International Journal of Informatics and Communication Technology (IJ-ICT)
The International Journal of Informatics and Communication Technology (IJ-ICT), p-ISSN 2252-8776, e-ISSN 2722-2616, is a common platform for publishing quality research papers as well as other intellectual outputs. This journal is aimed at promoting the dissemination of scientific knowledge and technology in the information and communication technology areas in front of an international audience of the scientific community, to encourage the progress and innovation of technology for human life, and also to be the best platform for the proliferation of ideas and thoughts for all scientists, regardless of their locations or nationalities. The IJ-ICT covers all areas of informatics and communication technology (ICT) and focuses on integrating hardware and software solutions for storage, retrieval, sharing, manipulation management, analysis, visualization, interpretation, and their applications for human services programs and practices. It publishes refereed original research articles and technical notes. It is designed to serve researchers, developers, managers, strategic planners, graduate students, and others interested in state-of-the-art research activities in ICT. This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU). Now, this journal is accredited by the Indonesian Ministry of Education, Culture, Research, and Technology. The journal was accepted for Scopus indexing on November 9, 2023, and it is now available on the Scopus source page, with coverage starting in 2023.
Artificial intelligence based prediction on lung cancer risk factors using deep learning
In this proposed work, we identified the significant research issues on lung cancer risk factors. Capturing and defining symptoms at an early stage is one of the most difficult phases for patients. Based on the history of patients records, we reviewed a number of current research studies on lung cancer and its various stages. We identified that lung cancer is one of the significant research issues in predicting the early stages of cancer disease. This research aimed to develop a model that can detect lung cancer with a remarkably high level of accuracy using the deep learning approach (convolution neural network). This method considers and resolves significant gaps in previous studies. We compare the accuracy levels and loss values of our model with VGG16, InceptionV3, and Resnet50. We found that our model achieved an accuracy of 94% and a minimum loss of 0.1%. Hence physicians can use our convolution neural network models for predicting lung cancer risk factors in the real world. Moreover, this investigation reveals that squamous cell carcinoma, normal, adenocarcinoma, and large cell carcinoma are the most significant risk factors. In addition, the remaining attributes are also crucial for achieving the best performance.
Copyright (c) 2023 Muhammad Sohaib, Mary Adewunmi https://creativecommons.org/licenses/by-sa/4.0
E-government implementation's impact on Saudi vision 2030 to become an international logistic center
This study examined the influence of electronic government (e-government) implementation for the Ministry of Transport on fulfilling Saudi vision 2030 by transforming the Kingdom of Saudi Arabia (KSA) into a logistics center linking three continents. Saudi vision 2030 aims to cut transportation costs by improving infrastructure, shorten importing and exporting times by streamlining and automating operations, and increase supply chain transparency through sector reform. Implementing e-government would improve government services and engagement through information and communication technology (ICT). This article focuses on four primary areas: i) making KSA a logistics center; ii) increasing the chance of living throughout the Kingdom; and iii) promoting long-term financial sustainability. The study is founded on the idea that logistics is a crucial component for competitive advantage and transportation (by land, sea, or air) is a logistical sub-process for Saudi enterprises that benefit from transport networks similar to the best in the world. The Kingdom's strategic location at the junction of three continents gives its transport sector a geographical competitive advantage that provides access to important emerging markets and critical sea lanes. Despite the optimistic future of the transport and logistics industries in KSA, some important hurdles must be overcome.
Copyright (c) 2023 Razan Alhujaili, Malak Albaqami, Omar Aboulola, Mashael Khayyat https://creativecommons.org/licenses/by-sa/4.0
Smart door access control system based on QR code
Wirelessly based security applications have exploded as a result of modern technology. To build and/or implement security access control systems, many types of wireless communication technologies have been deployed. quick response (QR code) is a contactless technology that is extensively utilised in a variety of sectors, including access control, library book tracking, supply chains, and tollgate systems, among others. This paper combines QR code technology with Arduino and Python to construct an automated QR code-based access management system. After detecting a QR code, the QR scanner at the entry collects and compares the user's unique identifier (UID) with the UID recorded in the system. The results show that this system is capable of granting or denying access to a protected environment in a timely, effective, and reliable way. Security systems can protect physical and intellectual property by preventing unauthorized persons from entering the area. Many door locks, such as mechanical and electrical locks, were created to meet basic security needs but it also helps to create a data files structure of the authorized persons.
Copyright (c) 2023 Agrim Jain, Abhinav Panwar, Mohd. Azam, Ruqaiya Khanam https://creativecommons.org/licenses/by-sa/4.0
Chronic kidney disease prediction model using machine learning approach
Chronic disease (CD) such as kidney disease and causes severe challenging issues to the people all around the world. Chronic kidney disease (CKD) and diabetes mellitus (DM) are considered in this paper. Predicting the diseases in earlier stage, gives better preventive measures to the people. Healthcare domain leads to tremendous cost savings and improved health status of the society. The main objective of this paper is to develop an algorithm to predict CKD occurrence using machine learning (ML) technique. The commonly used classification algorithms namely logistic regression (LR), random forest (RF), conditional random forest (CRF), and recurrent neural networks (RNN) are considered to predict the disease at an earlier stage. The proposed algorithm in this paper uses medical code data to predict disease at an earlier stage.
Copyright (c) 2023 Munusamy Chitra, Abdul Kuthus Parveen, Murugadoss Elavarasi, Jayamoorthy Sangeetha, Ramalingam Vaittilingame https://creativecommons.org/licenses/by-sa/4.0
A cluster and association analysis visualization using Moodle activity log data
The course activity log is where a learning management system (LMS) like Moodle keeps track of the various learning activities. In order to conduct a quicker and more in-depth examination of the students' behaviors, the instructor may either directly examine the log or make use of more complex methodologies such as data mining. The majority of the proposed methods for analyzing this log data center mostly on predictive analysis. In this research, cluster analysis and association analysis, two separate data mining functions, are investigated in order to analyze the log. The students' activities are used in the cluster analysis performed with K-Means++, and the association analysis performed with Apriori is used to investigate the connections between the students' various activities. A dashboard presentation of the findings is provided in order to facilitate clearer comprehension. Based on the findings of the analysis, it can be concluded that the structure of the student cluster is medium, whereas the association between the activities undertaken by students is positively correlated and well-balanced. The subjective review of the dashboard reveals that the visualization is already sufficient, but there are some recommendations for making it even better.
Copyright (c) 2023 Andri Reimondo Tamba, Krista Lumbantoruan, Aulia Pakpahan, Samuel Situmeang https://creativecommons.org/licenses/by-sa/4.0
Novel DV-hop algorithm-based machines learning technics for node localization in rang-free wireless sensor networks
Localization is a critical concern in many wireless sensor network (WSN) applications. Furthermore, correct information regarding the geographic placements of nodes (sensors) is critical for making the collected data valuable and relevant. Because of their benefits, such as simplicity and acceptable accuracy, the based connectivity algorithms attempt to localize multi-hop WSN. However, due to environmental factors, the precision of localisation may be rather low. This publication describes an Extreme Learning Machine (ELM) technique for minimizing localization error in range-free WSN. In this paper, we propose a Cascade Extreme Learning Machine (Cascade-ELM) to increase localization accuracy in Range-Free WSNs. We tested the proposed approaches in a variety of multi-hop WSN scenarios. Our research focused on an isotropic and irregular environment. The simulation results show that the proposed Cascade-ELM algorithm considerably improves localization accuracy when compared to previous algorithms derived from smart computing approaches. When compared to previous work, isotropic environments show improved localization results.
Copyright (c) 2023 Oumaima Liouane, Smain Femmam, Toufik Bakir, Abdessalem Ben Abdelali https://creativecommons.org/licenses/by-sa/4.0
Machine learning techniques for plant disease detection: an evaluation with a customized dataset
Diseases in edible and industrial plants remains a major concern, affecting producers and consumers. The problem is further exacerbated as there are different species of plants with a wide variety of diseases that reduce the effectiveness of certain pesticides while increasing our risk of illness. A timely, accurate and automated detection of diseases can be beneficial. Our work focuses on evaluating deep learning (DL) approaches using transfer learning to automatically detect diseases in plants. To enhance the capabilities of our approach, we compiled a novel image dataset containing 87,570 records encompassing 32 different plants and 74 types of diseases. The dataset consists of leaf images from both laboratory setups and cultivation fields, making it more representative. To the best of our knowledge, no such datasets have been used for DL models. Four pre[1]trained computer vision models, namely VGG-16, VGG-19, ResNet-50, and ResNet-101 were evaluated on our dataset. Our experiments demonstrate that both VGG-16 and VGG-19 models proved more efficient, yielding an accuracy of approximately 86% and a f1-score of 87%, as compared to ResNet-50 and ResNet-101. ResNet-50 attains an accuracy and a f1-score of 46.9% and 45.6%, respectively, while ResNet-101 reaches an accuracy of 40.7% and a f1-score of 26.9%.
Copyright (c) 2023 Amatullah Fatwimah Humairaa Mahomodally, Geerish Suddul, Sandhya Armoogum https://creativecommons.org/licenses/by-sa/4.0
Analyzing performance of deep learning models under the presence of distortions in identifying plant leaf disease
Convolutional neural networks (CNN) trained using deep learning (DL) have advanced dramatically in recent years. Researchers from a variety of fields have been motivated by the success of CNNs in computer vision to develop better CNN models for use in other visually-rich settings. Successes in image classification and research have been achieved in a wide variety of domains throughout the past year. Among the many popularized image classification techniques, the detection of plant leaf diseases has received extensive research. As a result of the nature of the procedure, image quality is often degraded and distortions are introduced during the capturing of the image. In this study, we look into how various CNN models are affected by distortions. Corn-maze leaf photos from the 4,188-image corn or maize leaf Dataset (split into four categories) are under consideration. To evaluate how well they handle noise and blur, researchers have deployed pre-trained deep CNN models like visual geometry group (VGG), InceptionV3, ResNet50, and EfficientNetB0. Classification accuracy and metrics like as recall and f1-score are used to evaluate CNN performance.
Copyright (c) 2023 Neha Sandotra, Palak Mahajan, Pawanesh Abrol, Parveen Kumar Lehana https://creativecommons.org/licenses/by-sa/4.0
Design of a 175 GHz SiGe-based voltage-controlled oscillator with greater than 7.6 dBm power
In this research, we present a low phase noise (PN) and wide tuning range 175 GHz inductors and capacitors (LC) voltage-controlled oscillator (VCO) based on a differential Colpitts oscillator that was designed using a 0.13 μm bipolar complementary metal oxide semiconductor (BiCMOS) and simulated. The square of the tank Q-factor and the square of the oscillation amplitude were both maximized to reduce PN. With an extensive examination of parasitic, mathematical analysis of load impedances, and implementation of differential design, the PN was reduced, and the output power was enhanced. Using a supply voltage of 1.6 V, the VCO consumes 41.9 mA, resulting in a total power usage of 67 mW to prevent undesirable PN deterioration, an inter-stage LC filter at the VCO-buffer interface increases the swing at the buffer input. To make a better output, a buffer is used to isolate the load from the VCO core. In addition, the VCO has a high linearity and the overall, the VCO presented in this study demonstrates excellent performance and has the potential to be used in a wide range of applications that require a high-performance, low-power VCO.
Copyright (c) 2023 Oluseun Damilola Oyeleke, Aliyu Danjuma Usman, Kabir Ahmad Abubilal, Habeeb Bello, Olabode Idowu-Bismark https://creativecommons.org/licenses/by-sa/4.0
Evaluating the level of inteference in UMTS/LTE heterogeneous network system
The study evaluated interference in a dense heterogeneous network using third-generation universal mobile telecommunication systems (UMTS) and fourth-generation long term evolution (LTE) networks LTE. The UMTs/LTE heterogeneous network determines the level of interference when the two communication systems coexist and how to improve the network by migrating from UMTs to LTE, which has a faster download speed and larger capacity. Techno lite 8 on third generation (3G) and Infinix Pro 6 on fourth generation (4G) were used to measure network the received signal strength (RSS) during site investigation. UE interference was detected and traced using a spectrum analyzer. UMTS and LTE path loss exponents are 2.6 and 3.2. Shannon's capacity theorem calculated LTE and UMTS capacity. When signal to interference and noise ratio (SINR) was used as a quality of service (QoS) indicator, MATLAB channel capacity plots did not match Shannon's due to neighboring interference. UMTS had an R2 of 0.54 and LTE 0.57 for the Shannon channel capacity equation. Adjacent channel interference (ACI) user devices reduce network capacity, lowering QoS for other customers.
Universal mobile telecommunication systems
Copyright (c) 2023 Nnebe Scholastica Ukamaka, Odeh Isaac Ochim, Okafor Chinenye Sunday, Ugbe Oluchi Christiana https://creativecommons.org/licenses/by-sa/4.0
Comparison of three common software-defined network controllers
The software-defined network (SDN) controller adds and removes the contents of the flow table through secure channels to determine how packets are processed and how the flow table is managed. The controller pays attention to network intelligence and becomes the middle part, where the network manages the transfer data of the aircraft delivered via the OpenFlow (OF) switch. To this end, the controller provides an interface for managing, controlling, and managing this switch flow table. Run tests to calculate controller throughput and latency levels and test using the cbance tool, which can test transmission control protocol (TCP) and user datagram protocol (UDP) protocols. The tests are run by forcing the controller to run at maximum without any additional settings (default settings) in order to use the correct information about the controller’s capabilities. Because of this need, you need to test the performance of your controller. In this study, the tests were run on three popular controllers. Test results show that flowed controllers are more stable than open network operating dystem (ONOS) and open daylight (ODL) controllers in managing switch and host loads.
Copyright (c) 2023 Rikie Kartadie, Edy Prayitno https://creativecommons.org/licenses/by-sa/4.0
During the last decade, the social media has been regarded as a rich dominant source of information and news. Its unsupervised nature leads to the emergence and spread of fake news. Fake news detection has gained a great importance posing many challenges to the research community. One of the main challenges is the detection accuracy which is highly affected by the chosen and extracted features and the used classification algorithm. In this paper, we propose a context-based solution that relies on account features and random forest classifier to detect fake news. It achieves the precision of 99.8%. The system accuracy has been compared to other commonly used classifiers such as decision tree classifier, Gaussian Naïve Bayes and neural network which give precision of 98.4%, 92.6%, and 62.7% respectively. The experiments’ accuracy results show the possibility of distinguishing fake news and giving credibility scores for social media news with a relatively high performance.
Copyright (c) 2023 Eslam Fayez, Amal Elsayed Aboutabl, Sarah N. Abdulkader https://creativecommons.org/licenses/by-sa/4.0
Smart parking for smart cities: a novel approach to reducing frivolous parking zone determination
Internet of things (IoT) infrastructures are rapidly expanding, which will lead to an unanticipated rise in demand for smart cities. The concept of a "smart city" has recently gained traction in urban planning circles. An IoT-based smart parking system is the focus of this article, and it allows a motorist to locate a car park and an available parking space in an indoor metropolis, all from the comfort of their own vehicle. Additional efforts are made to reduce the time spent defining parking zones. Reduced fuel use helps to cut down on pollution, as well as avoid needless travel through congested parking lots, which can help to reduce unlawful parking and alleviate traffic congestion in the city we all live in. These innovations include automobile particular identification via radio frequency identification (RFID) tags, unoccupied slot detection through the use of ultrasonic sensors, and cost calculating based largely on parking duration. The technology we've used is unique in that it runs on separate parts of the system for the hardware and the software.
Copyright (c) 2023 Atiqur Rahman, Ali Md Liton https://creativecommons.org/licenses/by-sa/4.0
Cloud and internet-of-things secure integration along with security concerns
Cloud computing is a new technology which refers to an infrastructure where both software and hardware application are operate for the network with the help of internet. Cloud computing provide these services with the help of rule know as you pay as you go on. Internet of things (IoT) is a new technology which is growing rapidly in the field of telecommunications. The aim of IoT devices is to connect all things around us to the internet and thus provide us with smarter cities, intelligent homes and generally more comfortable lives. The combation of cloud computing and IoT devices make rapid development of both technologies. In this paper, we present information about IoT and cloud computing with a focus on the security issues of both technologies. Concluding we present the contribution of cloud computing to the IoT technology. Thus, it shows how the cloud computing technology improves the function of the IoT. Finally present the security challenges of both technologies IoT and cloud computing.
Copyright (c) 2023 Arif Ullah, Imane Laassar, Canan Batur Şahin, Ozlem Batur Dinle, Hanane Aznaoui https://creativecommons.org/licenses/by-sa/4.0
Comparative performance study of HC-12, nRF24L01, and XBee for vehicular communication
In recent times, the volume of traffic congestion has been rapidly growing on roads. These days the necessity of having safe transportation and journey is essential. Thus, vehicle communication could be a possible solution to enhance safe transit. Vehicular communications provide a wide range of applications with different characteristics, namely vehicle and vehicle (V2V) communications. Every year, traffic accidents kill many people worldwide, and many people have been injured. V2V communication enables vehicles to communicate with each other to provide safety and convenience to drivers. Therefore, this paper explores a direction to develop a conceptual approach to V2V communication with HC-12, nRF24L01, and XBee. The study aims to analyze and evaluate the communication range that may contribute to the future road transportation system.
Copyright (c) 2023 Sumendra Yogarayan, Siti Fatimah Abdul Razak, Mohd. Fikri Azli Abdullah, Afizan Azman, Vethanayagi Arultas, Suneetha Sri Raman https://creativecommons.org/licenses/by-sa/4.0
Automated machine learning for analysis and prediction of vehicle crashes
This work discusses the study and development of a graphical interface and implementation of a machine learning model for vehicle traffic injury and fatality prediction for a specified date range and for a certain zip (US postal) code based on the New York City's (NYC) vehicle crash data set. While previous studies focused on accident causes, little insight has been offered into how such data may be utilized to forecast future incidents, Studies that have historically concentrated on certain road segment types, such as highways and other streets, and a specific geographic region, this study offers a citywide review of collisions. Using cutting-edge database and networking technology, a user-friendly interface was created to display vehicle crash series. Following this, a support vector machine learning model was built to evaluate the likelihood of an accident and the consequent injuries and deaths at the zip code level for all of NYC and to better mitigate such events. Using the visualization and prediction approach, the findings show that it is efficient and accurate. Aside from transportation experts and government policymakers, the machine learning approach deliver useful insights to the insurance business since it quantifies collision risk data collected at specific places.
Copyright (c) 2023 Abhishek Saxena, Stefan A. Robila https://creativecommons.org/licenses/by-sa/4.0
Single line noise cancellation using derivative of normalized least mean square algorithm
Suppression of noise in noisy speech signal is required in many speech enhancement applications like signal recording and transmission from one place to other. In this paper a novel single line noise cancellation system is proposed using derivative of normalized least mean spare algorithm. The proposed system has two phases. The first phase is generation of secondary reference signal from incoming primary signal itself at initial silence period and pause between two words, which is essential while adaptive filter using as noise canceller. Second phase is noise cancellation using proposed modified error data normalized step size (EDNSS) algorithm. The performance of the proposed algorithm is compared with normalized least mean square (NLMS) algorithm and original EDNSS algorithm using standard IEEE sentence (SP23) of Noizeus data base with different types of real-world noise at different level of signal to noise ratio (SNR). The output of proposed, NLMS and EDNSS algorithm are measured with output SNR, excessive mean square error (EMSE) and misadjustment (M). The results clearly illustrates that the proposed algorithm gives improved result over conventional NLMS and EDNSS algorithm. The speed of convergence is also maintained as same conventional NLMS algorithm.
Copyright (c) 2023 Rathnakara Srinivasa Pandit, Udayashankara Veerappa https://creativecommons.org/licenses/by-sa/4.0
Microstrip patch antenna review on various parameters, methods and its applications
The implementations of the microstrip patch antenna for wireless local area network (WLAN) and worldwide interoperability for microwave access (WiMAX) are analyzed in the literature in this research. Dual or multiband antenna has played a significant part in meeting the expectations of wireless service in this quickly developing world of wireless communication. Basically, a transitory guide, an antenna is a device that emits or absorbs radio waves. Numerous benefits exist for microstrip patch antennas, including affordability, portability, simplicity of construction, and compatibility with integrated circuits. This has several important uses in the military, radar, mobile communications, global positioning system (GPS), remote sensing, and more. In mobile devices like portable computers and smartphones, WLAN and WiMAX are often used.
Copyright (c) 2023 Kannadhasan Suriyan, Nagarajan Ramaingam https://creativecommons.org/licenses/by-sa/4.0
Development of a PC-based sign language translator
While a hearing-impaired individual depends on sign language and gestures, non-hearing-impaired person uses verbal language. Thus, there is need for means of arbitration to forestall situation when a non-hearing-impaired individual who does not understand the sign language wants to communicate with a hearing-impaired person. This paper is concerned with the development of a PC-based sign language translator to facilitate effective communication between hearing-impaired and non-hearing-impaired persons. Database of hand gestures in American sign language (ASL) is created using Python scripts. TensorFlow (TF) is used in the creation of a pipeline configuration model for machine learning of annotated images of gestures in the database with the real time gestures. The implementation is done in Python software environment and it runs on a PC equipped with a web camera to capture real time gestures for comparison and interpretations. The developed sign language translator is able to translate ASL/gestures to written texts along with corresponding audio renderings at an average duration of about one second. In addition, the translator is able to match real time gestures with the equivalent gesture images stored in the database even at 44% similarity.
Copyright (c) 2023 Kamoli Akinwale Amusa, Ayorinde Joseph Olanipekun, Tolulope Christiana Erinosho, Abiodun Akeem Salaam, Sodiq Segun Razaq https://creativecommons.org/licenses/by-sa/4.0
Architectural pattern for service collaboration
The aim of this paper is to propose a modeling framework, tailored to build efficient, elastic and autonomous applications from tasks and services. It includes integrated services to develop the software products, reusing on demand in-house services with specific requirements and flexible the representational state transfer (REST) services. The idea is to decouple authorization for reduced service dependency and to provide a possibility for developing the whole application by increasing the existing application flexibility. Based on the fact that there are different web application platforms that serve to offer services to users but they are not integrated; we propose a framework with high flexibility degree, especially integrating the most used services such: e-learning, administrative, and library services, as University services are concern.
Copyright (c) 2023 Agon Memeti, Betim Cico https://creativecommons.org/licenses/by-sa/4.0
A learning-based approach to breast cancer screening using mammography images
The current big challenge facing radiologists in healthcare is the automatic detection and classification of masses in breast mammogram images. In the last few years, many researchers have proposed various solutions to this problem. These solutions are effectively dependent and work on annotated breast image data. But these solutions fail when applied to unlabeled and non-annotated breast image data. Therefore, this paper provides the solution to this problem with the help of a neural network that considers any kind of unlabeled data for its procedure. In this solution, the algorithm automatically extracts tumors in images using a segmentation approach, and after that, the features of the tumor are extracted for further processing. This approach used a double thresholding-based segmentation technique to obtain a perfect location of the tumor region, which was not possible in existing techniques in the literature. The experimental results also show that the proposed algorithm provides better accuracy compared to the accuracy of existing algorithms in the literature.
Copyright (c) 2023 Khalid Shaikh, Sabitha Krishnan, Rohit Thanki https://creativecommons.org/licenses/by-sa/4.0
Quantum dronesand the future of military warfare
The advent of drones has significantly impacted military warfare, providing improved reconnaissance, surveillance, and target acquisition (RSTA), cost savings, increased convenience, safety, and flexibility. A layered network control architecture, known asthe internet of drones (IoD), coordinates drone access to controlled airspace and offers navigation services. Various systems, including wireless sensor networks (WSN) and dronesdirected to an expanded controlling zone, integrate with IoD to improve connection performance. This paper provides an overview of the IoDand the internet of quantum drones (IoQD), highlighting key issues and potential solutions in applications and deployment. The IoQD provides primary features such as secure message exchange, fast communication processes, the viability of creating and deploying private IoQD, and enabling a new field of application, quantum well (QW). In conclusion, the advent of drone technology has significantly improved various aspects of military operations, including reconnaissance, surveillance, and target acquisition. The IoQD offers apromising solution for military networks, facilitating the safe and expedited transfer of data, ultimately benefiting the entire military network.
Copyright (c) 2023 Tole Sutikno https://creativecommons.org/licenses/by-sa/4.0
Realization of an intelligent evaluation system
A number of benefits have been reported for computer-based assessments over traditional paper-based exams, both in terms of IT support for question development, reduced distribution and test administration costs, and automated support. Possible for the ranking. However, existing computerized assessment systems do not provide all kinds of questions, namely open questions that require writing solutions. To overcome the challenges of the existing, the objective of this work is to achieve an intelligent evaluation system (IES) responding to the problems identified, and which adapts to the different types of questions, especially open-ended questions of which the answer requires sentence writing or programming.
Copyright (c) 2023 Otman Maarouf, Rachid El Ayachi, Mohamed Biniz https://creativecommons.org/licenses/by-sa/4.0
Evaluating the impact of COVID-19 on the monetary crisis by machine learning
In this study, machine learning is examined in relation to commercial machine learning's resilience to the COVID-19 pandemic-related crisis. Two approaches are used to assess the pandemic's impact on machine learning risk, as well as a method to prioritize sectors according to the crisis's potential negative consequences. Iconducted the studyto determine Santander machine learning's resilience. The data mining area offers prospects for COVID-19's future. A total of 13 machine learning demos were selected for its organization. The Hellweg strategy and the technique for order preference by similarity to ideal solution (TOPSIS)technique were utilized as direct request strategies. Parametric assessment of machine learning versatility in business was based on capital sufficiency, liquidity proportion, market benefits, and share in an arrangement of openings with a perceived disability, and affectability of machine learning's credit portfolio to monetary hazard. As a result of the COVID-19 pandemic, these enterprises were ranked according to their threat. Based on the findings of the research, machine learning worked the best for the pandemic. Meanwhile, machine learning suffered the most during the downturn. It can be seen, for example, in conversations about the impact of the pandemic on developing business sector soundness and managing financial framework solidity risk.
Copyright (c) 2023 Milad Mohseni https://creativecommons.org/licenses/by-sa/4.0
Connected caregiving: investigating mothers in the era of digital access
Mothers have embraced and utilized digital access for nurturing and personal use to enhance their roles while balancing newfound demands. The Internet has provided mothers access to information on various topics, including pregnancy, childbirth, and infant care. Social media tools and platforms have also provided mothers with a space to connect with other mothers, share experiences, and seek support. This scoping review aims to identify the relationship of the focus skills among mothers in utilizing digital access. Four databases, including Scopus, web of science (WOS), education resources information centre (ERIC), and ScienceDirect, were used in this research, which found 36 articles for eligibility. Only 16 articles are eligible for analysis and reference after the exclusion and inclusion process for data collection. Based on the 16 publications examining mothers’ use of internet access, four essential skills have been identified. These included social, digital, cultural, and problem-solving skills and are acknowledged as being related to digital access mothering. The findings show these skills are offered to mothers through digital access, fostering diverse skill sets, contributing to their empowerment, and supporting sustainable development goal 5: gender equality, aiming to enhance women’s roles and ensure equal opportunities through digital inclusion.
Copyright (c) 2025 Anissa Saidi, Wong Yee Von, Tirzah Zubeidah Zachariah@ Omar, Lim Seong Pek, Rita Wong Mee Mee, Khoo Kim Leng https://creativecommons.org/licenses/by-sa/4.0
Smart portable system for monitoring vibration based on the Raspberry Pi microcomputer and the MEMS accelerometer
In this work, an internet of things (IoT)sensing and monitoring box has been developed. The proposed low-cost system is a portable device for smart buildings to measure vibrations, monitor, and control noise caused by the industrial machines. We will present an instrument and a method to measure the vibration and tilt of a mechanical system (air conditioner). The primary goal is to create a signal acquisition and monitoring system that is both user-friendly and affordable, while also delivering exceptional precision. The key concept is centered around acquiring and processing signals through the RaspberryPi. We will use for the first time as an application, which does not exist before, a conversion method to control and monitor remotely the noise generated by the machines. Once the noise reaches a high value or the air conditioner is too much tilted, the system sends an alert in the form of an email. We will use the Python language to acquire and process the signal and send the alerts. The proposed approach is straightforward to implement, and the obtained results demonstrate a high level of accuracy that is consistent with the existing literature.
Copyright (c) 2023 Hajar Baghdadi, Karim Rhofir, Mohamed Lamhamdi https://creativecommons.org/licenses/by-sa/4.0
Although there is a large interest about internet of things (IoT) architectures, still there is no consensus on their conceptualization in the extant literature. This lack of information in conceptualization is problematic because it hampers the deep understanding of the appeared proposals, as well as the adoption of a shared workflow by the involved architects of these systems. Thus, a concise and agreed-upon conceptualization of IoT architectures is called for. This paper aims at giving a contribution on the topic. We start by reviewing the available standards, then, in light of their suggestions, a workflow to be followed in the definition of the architecture descriptions (ADs) of IoT systems is detailed and, in addition, a sample case study, which implements that workflow, is proposed. The contributions are sufficiently abstract to be applicable also to the description of the architecture of artificial intelligence of things (AIoT) systems.
Copyright (c) 2025 Gaetanino Paolone, Romolo Paesani, Jacopo Camplone, Andrea Piazza, Paolino Di Felice https://creativecommons.org/licenses/by-sa/4.0
CNN inference acceleration on limited resources FPGA platforms_epilepsy detection case study
The use of a convolutional neural network (CNN) to analyze and classify electroencephalogram (EEG) signals has recently attracted the interest of researchers to identify epileptic seizures. This success has come with an enormous increase in the computational complexity and memory requirements of CNNs. For the sake of boosting the performance of CNN inference, several hardware accelerators have been proposed. The high performance and flexibility of the field programmable gate array (FPGA) make it an efficient accelerator for CNNs. Nevertheless, for resource-limited platforms, the deployment of CNN models poses significant challenges. For an ease of CNN implementation on such platforms, several tools and frameworks have been made available by the research community along with different optimization techniques. In this paper, we proposed an FPGA implementation for an automatic seizure detection approach using two CNN models, namely VGG-16 and ResNet-50. To reduce the model size and computation cost, we exploitedtwo optimization approaches: pruning and quantization. Furthermore, we presented the results and discussed the advantages and limitations of two implementation alternatives for the inference acceleration of quantized CNNs on Zynq-7000: an advanced RISC machine (ARM)software implementation-based ARM,NN,software development kit (SDK) and a software/hardware implementation-based deep learning processor unit (DPU) accelerator and DNNDK toolkit.
Copyright (c) 2023 Afef Saidi, Slim Ben Othman, Meriam Dhouibi, Slim Ben Saoud https://creativecommons.org/licenses/by-sa/4.0
A comprehensive survey of automatic dysarthric speech recognition
The need for automated speech recognition has expanded as a result of significant industrial expansion for a variety of automation and human-machine interface applications. The speech impairment brought on by communication disorders, neurogenic speech disorders, or psychological speech disorders limits the performance of different artificial intelligence-based systems. The dysarthric condition is a neurogenic speech disease that restricts the capacity of the human voice to articulate. This article presents a comprehensive survey of the recent advances in the automatic dysarthric speech recognition (DSR) using machine learning (ML) and deep learning(DL)paradigms. It focuses on the methodology, database, evaluation metrics,and major findings from the study of previous approaches. From the literature survey it provides the gaps between exiting work and previous work on DSR and provides the future direction for improvement of DSR. The performance of the various machine and DLschemes is evaluated for the DSR on UASpeech dataset based on accuracy, precision, recall,and F1-score. It is observed that the DLbased DSR schems outperforms the MLbased DSR schemes.
Copyright (c) 2023 Shailaja Yadav, DinkarManik Yadav, Kamalakar Ravindra Desai https://creativecommons.org/licenses/by-sa/4.0
Cost-effective IoT-based automated vehicle headlight control system: design and implementation
The current world would be difficult without vehicles, which offer vital advantages for social connectivity, mobility, and technical advancement. Though motor vehicles provide benefits to passenger transportation, they also present certain challenges in their use. A major issue is nighttime traffic accidents caused by headlamps from automobiles traveling in reverse directions, that's why there is a high probability of accidents due to the glare on the driver's eyes. The phrase "Troxler effect" refers to an unexpected glare that a motorist recognizes. In this paper, we will provide an optimal solution to this challenge/Troxler effect. The primary objective of this paper is to design an internet of things (IoT)-based smart headlight control model. Our system introduced a cost-effective vehicle’s headlights controlled by light detection. According to this paper, a vehicle’s headlights are automatically rotated down when the sensor detects lights from the opposite direction of the vehicle headlights. We tried to reduce the road accident rate with our proposed system. This type of technology will prove useful in the motor vehicle sector and offer an innovative approach that ensures driver safety as well as increasing economic development.
Vehicle lighting systems smart headlights
Copyright (c) 2025 Momotaz Begum, Nayeem Ullah, Mehedi Hasan Shuvo, Towhidul Islam, Thofazzol Hossen, Jia Uddin https://creativecommons.org/licenses/by-sa/4.0