention module (CBAM) to improve the model’s feature integration and extraction capabilities. Evaluation on established thermal imaging datasets confirms the method’s superiority over state-of-the-art convolutional neural networks (CNN) based techniques, achieving remarkable precision 99.1% and recall 96.9%.
Random forest for lung cancer analysis using Apache Mahout and Hadoop based on software defined networking
Authors: Khaleel Ali Abbood , Al-Azzawi Ahmed Adnan Mohammed , Alkhazraji Aws Mohammed
Published: September 2023

Random forest is a machine learning algorithm that mainly built as a classification method to make predictions based on decision trees. Many machine learning approaches used random forest to perform deep analysis on different cancer diseases to understand their complex characterstics and behaviour. However, due to massive and complex data generated from such diseases, it has become difficult to run random forest using single machine. Therefore, advanced tools are highly required to run random forest to analyse such massive data. In this paper, random forest algorithm using Apache Mahout and Hadoop based software defined networking (SDN) are used to conduct the prediction and analysis on large lung cancer datasets. Several experiments are conducted to evaluate the proposed system. Experiments are conducted using nine virtual nodes. Experiments show that the implementation of random forest algorithm using the proposed work outperforms its implementation in traditional environment with regard to the execution time. Comparison between the proposed system using Hadoop based SDN and Hadoop only is performed. Results show that random forest using Hadoop based SDN has less execution time than when using Hadoop only. Furthermore, experiments reveal that the performance of implemented system achieved more efficiency regarding execution time, accuracy and reliability.
A decision tree approach based on BOCR for minimizing criteria in requirements prioritization
Authors: Amelia Tan , Mohamed Rozlina
Published: September 2023

The requirements selection in the development of a software mostly requires a set of criteria. Determining the criteria used is often confusing because of the many criteria that must match with the characteristics of the project. This study introduces how to classify criteria based on benefits, opportunities, costs, risks (BOCR) to make the requirements prioritization process scalable. Project context characteristics and stakeholder perspectives are essential points discussed in this study because they are crucial in the requirements prioritization process. The criteria obtained from the literature review were followed by a survey to determine the importance of the criteria and their grouping in the BOCR using the decision tree method. There are 38 criteria and are grouped into four categories. There are two very significant criteria with a high level of importance, namely business value and stakeholder satisfaction. A decision tree based on BOCR can be used to classify the criteria for requirements prioritization. This research contributes to assisting software developers in finding and determining the criteria operated during the prioritization of requirements. Additionally, it is important to consider the project context and the collaboration the client and developer when prioritizing requirements.
An early warning system of heart failure mortality with combined machine learning methods
Authors: Sutradhar Ananda , Al Rafi Mustahsin , Alam Mohammad Jahangir , Islam Saiful
Published: September 2023

Heart failure (HF) is currently the leading cause of morbidity and mortality worldwide. Identifying the risk of mortality at the early stages is crucial to reducing the mortality rate. However, the traditional methods for exploring the signs of mortality are difficult and time-consuming. Whereas, machine learning (ML) methods are superior in reducing HF’s mortality rate by providing early warnings. This study presents a novel ML classifier called imperial boost-stacked (IBS) that can serve as an effective early warning system for predicting HF mortality. Initially, we performed an efficient data balancing technique named synthetic minority oversampling technique with edited nearest neighbors (SMOTE-ENN) to mitigate the imbalance problem. Next, two well-known feature selection techniques, the extra tree (ET) and information gain (IG), are applied to reduce the data dimensions and select the most significant features. Following that, the prepared feature sets are trained with our proposed IBS classifier. Simultaneously leveraging the advantages of Boosting, Stacking, and multiple robust methods, it significantly correlates with the intricate patterns of clinical data of HF patients. Finally, the robust outcomes of 92.75% accuracy over existing studies reveal that our proposed study can effectively warn the HF mortality at early stages and reduce the burden on the healthcare sector.
Prediction of the epileptic seizure through deep learning techniques using electroencephalography
Authors: Sunkara Mounika , Rajakumari Reeja Sundaran
Published: September 2023

Electroencephalography (EEG) is a widely used and significant technique for aiding in epilepsy diagnosis and investigating the electrical patterns of the human brain. Due to the non-stationary nature of EEG signals, seizure patterns will vary across different recording sessions for individual patients. In this study, a new deep learning long short-term memory (LSTM) model is implemented for the detection of brain tumors and seizures. The process consists of four key steps: EEG signal pre-processing, preictal feature extraction, hyper optimization using grey wolf optimization (GWO), and LSTM-based classification. The evaluation makes use of long-term EEG recordings from the EEG and ABIDE fMRI datasets. By experimenting with various modules and layers of memory units, a pre-analysis is first conducted to determine the best LSTM network architecture. The LSTM model makes use of numerous retrieved features, including temporal and frequency domain information between EEG channels that were extracted before classification. The discovery of the implemented methodology revealed significant advantages in accurately predicting seizures while minimizing false alarms. The implemented LSTM method achieves a 99% accuracy rate, 98% precision, 99% recall, and 98% f1-measure which is better when compared with cross sub-pattern correlation-based principal component analysis (SUBXPCA) and gradient-boosting decision tree (GBDT) methods.
Driver fatigue detection using Raspberry-Pi
Authors: Abdullah Mohd Fikri Azli , Hanafiah Mohamad Hizzudin Mohamad , Yogarayan Sumendra , Razak Siti Fatimah Abdul , Azman Afizan , Sayeed Md Shohel
Published: September 2023

The subject of fatigue monitoring is becoming more important in transportation and traffic management (including, for instance, the development of systems to detect and prevent driver drowsiness). People who work in offices are also susceptible to exhaustion, but there is currently no widely deployed system that is able to monitor this condition. In most cases, the driver’s eyelids will become heavy due to exhaustion after lengthy hours of driving or in the absence of mental concentration. Typically, when the driver’s concentration begins to fade, audio alert would be provided to force the drivers awake. In recent times, drowsiness is risky since it can result in an accident. Thus, a solution has been proposed to identify driver drowsiness by comparing several algorithms to find improved accuracy and execution time. Besides, this system will alert the driver with an audible warning in the event of drowsiness is detected.
Clustering performance using k-modes with modified entropy measure for breast cancer
Authors: Mahfuz Nurshazwani Muhamad , Suhartanto Heru , Kusmardi Kusmardi , Yusoff Marina
Published: September 2023

Breast cancer is a serious disease that requires data analysis for diagnosis and treatment. Clustering is a data mining technique that is often used in breast cancer research to assess the level of malignancy at an early stage. However, clustering categorical data can be challenging because different levels in categorical variables can impact the clustering process. This research proposes a modified entropy measure (MEM) to enhance clustering performance. MEM aims to address the issue of distance-based measures in clustering categorical data. It is also a useful tool for assessing data loss in categorical clustering, which helps to understand the patterns and relationships by quantifying the information lost during clustering. An evaluation compares k-modes+MEM, k-means+MEM, DBSCAN+MEM, and affinity+MEM with conventional clustering algorithms. The assessment metrics of clustering accuracy, intra-cluster distance and fowlkes-mallow index (FMI) are employed to evaluate the algorithm performance. Experimental results show significant improvements. k-Modes+MEM algorithm achieves a reduction in average intra-cluster distance and outperforms other algorithms in accuracy, intra-cluster distance, and FMI. The proposed algorithm can be extended to heterogeneous datasets in various domains such as healthcare, finance, and marketing.
A novel communication’s model between different blockchain using core-shell structure
Authors: Hajji Mohammed Amine , Kharmoum Nassim , Labrim Hicham , Ziti Soumia
Published: September 2023

lockchain technology has gained significant attention over the years for its potential to revolutionize various industries, including finance, supply chain management, agriculture, and healthcare. The key feature that makes blockchain technology secure and transparent is its decentralized nature, which relies on a network of nodes to validate transactions. In this research article, we propose a novel approach to model blockchain using the core-shell model. The proposed model provides a better understanding of the underlying mechanism of blockchain technology and helps in identifying the critical factors that affect the blockchain’s performance. To treat our contribution, we answered in this manuscript the following research questions: How can blockchain networks be designed and modeled in a more standardized and modular way? What is the core-shell model, and how can it be applied to blockchain modeling? How can the use of the core-shell model impact the efficiency and effectiveness of blockchain networks? What are the potential benefits and limitations of using the core-shell model for blockchain modeling?
A survey on deepfake video detection datasets
Authors: Sohan Md Fahimuzzman , Solaiman Md , Hasan Md Aumit
Published: September 2023

Deepfake video has usefulness in entertainment and multimedia technology, however, the danger of deepfake is significant to the social, economical, and political sectors so far. Specifically, to diverge any public opinion by generating fake news and spreading misleading information, national security may be under risk due to misrepresenting statements given by political leaders. The creation of such manipulated videos are getting easier day by day and at the same time it is necessary to detect and prevent them. In order to do that, researchers are creating challenging fake video databases for artificial intelligence (AI) based detection models to contribute to the research. This paper reviewed the existing deepfake video detection datasets available online and used in the previous research articles. We analyzed the literature from two different perspectives, datasets and detection models. The goal of this study is to introduce all publicly available datasets in this field including the discussion of techniques used to generate the data. In addition to our contribution, we showed a result comparison among different deepfake datasets and discussed the findings.
Enhancing safety communication in autonomous vehicles with hybrid elliptic curve digital signatures
Authors: Kalkundri Ravi , Khanai Rajashri , Kalkundri Praveen
Published: September 2023

The autonomous vehicles (AVs) are a part of vehicular ad hoc network (VANET) technology which is gaining a lot of researchers' attention for making the vehicle smarter and safer. In VANETs, the vehicles transmit various types of messages, some are important in terms of human lives while others are for infotainment. These messages give various information to the driver so that the driver can take appropriate precautions on the roads. In this work, the main aim was to concentrate on the safety messages that are very important in VANET infrastructure. In VANET the information transmitted are open, hence it is very easy for any attacker to manipulate or change the critical messages. Hence, in this paper, we intend to implement security to these safety messages by encrypting the elliptic curve digital signature algorithm (ECDSA) algorithm which is further optimized by ate pairing. The results have been compared with the traditional ECDSA in terms of throughput. By using the hybrid ECDSA, we increase the strength of ECDSA and still maintain the integrity of ECDSA, making sure that the authenticity of the vehicles and privacy of the messages is maintained within the VANET infrastructure.
The effects of artificial intelligence on the Kenyan society
Authors: Ndungi Rebeccah , Siregar Maria Ulfah
Published: September 2023

Artificial intelligence technology (AI) is an array of computer technologies that provide machines with human-like abilities in perception, action, and cognition. It can mimic human intellect in a broad variety of situations thanks to its advanced processing of information and mega-thinking procedures and powers. AI's power to mimic the intellect of humans means it will disrupt or significantly affect practically every major industry, including but not limited to manufacturing, healthcare, agriculture, and logistics. AI helps several industries move forward. Developed Western nations and leading digital titans like Google and Facebook have invested significantly in AI, utilizing its human skills in various vital societal sectors. Regrettably, most poor countries, particularly those in the sub-Saharan African region, do not have the necessary administration, learning, data reserves, and legislation to support the adoption and implementation of AI. Nevertheless, certain countries in sub-Saharan Africa, such as Kenya, have incorporated and used AI technologies in several key areas for the benefit of their inhabitants. This article explores how AI has affected Kenya and demonstrates how other sub-Saharan African countries might use AI-related technology to their full potential.