bluuebunny/crossref_metadata_2025 huggingface dataset:
An effective feature selection using improved marine predators algorithm for Alzheimer’s disease classification
Authors: Topannavar Preeti Sadanand , Yadav Dinkar M.
Published: June 2023

Alzheimer’s disease (AD) is an irremediable neurodegenerative illness developed by the fast deterioration of brain cells. AD is mostly common in elder people and it extremely disturbs the physical and mental health of patients, therefore early detection is essential to prevent AD development. However, the precise detection of AD and mild cognitive impairment (MCI) is difficult during classification. In this paper, the Residual network i.e., ResNet-18 is used for extracting the features, and the proposed improved marine predators algorithm (IMPA) is developed for choosing the optimum features to perform an effective classification of AD. The multi-verse optimizer (MVO) used in the IMPA helps to balance exploration and exploitation, which leads to the selection of optimal relevant features. Further, the classification of AD is accomplished using the multiclass support vector machine (MSVM). Open access series of imaging studies-1 (OASIS-1) and Alzheimer disease neuroimaging initiative (ADNI) datasets are used to evaluate the IMPA-MSVM method. The performance of the IMPA-MSVM method is analyzed using accuracy, sensitivity, specificity, positive predictive value (PPV) and matthews correlation coefficient (MCC). The existing methods such as the deep learning-based segmenting method using SegNet (DLSS), mish activation function (MAF) with spatial transformer network (STN) and BrainNet2D are used to evaluate the IMPA-MSVM method. The accuracy of IMPA-MSVM for the ADNI dataset is 98.43% which is more when compared to the DLSS and MAF-STN.
Automatic optical inspection for detecting keycaps misplacement using Tesseract optical character recognition
Authors: Munawaroh Anisatul , Jamzuri Eko Rudiawan
Published: June 2023

This research study aims to develop automatic optical inspection (AOI) for detecting keycaps misplacement on the keyboard. The AOI hardware has been designed using an industrial camera with an additional mechanical jig and lighting system. Optical character recognition (OCR) using the Tesseract OCR engine is the proposed method to detect keycaps misplacement. In addition, captured images were cropped using a predefined region of interest (ROI) during the setup. Subsequently, the cropped ROIs were processed to acquire binary images. Furthermore, Tesseract processed these binary images to recognize the text on the keycaps. Keycaps misplacement could be identified by comparing the predicted text with the actual text on the golden sample. Experiments on 25 defects and 25 non-defected samples provided a classification accuracy of 97.34%, a precision of 100%, and a recall of 90.70%. Meanwhile, the character error rate (CER) obtained from the test on a total of 57 characters provided a performance of 10.53%. This outcome has implications for developing AOI for various keyboard products. In addition, the precision level of 100% signifies that the proposed method always offers correct results in detecting product defects. Such outcomes are critical in industrial applications to prevent defective products from circulating in the market.
iBeacon-based indoor positioning system: from theory to practical deployment
Authors: Dinh Thi Thai-Mai , Duong Ngoc-Son
Published: June 2023

Developing an indoor positioning system became essential when global positioning system signals could not work well in indoor environments. Mobile positioning can be accomplished via many radio frequency technology such as Bluetooth low energy (BLE), wireless fidelity (Wi-Fi), ultra-wideband (UWB), and so on. With the pressing need for indoor positioning systems, we, in this work, present a deployment scheme for smartphone using Bluetooth iBeacons. Three main parts, hardware deployment, software deployment, and positioning accuracy assessment, are discussed carefully to find the optimal solution for a complete indoor positioning system. Our application and experimental results show that proposed solution is feasible and indoor positioning system is completely attainable.
Trust based multi objective honey badger algorithm to secure routing in vehicular ad-hoc networks
Authors: Mutalik Pramod , C. Patil Venkangouda
Published: June 2023

A vehicular ad-hoc network (VANET) is a set of intelligent vehicles that interact without any fixed infrastructure. Data transmission between each transmitter/receiver pair is accomplished using routing protocols. However, communication over the VANET is vulnerable to malicious attacks, because of the unavailability of fixed infrastructure and wireless communication. In this paper, the trust based multi objective honey badger algorithm (TMOHBA) is proposed to achieve secure routing over the VANET. The TMOHBA is optimized by incorporating different cost functions, namely, trust, end to end delay (EED), routing overhead, energy, and distance. The developed secure route discovery using the TMOHBA is used to improve the robustness against the malicious attacks, for increasing the data delivery. Moreover, the shortest path discovery is used to minimize the delay while improving the security of VANET. The TMOHBA method is evaluated using the packet delivery ratio (PDR), throughput and EED. Existing researches such as hybrid enhanced glowworm swarm optimization (HEGSO) and ad-hoc on-demand distance vector based secure protocol (AODV-SP) are used to evaluate the TMOHBA method. The PDR of the TMOHBA method for 10 malicious attacks is 90.6446% which is higher when compared to the HEGSO and AODV-SP.
Fifth-generation small cell backhaul capacity enhancement and large-scale parameter effect
Authors: Idowu-Bismark Olabode , Idachaba Francis , Atayero Aderemi A.
Published: June 2023

The proliferation of handheld devices has continued to push the demand for higher data rates. Network providers will use small cells as an overlay to macrocell in fifth-generation (5G) for network capacity enhancement. The current cellular wireless backhauls suffer from the problem of insufficient backhaul capacity to cater to the new small cell deployment scenarios. Using the 3D digital map of Lagos Island in the Wireless InSite, small cells are deployed on a street canyon and in high-rise scenarios to simulate the backhaul links to the small cells at 28 GHz center frequency and 100 MHz bandwidth. Using a user-defined signal to interference plus noise ratio-throughput (SINR-throughput) table based on an adaptive modulation and coding scheme (MCS), the throughput values were generated based on the equation specified by 3GPP TS 38.306 V15.2.0 0, which estimates the peak data rate based on the modulation order and coding rate for each data stream calculated by the propagation model. Finding shows achieved channel capacity is comparable with gigabit passive optical networks (GPON) used in fiber to the ‘X’ (FTTX) for backhauling small cells. The effect of channel parameters such as root mean squared (RMS) delay spread and RMS angular spread on channel capacity are also investigated and explained.
Performance analysis of smart optimization antenna for wireless networks
Authors: Abraham Jacob , Suriyan Kannadhasan , Jackson Beulah , Natarajan Mahendran , Lakshmanaperumal Thanga Mariappan
Published: June 2023

Antenna design has significantly advanced as a result of the widespread need for wireless communications and data substitution through wireless devices. The research article's goal is to provide a conceptual framework, difficulties, and opportunities for a source as well as a general overview of the antenna used in wireless communications applications. In this proposed research, we will go over a variety of topics related to mobile communication and fifth generation (5G) technologies, including its pros and benefits. A thorough comparison between the expected properties of the antennas and each generation, from 1st generation (1G) to 5G, is also included. This article also provides an overview of the investigated 5G technologies and various antenna designs.
An ensemble model to detect packet length covert channels
Authors: Elsadig Muawia A. , Gafar Ahmed
Published: June 2023

Covert channel techniques have enriched the way to commit dangerous and unwatched attacks. They exploit ways that are not intended to convey information; therefore, traditional security measures cannot detect them. One class of covert channels that difficult to detect, mitigate, or eliminate is packet length covert channels. This class of covert channels takes advantage of packet length variations to convey covert information. Numerous research articles reflect the useful use of machine learning (ML) classification approaches to discover covert channels. Therefore, this study presented an efficient ensemble classification model to detect such types of attacks. The ensemble model consists of five machine learning algorithms representing the base classifiers. The base classifiers include naive Bayes (NB), decision tree (DT), support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF). Whereas, the logistic regression (LR) classifier was employed to aggregate the outputs of the base classifiers and thus to generate the ensemble classifier output. The results showed a good performance of our proposed ensemble classifier. It beats all single classification algorithms, with a 99.3% accuracy rate and negligible classification errors.
An effective deep learning network for detecting and classifying glaucomatous eye
Authors: Ahmed Md. Tanvir , Ahmed Imran , Rakin Rubayed Ahmmad , Akter Mst. Tuhin , Jahan Nusrat
Published: June 2023

Glaucoma is a well-known complex disease of the optic nerve that gradually damages eyesight due to the increase of intraocular pressure inside the eyes. Among two types of glaucoma, open-angle glaucoma is mostly happened by high intraocular pressure and can damage the eyes temporarily or sometimes permanently, another one is angle-closure glaucoma. Therefore, being diagnosed in the early stage is necessary to safe our vision. There are several ways to detect glaucomatous eyes like tonometry, perimetry, and gonioscopy but require time and expertise. Using deep learning approaches could be a better solution. This study focused on the recognition of open-angle affected eyes from the fundus images using deep learning techniques. The study evolved by applying VGG16, VGG19, and ResNet50 deep neural network architectures for classifying glaucoma positive and negative eyes. The experiment was executed on a public dataset collected from Kaggle; however, every model performed better after augmenting the dataset, and the accuracy was between 93% and 97.56%. Among the three models, VGG19 achieved the highest accuracy at 97.56%.
Development of an intelligent information resource model based on modern natural language processing methods
Authors: Sadirmekova Zhanna , Sambetbayeva Madina , Serikbayeva Sandugash , Borankulova Gauhar , Yerimbetova Aigerim , Murzakhmetov Aslanbek
Published: June 2023

Currently, there is an avalanche-like increase in the need for automatic text processing, respectively, new effective methods and tools for processing texts in natural language are emerging. Although these methods, tools and resources are mostly presented on the internet, many of them remain inaccessible to developers, since they are not systematized, distributed in various directories or on separate sites of both humanitarian and technical orientation. All this greatly complicates their search and practical use in conducting research in computational linguistics and developing applied systems for natural text processing. This paper is aimed at solving the need described above. The paper goal is to develop model of an intelligent information resource based on modern methods of natural language processing (IIR NLP). The main goal of IIR NLP is to render convenient valuable access for specialists in the field of computational linguistics. The originality of our proposed approach is that the developed ontology of the subject area “NLP” will be used to systematize all the above knowledge, data, information resources and organize meaningful access to them, and semantic web standards and technology tools will be used as a software basis.
Factors affecting students’ continuance intention to use teaching performance assessment application from technology continuance theory
Authors: Nurdin Nurdin , Pettalongi Sagaf S , Ahsan Muhammad Nur , Febrianti Vindy Febrianti
Published: June 2023

This study aims to determine university students’ continuance intention in using an android-based teaching performance assessment (TPA) application. For the data gathering instrument, we employed an online structured questionnaire. Two hundred and forty students from four faculties were selected and assigned a five-scale survey. All completed questionnaires were analyzed using analysis of moment structure (AMOS). The findings show that the factors of productivity, performance, relevancy, quality and mobility of the android-based TPA have significantly influenced students’ continuance intention to use the application. The results highlighted that when an android-based system was developed based on the criteria, the long-term use of the android-based TPA application can be consistently maintained to improve universities’ teaching quality assessment. However, our study needs to improve in that the university students may evaluate teaching staffs who are not teaching a subject in their class because all teaching staff has appeared in the application database. In addition, further research needs to limit each lecture based on a specific course to be assessed by a particular student’s class.
Arabic spellchecking: a depth-filtered composition metric to achieve fully automatic correction
Authors: Gueddah Hicham , Lachibi Youssef
Published: June 2023

Digital environments for human learning have evolved a lot in recent years thanks to incredible advances in information technologies. Computer assistance for text creation and editing tools represent a future market in which natural language processing (NLP) concepts will be used. This is particularly the case of the automatic correction of spelling mistakes used daily by data operators. Unfortunately, these spellcheckers are considered writing aids tools, they are unable to perform this task automatically without user’s assistance. In this paper, we suggest a filtered composition metric based on the weighting of two lexical similarity distances in order to reach the auto-correction. The approach developed in this article requires the use of two phases: the first phase of correction involves combining two well-known distances: the edit distance weighted by relative weights of the proximity of the Arabic keyboard and the calligraphical similarity between Arabic alphabet, and combine this measure with the JaroWinkler distance to better weight, filter solutions having the same metric. The second phase is considered as a booster of the first phase, this use the probabilistic bigram language model after the recognition of the solutions of error, which may have the same lexical similarity measure in the first correction phase. The evaluation of the experimental results obtained from the test performed by our filtered composition measure on a dataset of errors allowed us to achieve a 96% of auto-correction rate.
Comparative study of proactive and reactive routing protocols in vehicular ad-hoc network
Authors: Sbayti Oussama , Housni Khalid , Hanin Moulay Hicham , El Makrani Adil
Published: June 2023

In recent years, the vehicular ad-hoc network (VANET), which is an ad-hoc network used by connected autonomous vehicles (CAV) for information processing, has attracted the interest of researchers in order to meet the needs created by the accelerating development of autonomous vehicle technology. The enormous amount of information and the high speed of the vehicles require us to have a very reliable communication protocol. The objective of this paper is to determine a topology-based routing protocol that improves network performance and guarantees information traffic over VANET. This comparative study was carried out using the simulation of urban mobility (SUMO) and network simulator (NS-3). Through the results obtained, we will show that the choice of the type of protocol to use depends on the size of the network and also on the metrics to be optimized.
An efficient asynchronous spatial division multiplexing router for network-on-chip on the hardware platform
Authors: Siddagangappa Renu , Krishnagowda Nayana Dunthur , Srinivas Murthy Deepthi Tumkur
Published: June 2023

The quasi-delay-insensitive (QDI) based asynchronous network-on-chip (ANoC) has several advantages over clock-based synchronous network-on-chips (NoCs). The asynchronous router uses a virtual channel (VC) as a primary flow-control mechanism however, the spatial division multiplexing (SDM) based mechanism performs better over input traffics over VC. This manuscript uses an asynchronous spatial division multiplexing (ASDM) based router for NoC architecture on a field-programmable gate array (FPGA) platform. The ASDM router is configurable to different bandwidths and VCs. The ASDM router mainly contains input-output (I/O) buffers, a switching allocator, and a crossbar unit. The 4-phase 1-of-4 dual-rail protocol is used to construct the I/O buffers. The performance of the ASDM router is analyzed in terms of lower urinary tract symptoms (LUTs) (chip area), delay, latency, and throughput parameters. The work is implemented using Verilog-HDL with Xilinx ISE 14.7 on artix-7 FPGA. The ASDM router achieves % chip area and obtains 0.8 ns of latency with a throughput of 800 Mfps. The proposed router is compared with existing asynchronous approaches with improved latency and throughput metrics.
Fine-tuning U-net for medical image segmentation based on activation function, optimizer and pooling layer
Authors: Younisse Remah , Ghnemat Rawan , Al Saraireh Jaafer
Published: June 2023

U-net convolutional neural network (CNN) is a famous architecture developed to deal with medical images. Fine-tuning CNNs is a common technique used to enhance their performance by selecting the building blocks which can provide the ultimate results. This paper introduces a method for tuning U-net architecture to improve its performance in medical image segmentation. The experiment is conducted using an x-ray image segmentation approach. The performance of U-net CNN in lung x-ray image segmentation is studied with different activation functions, optimizers, and pooling-bottleneck-layers. The analysis focuses on creating a method that can be applied for tuning U-net, like CNNs. It also provides the best activation function, optimizer, and pooling layer to enhance U-net CNN’s performance on x-ray image segmentation. The findings of this research showed that a U-net architecture worked supremely when we used the LeakyReLU activation function and average pooling layer as well as RMSProb optimizer. The U-net model accuracy is raised from 89.59 to 93.81% when trained and tested with lung x-ray images and uses the LeakyReLU activation function, average pooling layer, and RMSProb optimizer. The fine-tuned model also enhanced accuracy results with three other datasets.
Predicting user behavior using data profiling and hidden Markov model
Authors: Elbaghazaoui Bahaa Eddine , Amnai Mohamed , Fakhri Youssef
Published: June 2023

Mental health disorders affect many aspects of patient’s lives, including emotions, cognition, and especially behaviors. E-health technology helps to collect information wealth in a non-invasive manner, which represents a promising opportunity to construct health behavior markers. Combining such user behavior data can provide a more comprehensive and contextual view than questionnaire data. Due to behavioral data, we can train machine learning models to understand the data pattern and also use prediction algorithms to know the next state of a person’s behavior. The remaining challenges for this issue are how to apply mathematical formulations to textual datasets and find metadata that aids to identify the person’s life pattern and also predict the next state of his comportment. The main idea of this work is to use a hidden Markov model (HMM) to predict user behavior from social media applications by analyzing and detecting states and symbols from the user behavior dataset. To achieve this goal, we need to analyze and detect the states and symbols from the user behavior dataset, then convert the textual data to mathematical and numerical matrices. Finally, apply the HMM model to predict the hidden user behavior states. We tested our program and identified that the log-likelihood was higher and better when the model fits the data. In any case, the results of the study indicated that the program was suitable for the purpose and yielded valuable data.
Software defined fog platform
Authors: Nejad Sepideh Sheikhi , Khademzadeh Ahmad , Rahmani Amir masoud , Broumandnia Ali
Published: June 2023

In recent years, the number of end users connected to the internet of things (IoT) has increased, and we have witnessed the emergence of the cloud computing paradigm. These users utilize network resources to meet their quality of service (QoS) requirements, but traditional networks are not configured to backing maximum of scalability, real-time data transfer, and dynamism, resulting in numerous challenges. This research presents a new platform of IoT architecture that adds the benefits of two new technologies: software-defined networking and fog paradigm. Software-defined networking (SDN) refers to a centralized control layer of the network that enables sophisticated methods for traffic control and resource allocation. So, fog paradigm allows for data to be analyzed and managed at the edge of the network, making it suitable for tasks that require low and predictable delay. Thus, this research provides an in-depth view of the platform organize and performance of its base ingredients, as well as the potential uses of the suggested platform in various applications.
Secured authentication of radio-frequency identification system using PRESENT block cipher
Authors: Ramachandra Bharathi , Peter Smitha Elsa
Published: June 2023

The internet of things (IoT) is an emerging and robust technology to interconnect billions of objects or devices via the internet to communicate smartly. The radio frequency identification (RFID) system plays a significant role in IoT systems, providing most features like mutual establishment, key establishment, and data confidentiality. This manuscript designed secure authentication of IoT-based RFID systems using the light-weight PRESENT algorithm on the hardware platform. The PRESENT-256 block cipher is considered in this work, and it supports 64-bit data with a 256-key length. The PRESENT-80/128 cipher is also designed along with PRESENT-256 at electronic codebook (ECB) mode for Secured mutual authentication between RFID tag and reader for IoT applications. The secured authentication is established in two stages: Tag recognition from reader, mutual authentication between tag and reader using PRESENT-80/128/256 cipher modules. The complete secured authentication of IoT-based RFID system simulation results is verified using the chip-scope tool with field-programmable gate array (FPGA) results. The comparative results for PRESENT block cipher with existing PRESENT ciphers and other light-weight algorithms are analyzed with resource improvements. The proposed secured authentication work is compared with similar RFID-mutual authentication (MA) approaches with better chip area and frequency improvements.
Enhanced TextRank using weighted word embedding for text summarization
Authors: Yulianti Evi , Pangestu Nicholas , Jiwanggi Meganingrum Arista
Published: June 2023

The length of a news article may influence people’s interest to read the article. In this case, text summarization can help to create a shorter representative version of an article to reduce people’s read time. This paper proposes to use weighted word embedding based on Word2Vec, FastText, and bidirectional encoder representations from transformers (BERT) models to enhance the TextRank summarization algorithm. The use of weighted word embedding is aimed to create better sentence representation, in order to produce more accurate summaries. The results show that using (unweighted) word embedding significantly improves the performance of the TextRank algorithm, with the best performance gained by the summarization system using BERT word embedding. When each word embedding is weighed using term frequency-inverse document frequency (TF-IDF), the performance for all systems using unweighted word embedding further significantly improve, with the biggest improvement achieved by the systems using Word2Vec (with 6.80% to 12.92% increase) and FastText (with 7.04% to 12.78% increase). Overall, our systems using weighted word embedding can outperform the TextRank method by up to 17.33% in ROUGE-1 and 30.01% in ROUGE-2. This demonstrates the effectiveness of weighted word embedding in the TextRank algorithm for text summarization.
System of gender identification and age estimation from radiography: a review
Authors: Nafi’iyah Nur , Fatichah Chastine , Herumurti Darlis , Astuti Eha Renwi , Putra Ramadhan Hardani , Prakasa Esa , Kristian Yosi
Published: June 2023

Under extreme conditions postmortem, dental radiography examinations can play an essential role in individual identification. In forensic odontology, individual identification traditionally compares antemortem dental records radiographs with those obtained on postmortem examination. As such, these traditional methods are vulnerable to oversights or mistakes in the individual identification of unidentified bodies. Digital technology can develop forensic odontology well. An automatic individual identification system is needed to support the forensic odontology process more easily and quickly because there are still opportunities to be created. We aimed to review the complete range of recent developments in identifying individuals from panoramic radiographs. We study methods in gender identification, age estimation, radiographic segmentation, performance analysis, and promising future directions.
Query expansion using novel use case scenario relationship for finding feature location
Authors: Arwan Achmad , Rochimah Siti , Fatichah Chastine
Published: June 2023

Feature location is a technique for determining source code that implements specific features in software. It developed to help minimize effort on program comprehension. The main challenge of feature location research is how to bridge the gap between abstract keywords in use cases and detail in source code. The use case scenarios are software requirements artifacts that state the input, logic, rules, actor, and output of a function in the software. The sentence on use case scenario is sometimes described another sentence in other use case scenario. This study contributes to creating expansion queries in feature locations by finding the relationship between use case scenarios. The relationships include inner association, outer association and intratoken association. The research employs latent Dirichlet allocation (LDA) to create model topics on source code. Query expansion using inner, outer and intratoken was tested for finding feature locations on a Java-based open-source project. The best precision rate was 50%. The best recall was 100%, which was found in several use case scenarios implemented in a few files. The best average precision rate was 16.7%, which was found in inner association experiments. The best average recall rate was 68.3%, which was found in all compound association experiments.
Machine learning in drug supply chain management during disease outbreaks: a systematic review
Authors: Emmanuel Gunadi , Ramadhan Arief , Zarlis Muhammad , Abdurachman Edi , Trisetyarso Agung
Published: June 2023

The drug supply chain is inherently complex. The challenge is not only the number of stakeholders and the supply chain from producers to users but also production and demand gaps. Downstream, drug demand is related to the type of disease outbreak. This study identifies the correlation between drug supply chain management and the use of predictive parameters in research on the spread of disease, especially with machine learning methods in the last five years. Using the Publish or Perish 8 application, there are 71 articles that meet the inclusion criteria and keyword search requirements according to Kitchenham's systematic review methodology. The findings can be grouped into three broad groupings of disease outbreaks, each of which uses machine learning algorithms to predict the spread of disease outbreaks. The use of parameters for prediction with machine learning has a correlation with drug supply management in the coronavirus disease case. The area of drug supply risk management has not been heavily involved in the prediction of disease outbreaks.
Analysis of code-based digital signature schemes
Authors: Khurana Rupali , Narwal Ekta
Published: June 2023

Digital signatures are in high demand because they allow authentication and non-repudiation. Existing digital signature systems, such as digital signature algorithm (DSA), elliptic curve digital signature algorithm (ECDSA), and others, are based on number theory problems such as discrete logarithmic problems and integer factorization problems. These recently used digital signatures are not secure with quantum computers. To protect against quantum computer attacks, many researchers propose digital signature schemes based on error-correcting codes such as linear, Goppa, polar, and so on. We studied 16 distinct papers based on various error-correcting codes and analyzed their various features such as signing and verification efficiency, signature size, public key size, and security against multiple attacks.
Concept drift and machine learning model for detecting fraudulent transactions in streaming environment
Authors: Shahapurkar Arati , Patil Rudragoud
Published: June 2023

In a streaming environment, data is continuously generated and processed in an ongoing manner, and it is necessary to detect fraudulent transactions quickly to prevent significant financial losses. Hence, this paper proposes a machine learning-based approach for detecting fraudulent transactions in a streaming environment, with a focus on addressing concept drift. The approach utilizes the extreme gradient boosting (XGBoost) algorithm. Additionally, the approach employs four algorithms for detecting continuous stream drift. To evaluate the effectiveness of the approach, two datasets are used: a credit card dataset and a Twitter dataset containing financial fraud-related social media data. The approach is evaluated using cross-validation and the results demonstrate that it outperforms traditional machine learning models in terms of accuracy, precision, and recall, and is more robust to concept drift. The proposed approach can be utilized as a real-time fraud detection system in various industries, including finance, insurance, and
 e-commerce.
Applying textural Law’s masks to images using machine learning
Authors: Abdikerimova Gulzira , Yessenova Moldir , Yerzhanova Akbota , Manbetova Zhanat , Murzabekova Gulden , Kaibassova Dinara , Bekbayeva Roza , Aldashova Madina
Published: June 2023

Currently, artificial neural networks are experiencing a rebirth, which is primarily due to the increase in the computing power of modern computers and the emergence of very large training data sets available in global networks. The article considers Laws texture masks as weights for a machine-learning algorithm for clustering aerospace images. The use of Laws texture masks in machine learning can help in the analysis of the textural characteristics of objects in the image, which are further identified as pockets of weeds. When solving problems in applied areas, in particular in the field of agriculture, there are often problems associated with small sample sizes of images obtained from aerospace and unmanned aerial vehicles and insufficient quality of the source material for training. This determines the relevance of research and development of new methods and algorithms for classifying crop damage. The purpose of the work is to use the method of texture masks of Laws in machine learning for automated processing of high-resolution images in the case of small samples using the example of problems of segmentation and classification of the nature of damage to crops.
Blockchain for global vaccinations efforts: State of the art, challenges, and future directions
Authors: Al-Muhtadi Jalal , Hassan Abeer , Saleem Kashif , Gawanmeh Amjad , Coelho Rodrigues Joel Jose Puga
Published: June 2023

The emergence of the coronavirus disease 2019 (COVID-19) global crisis negatively affected all aspects of human life. One of the most important methods used worldwide to survive this global crisis is the vaccination process to circumvent the proliferation of this pandemic. Many restrictions were alleviated in many countries such as access to public facilities and events. There is a huge amount of data about vaccination campaigns that are collected and maintained worldwide. Although the vaccination data can be analyzed to find out how the alleviation of restrictions can be applied if the data management process requires preserving key aspects like trust, transparency, and availability for easy and reliable access to such data. In this regard, blockchain technology is an excellent choice for meeting the requirements and providing a secure trusted framework for global verification. In this article, the related literature on blockchain technology is surveyed and summarized for all systems that embody solutions. The pros and cons of each solution are presented and provide a comparative summary. Furthermore, a detailed analysis is given to present the current problems and provide a promising mechanism to verify the vaccinated persons anywhere in the world, in a secure manner while retaining individual privacy.
Image compression approach for improving deep learning applications
Authors: Altabeiri Raed , Alsafasfeh Moath , Alhasanat Mohanad
Published: June 2023

In deep learning, dataset plays a main role in training and getting accurate results of detection and recognition objects in an image. Any training model needs a large size of dataset to be more accurate, where improving the dataset size is one of the most research problems that needs enhancement. In this paper, an image compression approach was developed to reduce the dataset size and improve classification accuracy for the trained model using a convolutional neural network (CNN), and speeds up the machine learning process, while maintaining image quality. The results revealed that the best scenario for deep learning models that provided good and acceptable classification accuracy was one that had the following parameters: 80×80 image size, 10 epochs, 64 batch size, 40 images dataset quality (images compressed 60%), and gray image mode. For this scenario a Dog vs Cat dataset is used, and the training time was 48 minutes, classification accuracy was 86%, and images dataset size was 317 MB on storage device. This size makes up 58% of the size of the original image’s dataset, saves 42% of the storage space and reduces the processing resources consumption.
Machine and deep learning techniques for detecting internet protocol version six attacks: a review
Authors: Hasan Kabla Arkan Hammoodi , Anbar Mohammed , Hamouda Shady , Bahashwan Abdullah Ahmed , Al-Amiedy Taief Alaa , Hasbullah Iznan Husainy , Faisal Serri
Published: June 2023

The rapid development of information and communication technologies has increased the demand for internet-facing devices that require publicly accessible internet protocol (IP) addresses, resulting in the depletion of internet protocol version 4 (IPv4) address space. As a result, internet protocol version 6 (IPv6) was designed to address this issue. However, IPv6 is still not widely used because of security concerns. An intrusion detection system (IDS) is one example of a security mechanism used to secure networks. Lately, the use of machine learning (ML) or deep learning (DL) detection models in IDSs is gaining popularity due to their ability to detect threats on IPv6 networks accurately. However, there is an apparent lack of studies that review ML and DL in IDS. Even the existing reviews of ML and DL fail to compare those techniques. Thus, this paper comprehensively elucidates ML and DL techniques and IPv6-based distributed denial of service (DDoS) attacks. Additionally, this paper includes a qualitative comparison with other related works. Moreover, this work also thoroughly reviews the existing ML and DL-based IDSs for detecting IPv6 and IPv4 attacks. Lastly, researchers could use this review as a guide in the future to improve their work on DL and ML-based IDS.
Face recognition for occluded face with mask region convolutional neural network and fully convolutional network: a literature review
Authors: Budiarsa Rahmat , Wardoyo Retantyo , Musdholifah Aina
Published: June 2023

Face recognition technology has been used in many ways, such as in the authentication and identification process. The object raised is a piece of face image that does not have complete facial information (occluded face), it can be due to acquisition from a different point of view or shooting a face from a different angle. This object was raised because the object can affect the detection and identification performance of the face image as a whole. Deep leaning method can be used to solve face recognition problems. In previous research, more focused on face detection and recognition based on resolution, and detection of face. Mask region convolutional neural network (mask
 R-CNN) method still has deficiency in the segmentation section which results in a decrease in the accuracy of face identification with incomplete face information objects. The segmentation used in mask R-CNN is fully convolutional network (FCN). In this research, exploration and modification of many FCN parameters will be carried out using the CNN backbone pooling layer, and modification of mask R-CNN for face identification, besides that, modifications will be made to the bounding box regressor. it is expected that the modification results can provide the best recommendations based on accuracy.
Comprehensive study: machine learning approaches for COVID-19 diagnosis
Authors: Nasir Hussein Amir , Makki Seyed Vahab Al-Din , Al-Sabbagh Ali
Published: June 2023

Coronavirus disease 2019 (COVID-19) is caused a large number of death since has declared as an international pandemic in December 2019, and it is spreading all over the world (more than 200 countries). This situation puts the health organizations in an aberrant demand for urgent needs to develop significant early detection and monitoring smart solutions. Therefore, that new system or solution might be capable to identify COVID-19 quickly and accurately. Nowadays, the science of artificial intelligence (AI), and internet of things (IoT) techniques have an extensive range of applications, it can be initiated a possible solution for early detection and accurate decisions. We believe, combine both of the IoT revolution and machine learning (ML) methods are expected to reshape healthcare treatment strategies to provide smart (diagnosis, treatments, monitoring, and hospitals). This work aims to overview the recent solutions that have been used for early detection, and to provide the researchers a comprehensive summary that contribute to the pandemic control such AI, IoT, cloud, fog, algorithms, and all the dataset and their sources that recently published. In addition, all models, frameworks, monitoring systems, devices, and ideas (in four sections) have been sufficiently presented with all clarifications and justifications. Also, we propose a new vision for early detection based on IoT sensors data entry using 1 million patients-data to verify three proposed methods.
Stock market prediction of Bangladesh using multivariate long short-term memory with sentiment identification
Authors: Islam Md. Ashraful , Sikder Md. Rana , Ishtiaq Sayed Mohammed , Sattar Abdus
Published: June 2023

The prediction of stock market trends is a challenging task due to its dynamic and volatile nature. Research has shown that predicting the stock market, especially in developing nations like Bangladesh, is challenging due to the presence of multiple external factors in addition to technical ones. To address this, this study proposed a novel dataset that includes not only technical stock market data from 2014 to 2021, but also external factors such as news sentiment and other economic indicators like inflation, gross domestic product (GDP), exchange rate, interest rate, and current balance. The goal is to provide a comprehensive view of the Dhaka Stock Exchange (DSE), the largest stock market in Bangladesh. The main objective of this study is to predict the trend of DSE by taking into account both technical stock market data and relevant external factors, and to compare the predictions made with and without using external factors. The study utilized a multivariate long short-term memory (LSTM) neural network for the stock market trend prediction. The experimental results showed that the use of external factors improved the accuracy of the LSTM-based stock market trend predictions by approximately 24%.
A deep reinforcement learning strategy for autonomous robot flocking
Authors: Martinez Fredy , Montiel Holman , Wanumen Luis
Published: June 2023

Social behaviors in animals such as bees, ants, and birds have shown high levels of intelligence from a multi-agent system perspective. They present viable solutions to real-world problems, particularly in navigating constrained environments with simple robotic platforms. Among these behaviors is swarm flocking, which has been extensively studied for this purpose. Flocking algorithms have been developed from basic behavioral rules, which often require parameter tuning for specific applications. However, the lack of a general formulation for tuning has made these strategies difficult to implement in various real conditions, and even to replicate laboratory behaviors. In this paper, we propose a flocking scheme for small autonomous robots that can self-learn in dynamic environments, derived from a deep reinforcement learning process. Our approach achieves flocking independently of population size and environmental characteristics, with minimal external intervention. Our multi-agent system model considers each agent’s action as a linear function dynamically adjusting the motion according to interactions with other agents and the environment. Our strategy is an important contribution toward real-world flocking implementation. We demonstrate that our approach allows for autonomous flocking in the system without requiring specific parameter tuning, making it ideal for applications where there is a need for simple robotic platforms to navigate in dynamic environments.
Soft computing for hazardous waste routing in Malaysia: a review
Authors: Masran Muhamad Hafiz , Abdul-Rahman Syariza , Ariffin Wan Nor Munirah
Published: June 2023

Nowadays, a significant number of researchers are focusing on utilizing soft computing approaches to address the issue of scheduling in applications concerned with hazardous waste management. In Malaysia, there is thoughtless awareness of the management of hazardous waste, even though the production of wastes in hazardous domains at the industrial and domestic levels has been rising lately. According to previous research findings, the location routing problem (LRP) can be designated as one of the models closer to the actual situation, evaluating the most suitable and optimal location for establishing facilities and utilizing transportation for pick-up and distribution. Recent studies have focused on enhancing the LRP model, and its methodologies approach to solve the waste management problem in hazardous domains. In this paper, a comprehensive review of the better promising and practicable mathematical model of LRP and its methodology approach is discussed, as well as an analysis of the publishing pattern and the trend of research over the preceding five years and more, as retrieved from the web of science (WoS) database. In conclusion, this research is significant in ensuring the effectiveness of reliable mathematical model development and suitable methodologies in the future for solving hazardous waste management problems.
A multi-microcontroller-based hardware for deploying Tiny machine learning model
Authors: Nguyen Van-Khanh , Tran Vy-Khang , Pham Hai , Nguyen Van-Muot , Nguyen Hoang-Dung , Nguyen Chi-Ngon
Published: June 2023

The tiny machine learning (TinyML) has been considered to applied on the edge devices where the resource-constrained micro-controller units (MCUs) were used. Finding a good platform to deploy the TinyML effectively is very crucial. The paper aims to propose a multiple micro-controller hardware platform for productively running the TinyML model. The proposed hardware consists of two dual-core MCUs. The first MCU is utilized for acquiring and processing input data, while the second is responsible for executing the trained TinyML network. Two MCUs communicate to each other using the universal asynchronous receiver-transmitter (UART) protocol. The multi-tasking programming technique is mainly applied on the first MCU to optimize the pre-processing new data. A three-phase motors faults classification TinyML model was deployed on the proposed system to evaluate the effectiveness. The experimental results prove that our proposed hardware platform was improved 34.8% the total inference time including pre-processing data of the proposed TinyML model in comparing with single micro-controller hardware platform.
Explainable extreme boosting model for breast cancer diagnosis
Authors: Suresh Tamilarasi , Assegie Tsehay Admassu , Ganesan Sangeetha , Tulasi Ravulapalli Lakshmi , Mothukuri Radha , Salau Ayodeji Olalekan
Published: June 2023

This study investigates the Shapley additive explanation (SHAP) of the extreme boosting (XGBoost) model for breast cancer diagnosis. The study employed Wisconsin’s breast cancer dataset, characterized by 30 features extracted from an image of a breast cell. SHAP module generated different explainer values representing the impact of a breast cancer feature on breast cancer diagnosis. The experiment computed SHAP values of 569 samples of the breast cancer dataset. The SHAP explanation indicates perimeter and concave points have the highest impact on breast cancer diagnosis. SHAP explains the XGB model diagnosis outcome showing the features affecting the XGBoost model. The developed XGB model achieves an accuracy of 98.42%.
IPv6 flood attack detection based on epsilon greedy optimized Q learning in single board computer
Authors: Daru April Firman , Hartomo Kristoko Dwi , Purnomo Hindriyanto Dwi
Published: June 2023

Internet of things is a technology that allows communication between devices within a network. Since this technology depends on a network to communicate, the vulnerability of the exposed devices increased significantly. Furthermore, the use of internet protocol version 6 (IPv6) as the successor to internet protocol version 4 (IPv4) as a communication protocol constituted a significant problem for the network. Hence, this protocol was exploitable for flooding attacks in the IPv6 network. As a countermeasure against the flood, this study designed an IPv6 flood attack detection by using epsilon greedy optimized Q learning algorithm. According to the evaluation, the agent with epsilon 0.1 could reach 98% of accuracy and 11,550 rewards compared to the other agents. When compared to control models, the agent is also the most accurate compared to other algorithms followed by neural network (NN), K-nearest neighbors (KNN), decision tree (DT), naive Bayes (NB), and support vector machine (SVM). Besides that, the agent used more than 99% of a single central processing unit (CPU). Hence, the agent will not hinder internet of things (IoT) devices with multiple processors. Thus, we concluded that the proposed agent has high accuracy and feasibility in a single board computer (SBC).