bluuebunny/crossref_metadata_2025 huggingface dataset:
Homomorphic technique for group data sharing in cloud computing environment
Authors: Karemallaiah Jayalakshmi , Revaiah Prabha
Published: October 2024

The main aim of this research work is to make it easier for the same group to share and store anonymous data on the cloud securely and effectively. This research work presents verifiable privacy-aware enhanced homomorphic (VPEH) for multiple participants; moreover, the enhanced homomorphic encryption mechanism provides end-to-end encryption and allows the secure computation of data without revealing any data in the cloud. The proposed algorithm uses homomorphic multiplication to compute the hashes product of challenges blocks that make it more efficient, furthermore an additional security model is incorporated to verify the shared data integrity. The VPEH mechanism is evaluated considering parameters such as tag generation, proof generation, and verification; model efficiency is proved by observing the marginal improvisation over the other existing models by varying the number of blocks and number of challenge blocks.
Neutrosophic enhanced convolutional neural network for occupancy detection: structured model development and evaluation
Authors: Mittal Ranjeeta , Kumar Suresh , Chugh Urvashi
Published: October 2024

In this study, we introduce an advanced convolutional neural network (CNN) model tailored for house occupancy detection, designed to accommodate the inherent uncertainties and contradictory information often encountered in sensor data. By integrating neutrosophic layers into the CNN architecture, we enable the model to effectively handle indeterminacy, vagueness, and inconsistency present in real-world sensor readings. Our approach employs neutrosophic convolutional, max-pooling, and logic layers, providing a comprehensive framework for feature extraction and decision-making. Through a structured methodology encompassing data preprocessing, model initialization, training, evaluation, and optimization, we demonstrate the efficacy of the proposed model in accurately detecting occupancy status within residential environments. This enhanced CNN model offers improved accuracy, robustness, and interpretability, thereby facilitating its integration into smart home systems and building automation applications, contributing to enhanced efficiency, comfort, and energy savings.
Implementation of innovative approach for detecting brain tumors in magnetic resonance imaging using NeuroFusionNet model
Authors: Kotte Arpitha , Ahmad Syed Shabbeer
Published: October 2024

The goal of this study is to create a strong system that can quickly detect and precisely classify brain tumors, which is essential for improving treatment results. The study uses advanced image processing techniques and the NeuroFusionNet deep learning model to accurately segment data from the brain tumor segmentation (BRATS) dataset, presenting a detailed methodology. The objective is to create a high-precision system that surpasses current methods in key performance metrics. NeuroFusionNet demonstrates outstanding accuracy of 99.21%, as well as impressive specificity and sensitivity rates of 99.17% and 99.383%, respectively, exceeding previous benchmarks. The findings emphasize the system's ability to greatly enhance the diagnostic process, enabling early intervention and ultimately improving patient care in brain tumor detection and classification.
Development of a digital twin of a network of heating systems for smart cities on the example of the city of Almaty
Authors: Tyulepberdinova Gulnur , Kunelbayev Murat , Shiryayeva Olga , Sakypbekova Meruyert , Sarsenbayev Nurlan , Bayandina Gulmira
Published: October 2024

In this paper, a digital twin of the network of heating systems for smart cities is developed using the example of the city of Almaty. The study used machine learning algorithms to estimate future thermal energy consumption and develop thermodynamic formulas. This work offers a thorough and in-depth analysis of thermal energy consumption. In addition, the paper identifies the relationship between thermal energy consumption and ambient temperature, and wind uncertainty in certain urban areas using machine learning methods to predict thermal energy consumption. Using both training and regression models, this interdependence is revealed. The obtained forecasts provide useful information for studying the structure of heat consumption in Almaty and reducing heat losses by reducing overheating in the zones of heating networks. In addition, the study analyzes high-resolution spatial data collected from 385 homes and 62 heat transfer circuits located throughout the city during the heating season. The study examines the degree of relationship between the ambient temperature and the amount of heat energy used in the areas of Astana. A minor impact of wind speed is also estimated. These discoveries allow us to use machine learning algorithms to find the location of hot spots and inefficient zones with high losses.
Assessing risk factors for heart disease using machine learning methods
Authors: Maxutova Natalya , Tussupov Jamalbek , Kedelbayeva Kamilya , Tynykulova Assemgul , Balabayeva Zulfiya , Yersultanova Zauresh , Khamitova Zhainagul , Zhunussova Kamila
Published: October 2024

This paper examines various machine learning methods for assessing risk factors for cardiovascular diseases. To build predictive models, two approaches were used: the extreme gradient boosting (XGBoost) algorithm and a convolutional neural network (CNN). The focus is on analyzing the performance of each model in classification and regression tasks, as well as their ability to identify key biomarkers and risk factors such as cholesterol, ferritin, homocysteine and aspartate aminotransferase (AST) levels. XGBoost parameters have been optimized for working with tabular data, demonstrating high accuracy in risk prediction. The CNN model, despite the initial reduction in error on the training set, showed signs of overfitting when analyzing validation data. Performance evaluation using the metrics of mean squared error (MSE), coefficient of determination (R²), Akaike information criterion (AIC), and Bayesian information criterion (BIC) revealed significant differences between the models. The study results confirm the effectiveness of XGBoost in analyzing tabular data and summarizing risk factor knowledge, while the CNN model needs further optimization to handle sparse data. The work demonstrates the importance of choosing the right model architecture and training parameters to ensure reliable diagnosis of cardiovascular diseases.
Optimal shortest path selection using an evolutionary algorithm in wireless sensor networks
Authors: Rajkumar Dhamodharan Udaya Suriya , Karani Krishna Prasad , Sathiyaraj Rajendran , Vidyullatha Pellakuri
Published: October 2024

A wireless sensor network comprises of distributed independent devices, called sensors that monitor the physical conditions of the environment for various applications, such as tracking and observing environmental changes. Sensors have the ability to detect information, process it, and forward it to neighboring sensor nodes. Wireless sensor networks are facing many issues in terms of scalability, which necessitates numerous nodes and network range. The route chosen between the source node and the destination node with the shortest distance determines how well the network performs. In this paper, evolutionary algorithm based shortest path selection provides high end accessibility of path nodes for data transmission among source and destination. It employs the best fitness function methodology, which involves the replication of input, mutation, crossover, and mutation methods, to produce efficient outcomes that align with the best fitness function, thereby determining the shortest path. This is a probabilistic technique that receives input from learning models and provides the best results. The execution results are presented well compared with earlier methodologies in terms of path cost, function values, throughput, packet delivery ratio, and computation time.
Text encryption using secure and expeditious multiprocessing SerpentCTR using logistic map
Authors: Elshoush Huwaida T. , Ahmed Duaa M. , Ishag Abdalmajid A. , Elsadig Muawia A. , Altigani Abdelrahman
Published: October 2024

Unarguably performance is a critical factor to the success of any cipher. Al-Beit Serpent is more secure than advanced encryption standard (AES), it faces limitations such as speed and memory requirement. Hence, this paper proffers a text encryption method S  that ameliorates the performance by running Serpent in parallel using the counter (CTR) encryption mode and further enhances the security by generating sub-keys for each block using logistic map. The intricate logistic map generated keys adds robustness to the proposed algorithm. Comprehensive experiments using Python 3.9 on commonly used metrics verify the efficacy of the proposed method in terms of execution time, central processing unit (CPU) usage, security analysis including key space, strict avalanche effect and its randomness. The encryption/decryption reduction rate reached up to 80.81%. It is worthy of note that it is effectually resistant to brute force attacks having a large key space in addition to its dependency on the number of blocks besides the randomly generated keys. The enhanced Serpent was examined using the statistical test suite (STS) recommended by the National Institute of Standards and Technology (NIST) and verified its randomness by passing all tests. Furthermore, it efficaciously resisted statistical analysis, particularly histogram and correlation coefficient analysis. Moreover, it prevails over current methods when juxtaposed with them in terms of performance, key space, key sensitivity, avalanche effect, histogram analysis and correlation coefficient, ergo affirming its efficiency.
Precipitation and water discharge for internet of things based flood disaster prediction improvement
Authors: Efendi Rissal , Widiasari Indrastanti R.
Published: October 2024

Floods are a major global problem affect communities and businesses. For these effects to be mitigated and emergency measures to be improved, accurate prediction is essential. Conventional flood prediction models frequently fail because the models ignore important hydrological elements like water discharge and instead solely use rainfall data. This limitation was addressed by the combination of rainfall and water discharge data on internet of things (IoT)-based technologies. It focuses on analyzing historical records from flood-prone areas in Semarang using gated recurrent unit (GRU) models. The findings demonstrate how effectively the GRU model performs when rainfall and water discharge factors are taken into account, resulting in very accurate and dependable predictions of flood events. Precision, Recall, and F1-score are evaluation metrics that demonstrate the accuracy on which the model determines flood emergency statuses. This study advances flood prediction methods and highlights the value of integrating internet of things data to improve preparedness and resilience against flood disasters.
Faculty Assistant Bot-automation of administrative activities using robotic process automation
Authors: Prasad Mamidyala Durga , Nandini Balusu
Published: October 2024

In this paper, a process workflow for Bot is created using robotic process automation (RPA), associated with artificial intelligence that is used to stream line the administrative tasks and alleviate stress levels of faculty in handling administrative tasks while teaching in higher education. These activities are must for National Academic Audit Council (NAAC) accreditation and All India Survey on Higher Education (AISHE) surveys, which strive to bring quality in teaching higher education by shaping educational policies. Therefore, ensuring the accuracy of this data is paramount to avoid misleading decisions. The Bot automatically gathers student results from the website and saves them into individual files, eliminating the need for human intervention. It is trained to find the related file of student and update his results of upcoming semesters or backlogs. The Bot efficiently manages folders during file saving to enhance retrieval. Additionally, it maintains pertinent student details such as community, caste, and religion, beneficial for educational policy surveys aiming for improved quality. Moreover, it generates and updates reports post each process execution, ensuring data integrity, and can be trained for statistical analysis to predict student outcomes. The UiPath tool is used in the design and testing of the developed Bot
Automated lung cancer T-Stage detection and classification using improved U-Net model
Authors: Sathiyamurthy Babu Kumar , Madhaiyan Vinoth Kumar
Published: October 2024

Lung cancer results from the uncontrolled growth of abnormal cells. This research proposes an automated, improved U-Net model for lung cancer detection and tumor staging using the TNM system. A novel mask-generation process using thresholding and morphological operations is developed for the U-Net segmentation process. In the pre-processing stage, an advanced augmentation technique and contrast limited adaptive histogram equalization (CLAHE) are implemented for image enhancement. The improved U-Net model, enhanced with an advanced residual network (ARESNET) and batch normalization, is trained to accurately segment the tumor region from lung computed tomography (CT) images. Geometrical parameters, including perimeter, area, convex area, solidity, roundness, and eccentricity, are used to find precise T-stage of lung cancer. Validation using performance metrics such as accuracy, specificity, sensitivity, precision, and recall shows the proposed hybrid method is more accurate than existing approaches, achieving a staging accuracy of 94%. This model addresses the need for a highly accurate automated technique for lung cancer staging, essential for effective detection and treatment.
Anomaly detection system based on deep learning for cyber physical systems on sensory and network datasets
Authors: Almendli Muhammed , Mohasefi Jamshid Bagherzadeh
Published: October 2024

Cyber-physical systems (CPSs), a type of computing system integrated with physical devices, are widely used in many areas such as manufacturing, traffic control, and energy. The integration of CPS and networks has expanded the range of cyber threats. Intrusion detection systems (IDSs), use signature based and machine learning based techniques to protect networks, against threats in CPSs. Water purifying plants are among the important CPSs. In this context some research uses a dataset obtained from secure water treatment (SWaT) an operational water treatment testbed. These works usually focus solely on sensory dataset and omit the analysis of network dataset, or they focus on network information and omit sensory data. In this paper we work on both datasets. We have created IDSs using five traditional machine learning techniques, decision tree, support vector machine (SVM), random forest, naïve Bayes, and artificial neural network along with two deep methods, deep neural network, and convolutional neural network. We experimented with IDSs, on three different datasets obtained from SWaT, including network data, sensory data, and Modbus data. The accuracies of proposed methods show higher values on all datasets especially on sensory (99.9%) and Modbus data (95%) and superiority of random forest and deep learning methods compared to others.
Design of decryption process for advanced encryption standard algorithm in system-on-chip
Authors: Prathap Joseph Anthony , Raj Mrinal , Patnaik Ritu
Published: October 2024

This paper concentrates on the development of system-on-chip for the decryption algorithm in the advanced encryption standard (AES). This method includes the transformation of cipher text into plain text and consists of 4 sub-tasks based on the resolution. In this work, the 128-bit resolution is utilized to perform 10 rounds of transformation with the round key added at every round generated by the key expansion algorithm. Though there are many cryptography algorithms, the AES is simple, secure, faster in operation, and easy to develop compared to the others. The system-on-chip (SOC) design for the decryption of the AES depends on the synthesizable hardware description language (HDL) code development for all 10 rounds of processes with the key expansion algorithm. The lookup tables (LUTs) are used for the inverse S-box in the HDL code. The proposed SOC is designed using the Cadence electronic design automation (EDA) tools by making use of the synthesized HDL code for the proposed methods and analyzed for the very large-scale integration (VLSI) parameters.
About one lightweight encryption algorithm ensuring the security of data transmission and communication between internet of things devices
Authors: Atanov Sabyrzhan , Seitkulov Yerzhan , Moldamurat Khuralay , Yergaliyeva Banu , Kyzyrkanov Abzal , Seitbattalov Zhexen
Published: October 2024

In this paper, a new encryption algorithm Twine-Mersenne was developed based on the Twine algorithm with the addition of a random number generator for the dynamic generation of S-boxes. Dynamic generation of random numbers based on the Mersenne Twister helps to increase the cryptographic strength of the proposed algorithm. The algorithm we propose solves the issues of optimizing the costs of computing and energy resources of internet of things (IoT) devices, using a combination of lightweight cryptographic principles and fuzzy logic, and also provides reliable security and intelligent authentication of the mobile application user. The paper also considers the practical implementation of the proposed algorithm based on Arduino ESP32, a device with limited computing resources. In addition to this, fuzzy logic has found its practical application in the field of intelligent user authentication in developed mobile applications based on Arduino Studio for mobile cellular applications. As a result, the proposed lightweight encryption algorithm has proven itself to be an effective tool in ensuring the security of data transmission and communication between IoT devices.
A study of Tobacco use and mortality by data mining
Authors: Arenas Laberiano Andrade , Paucar Inoc Rubio , Yactayo-Arias Cesar
Published: October 2024

The use of data mining to address the issue of people who consume tobacco and other harmful substances for their health has led to a significant dependence among smokers, which over time causes illnesses that may result in the addict's death. As a result, the research's goal is to apply a data mining study whose findings showed that the confidence intervals are less than 0.355. However, the lift and conviction in the last three rules are also lower, making it unlikely that these rules will be followed. On the other hand, the knowledge discovery in data bases method was used. It consists of the following stages: data selection, preparation, data mining, and evaluation and interpretation of the results. To that end, comparisons of agile data mining methodologies like crisp-dm, knowledge discovery in data, and Semma are also done. As a result, using specific criteria, dimensions are segmented to allow for the differentiation of these methodologies. As a result, a comparison graph of models such as naive Bayes, decision trees, and rule induction is used. To sum up, it can be said that the rules of association apply to men, the number of admissions, and the cancers that can be brought on by smoking. Also, the percentage of male patients admitted with cancers that can be brought on by smoking Last but not least, the number of admissions and cancers that can be brought on by smoking
A novel semi-supervised consensus fuzzy clustering method for multi-view relational data
Authors: Thi Canh Hoang , Huy Thong Pham , The Huan Phung , Thuy Trang Vu , Nhu Hieu Nguyen , Tien Phuong Nguyen , Nhu Son Nguyen
Published: October 2024

Multi-view data is widely employed in various domains, highlighting the need for advanced clustering methodologies to efficiently extract knowledge from these datasets. Consequently, multi-view clustering has emerged as a prominent research topic in recent years. In this paper, we propose a novel approach: the semi-supervised consensus fuzzy clustering method for multi-view relational data (SSCFMC). This method combines the advantages of fuzzy clustering and consensus clustering to address the challenges posed by multi-view data. By leveraging available labeled information and the relational structure among views, our method aims to enhance clustering performance. Extensive experiments on benchmark datasets demonstrate that our method surpasses existing single-view and multi-view relational clustering algorithms in terms of accuracy and stability. Specifically, the SSCFMC algorithm exhibits superior clustering performance across various datasets, achieving an adjusted rand index (ARI) of 0.68 on the multiple features dataset and an F-measure of 0.91 on the internet dataset, highlighting its robustness and efficiency. Overall, this study advances multi-view clustering techniques for relational data and provides valuable insights for researchers in this field.
Detection and classification of pneumonia using the Orange3 data mining tool
Authors: Altayeb Muneera , Arabiat Areen , Al-Ghraibah Amani
Published: October 2024

A chest X-ray can convey a lot about a patient's condition. However, it requires a specialized and skilled doctor to determine the type of lung disease with high accuracy. Here comes the role of deep learning techniques (DL) and artificial intelligence (AI) in accelerating the process of detecting lung diseases and classifying them with high precision, which saves time and effort for the patient and the doctor alike. This work presents a proposed model for a machine learning (ML) and AI system to analyze chest X-ray images and categorize them into four cases normal, viral pneumonia, bacterial pneumonia, and coronavirus disease 2019 (COVID-19). The system relies on extracting Mel frequency cepstral coefficient (MFCC) features from a dataset consisting of 4,800 chest X-ray images, and then these features are used to train four basic classifiers based on the data mining tool Orange3, which are adaptive boosting (AdaBoost), decision trees (DTs), gradient boosting (GB), and random forest (RF). The model was tested and evaluated, where the AdaBoost classifier excelled with an accuracy of 100%, followed by RF with an accuracy of 99.5%. Finally, GB and DTs came with a classification accuracy of 98.5%, and 97.2%, respectively.
Implementation of artificial intelligence in the prediction of the elastic characteristics of bio-loaded polypropylene with bamboo fibers
Authors: Laabid Zineb , Lakhdar Abdelghani , Mansouri Khalifa , Siadat Ali
Published: October 2024

Artificial intelligence is the current trend in the world, which has taken the opportunity to advance in all its fields, particularly in scientific research. In materials engineering, the results obtained from classic methods such as experimentation, homogenization methods, or finite element methods have become input and validation elements for intelligent models to obtain more effective results in an optimal time frame. In this article, we discuss the use of artificial neural networks to determine the mechanical properties of biocomposites, which are the subject of much research due to the advantages they represent. The properties of these complex materials depend on various parameters, such as the behavior of the constituent materials, the percentage of the mixture, and the manufacturing process. In this work, our goal is to predict how polypropylene behaves elastically when reinforced with 15% various natural fillers. and we will study the impact of bamboo on polypropylene to test and validate our model. By exploiting the results of the Mori-Tanaka model, we were able to generate our dataset, with which we feed our feedforward backpropagation neural network and demonstrate that our biocomposite gained in terms of stiffness, marked by an increase in Young's modulus to 550.3 MPa, with better performance validation and a very good regression coefficient.
Noise reduction in Hyperion high dynamic range hyperspectral data using machine learning and statistical techniques
Authors: Nair Priyanka , Srivastava Devesh Kumar , Bhatnagar Roheet
Published: October 2024

Numerous remote sensing applications rely heavily on hyperspectral imagery, but it is frequently plagued by noise, which degrades the data quality and hinders subsequent analysis. In this research paper, we present an in-depth analysis of noise removal techniques for hyperspectral imagery, specifically for data acquired from the Hyperion EO-1 sensor. Setting off with obtaining Hyperion data and the pre-processing stages, the paper discusses the acquisition and denoising of Hyperion data. The hyperspectral data considered is in the high dynamic range (HDR) format, which maintains the original imagery's complete dynamic range. The study explores various noise reduction methods, such as minimum noise fraction (MNF), principal component analysis (PCA), wavelet denoising, non-local means (NLM), and denoising autoencoders, aimed at enhancing the signal-to-noise ratio. The effectiveness of these techniques is evaluated through visual quality, mean square error (MSE), and peak signal-to-noise ratio (PSNR), alongside their impact on mineral exploration. Furthermore, the paper investigates the application of machine learning algorithms on denoised data for mineral identification, highlighting the potential of integrating denoising techniques with machine learning for improved mineral exploration. This comparative analysis aims to identify the most efficient noise removal methods for hyperspectral imagery, facilitating higher quality data for subsequent analysis.
Systematic review: State-of-the-art in sensor-based abnormality respiration classification approaches
Authors: Razman Nur Fatin Shazwani Nor , Nasir Haslinah Mohd , Zainuddin Suraya , Brahin Noor Mohd Ariff , Ibrahim Idnin Pasya , Mispan Mohd Syafiq
Published: October 2024

Respiration-related disease refers to a wide range of conditions, including influenza, pneumonia, asthma, sudden infant death syndrome (SIDS) and the latest outbreak, coronavirus disease 2019 (COVID-19), and many other respiration issues. However, real-time monitoring for the detection of respiratory disorders is currently lacking and needs to be improved. Real-time respiratory measures are necessary since unsupervised treatment of respiratory problems is the main contributor to the rising death rate. Thus, this paper reviewed the classification of the respiratory signal using two different approaches for real-time monitoring applications. This research explores machine learning and deep learning approaches to forecasting respiration conditions. Every consumption of these approaches has been discussed and reviewed. In addition, the current study is reviewed to identify critical directions for developing respiration real-time applications.
Sectoral vulnerabilities and adaptations to climate change: insights from a systematic literature review
Authors: Prihandoko Prihandoko , Windarto Agus Perdana , Yanto Musli , Yuhandri Muhammad Habib
Published: October 2024

Climate change is an urgent global issue impacting various life sectors, including health, agriculture, and infrastructure. This systematic literature review (SLR) aims to provide a comprehensive synthesis of research on sectoral vulnerabilities and adaptation strategies to climate change. Utilizing bibliometric analysis, the review identifies key themes and research gaps, highlighting the successes and challenges in implementing adaptation strategies. Key findings reveal that topics such as climate change, adaptive management, agriculture, public health, and food security are central to the research discourse. However, areas like health equity, sanitation, and agricultural worker adaptation remain under-researched. The analysis underscores the necessity for holistic, context-specific, and innovative approaches to policy-making, Scopus integrating sustainable development and public health to enhance resilience and adaptive capacity in vulnerable regions. This review offers valuable insights for researchers and policymakers aiming to develop effective adaptation strategies and address the multifaceted challenges of climate change.
A significant features vector for internet traffic classification based on multi-features selection techniques and ranker, voting filters
Authors: Munther Alhamza , Abualhaj Mosleh M. , Alalousi Alabass , Fadhil Hilal A.
Published: October 2024

The pursuit of effective models with high detection accuracy has sparked great interest in anomaly detection of internet traffic. The issue still lies in creating a trustworthy and effective anomaly detection system that can handle massive data volumes and patterns that change in real-time. The detection techniques used, especially the feature selection methods and machine learning algorithms, are crucial to the design of such a system. The fundamental difficulty in feature selection is selecting a smaller subset of features that are more related to the class but are less numerous. To reduce the dimensionality of the dataset, this research offered a multi-feature selection technique (MFST) using four filter techniques: fast correlation-based filter, significance feature evaluator, chi-square, and gain ratio. Each technique's output vector is put via ranker and Borda voting filters. The feature with the highest number of votes and rank values will be selected from the dataset. The performance of the given MFST framework was the best when compared to the four strategies listed above functioning alone; three different classifiers were employed to test the accuracy. C4.5, nave Bayes, and support vector machine. The experiment outcomes employed ten datasets of different sizes with 10,000-300,000 instances. Only 8 out of 248 characteristics were chosen, with classifiers percentages averaging 65%, 93.8%, and 95.5%.
Comparative analysis of deep Siamese models for medical reports text similarity
Authors: Kurniasari Dian , Usman Mustofa , Warsono Warsono , Lumbanraja Favorisen Rosyking
Published: October 2024

Even though medical reports have been digitized, they are generally text data and have not been used optimally. Extracting information from these reports is challenging due to their high volume and unstructured nature. Analyzing the extraction of relevant and high-quality information can be achieved by measuring semantic textual similarity (STS). Consequently, the primary aim of this study is to develop and evaluate the performance of four models: Siamese Manhattan convolution neural network (CNN), Siamese Manhattan long short-term memory (LSTM), Siamese Manhattan hybrid CNN-LSTM, and Siamese Manhattan hybrid LSTM-CNN, in determining STS between sentence pairs in medical reports. Performance comparisons were conducted using Cosine Similarity and word mover's distance (WMD) methods. The results indicate that the Siamese Manhattan hybrid LSTM-CNN model outperforms the other models, with a similarity score of 1 for each sentence pair, signifying identical semantic meaning.
Braille code classifications tool based on computer vision for visual impaired
Authors: Sadak Hany M. , Khalaf Ashraf A. M. , Hussein Aziza I. , Salama Gerges Mansour
Published: October 2024

Blind and visually impaired people (VIP) face many challenges in writing as they usually use traditional tools such as Slate and Stylus or expensive typewriters as Perkins Brailler, often causing accessibility and affordability issues. This article introduces a novel portable, cost-effective device that helps VIP how to write by utilizing a deep-learning model to detect a Braille cell. Using deep learning instead of electrical circuits can reduce costs and enable a mobile app to act as a virtual teacher for blind users. The app could suggest sentences for the user to write and check their work, providing an independent learning platform. This feature is difficult to implement when using electronic circuits. A portable device generates Braille character cells using light- emitting diode (LED) arrays instead of Braille holes. A smartphone camera captures the image, which is then processed by a deep learning model to detect the Braille and convert it to English text. This article provides a new dataset for custom-Braille character cells. Moreover, applying a transfer learning technique on the mobile network version 2 (MobileNetv2) model offers a basis for the development of a comprehensive mobile application. The accuracy based on the model reached 97%.
Wheelchair safety system using fuzzy logic controller to avoid obstruction
Authors: Yulianto Endro , Salwa Umaimah Mitsalia Ummi , Triwiyanto Triwiyanto , Indarto Tri Bowo
Published: October 2024

A wheelchair is the primary means of mobility for individuals unable to walk. This study aimed to develop a safety system for electric wheelchairs to help people with tetraplegia avoid obstructions. The main contribution of this study is the implementation of a sensor with a wider reflection angle and the adjustment of the wheelchair's speed based on the distance to the obstruction, eliminating the need for manual speed selection. The safety system utilizes LV-MaxSonarEZ1 ultrasonic sensors, which function as reflectance distance readers placed on the front, rear, right, and left sides of the wheelchair. The output from the sensors is input into an Arduino, which functions as the controller. The safety system employs adaptive speed control based on distance through a fuzzy logic controller. The wheelchair was tested with obstruction distances of 1, 1.8, 3, and 10 m. The wheelchair could stop at a distance of 34.06 cm for forward movement and 45.16 cm for reverse movement. The results of this study successfully demonstrate the creation of a safety system on a wheelchair using ultrasonic sensors to avoid obstructions and detect areas, with more adaptive speed control based on distance through a fuzzy logic controller.
Hybrid metaheuristic algorithms: a recent comprehensive review with bibliometric analysis
Authors: Nassef Ahmed M. , Abdelkareem Mohammad Ali , Maghrabie Hussein M. , Baroutaji Ahmad
Published: October 2024

Metaheuristic algorithms are widely used in various applications. Collaborating two or more algorithms in a hybrid form has shown great improvements in terms of the algorithm's performance. This paper highlights the recently published work during the last decade from a quantitative perspective. The biometric measures include the number of publications, citations, average citations per publication, h-index, and field-weighted citation impact (FWCI) based on the data extracted from the Scopus database. Statistical measures, co-occurrence and co-authorship maps, and illustrative graphs have been implemented using software tools. According to the collected data, about 3469 articles have been published during the last decade with an increasing rate of 44.1 papers per year. Most of these articles have been published as journal articles with a percentage of 68.3%, followed by conference articles occupied 29.5%. China, India and Iran contributed the largest number of articles at 1076, 965, and 239, respectively. Parouha, Verma, and Kamel, are the top-ranked authors with 14, 10, and 9 publications, respectively. The most areas of interest are computer science, engineering and mathematics with publication percentages of 27.69%, 25.55% and 13.91%, respectively. The data presented in this paper gives the researchers a clear image of this hot topic to start new research.
Homonym and polysemy approaches with morphology extraction in weighting terms for Indonesian to English machine translation
Authors: Harjo Budi , Muljono Muljono , Abdullah Rachmad
Published: October 2024

Homonym and polysemy features can influence some errors in translation from a source language to another target language, for example, from Indonesian to English. A lemma or a morphology factor can cause the configuration of Indonesian homonym features. For example, the word beruang can mean an animal beruang (bear) and can mean a verb alternation ber+uang (has/have money). The Indonesian polysemy feature can also impact an error in the translation process because it can have a literal meaning and a symbolic meaning. For example, the terms bunga melati (jasmine flower) and bunga hati (lover), where bunga does not only mean flower. Therefore, the development machine translation (MT) method needs to capture homonym and polysemy features in the form of a word or a phrase. This research proposes homonym and polysemy approaches with morphology extraction in weighting terms for Indonesian to English MT. First, this research uses morphology extraction to detect sentences that contain prefixes, lemma, and suffixes. Then, the word similarity measurement functions to extract homonym and polysemy in the form of uni-gram and bi-gram using bidirectional encoder representations from transformers (BERT) embedding, named entity recognition (NER), synonym-based term expansion, and semantic similarity. This research uses neural machine translation for the translation process.
Enhancing El Niño-Southern oscillation prediction using an attention-based sequence-to-sequence architecture
Authors: Setiawan Karli Eka , Fredyan Renaldy , Alam Islam Nur
Published: October 2024

The ability to accurately predict the EI Nino-Southern oscillation (ENSO) is essential for seasonal climate forecasting. Monitoring the Pacific Ocean's surface temperature has many benefits for human life, including a better understanding of climate and weather, the ability to predict summer and winter, the ability to manage natural resources, serving as a reference for maritime transportation and navigation needs, serving as a reference for climate change monitoring needs, and even serving as a renewable energy source by utilizing high sea surface temperatures. This study introduces a deep learning (DL) model with AttentionSeq2Luong model as our proposed model to the ENSO research community. The present study showcases the capability of our proposed model to effectively forecast the forthcoming monthly average Nino index compared to the baseline seq2seq architecture model. For the dataset, this study utilized monthly observations of Nino 12, Nino 3, Nino 34, and Nino 4 between January 1870 and August 2022. The brief result of our experiment was that applying Luong Attention in the seq2seq model reduced the RMSE error by around 0.03494, 0.04635, 0.03853, and 0.03892 for forecasting Nino 12, Nino 3, Nino 34, and Nino 4, respectively.
A classification model for predicting course outcomes using ensemble methods
Authors: Al-Momani Emad , Shatnawi Ala'a , Almomani Mohammad , Almomani Ammar , Alauthman Mohammad
Published: October 2024

Educational data mining has sparked a lot of attention in latest years. Many machine learning methods have been suggested to discover hidden information from educational data. The extracted knowledge assists institutions in enhancing the effectiveness of teaching tactics and the quality of education. As a result, it improves students' performance and educational outputs overall. In this paper, a classification model was built to classify students' grades in a specific course into different categories (binary and multi-level classification tasks). The dataset contains features related to academic and non-academic information. The models were built using a variety of machine learning algorithms: decision tree (J48), support vector machine (SVM), and k-nearest neighbor (K-NN). Furthermore, ensemble methods (bagging, boosting, random subspace, and random forest) which combined multiple decision tree classifiers were implemented to improve the models' performance. The data set was modified under two stages: features selection method and data augmentation using a method called synthetic minority over sampling technique (SMOTE). Based on the results of the experiments, it is possible to predict the students' performance successfully by using machine learning algorithms and ensemble methods. Random subspace obtained the best accuracy at two-level classification task with modified data with 91.20%. At the three-level classification task, the best accuracy was obtained by random forest with 87.18%.
Developing a mathematical model for predicting ultimate tensile strength to identify optimal machining parameters
Authors: Thilagham Kancheepuram Thirumal , Ladha Lekshmy Premachandran , Tiwary Anand Prakash , Haribhau Munde Kashinath , Dudhajirao Darade Pradipkumar , Kumar Shailseh Ranjan
Published: October 2024

Identifying the ultimate tensile strength (UTS) for friction stir welded joints between AA6082-T6 and AA2014-T87 is crucial for ensuring material compatibility, optimizing welding parameters, and assessing mechanical performance. This information helps engineers design safer, more reliable structures and optimize the welding process, improving the utilization of these aluminum alloys in high-performance applications. Traditional methods for identifying UTS face challenges such as material variability, precise experimental setup, the influence of welding parameters, and are time-consuming and costly. This research aims to develop a mathematical model capable of identifying the UTS based on given inputs, specifically optimal tilt angle, travel speed, and rotational speed. The developed model is further utilized to identify the optimal machining parameters. Processing this manually or through trial and error is time-consuming and complex, highlighting the need to incorporate optimization techniques to determine the optimal parameters efficiently. This research involves several optimization techniques, among which the evolved wild horse optimization (EWHO) performs better, achieving a mean square error of 0.45. This is superior performance compared to other optimization techniques and employed prediction models. This approach saves time, reduces complexity, and enhances precision compared to manual or trial-and-error methods, ultimately improving the efficiency and reliability of material processing.
Efficient smart distributed face identification using the MixMaxSim decision function
Authors: Ahmadi Sayed Mohammad , Dianat Rouhollah
Published: October 2024

Recognizing a large number of people is a common challenge in face identification applications, involving decreased accuracy, increased memory and time complexities. To address these issues, this study introduces a three-module approach: “toilers,” “affinity-meter,” and “decision-maker.” Unlike the random distribution methods used in previous solutions, this method employs clustering to distribute the problem into subnetworks called “toilers.” The toiler’s module calculates the likelihood of test data belonging to each class of each toiler, using the last layer outputs of deep learning models. Meanwhile, the affinity-meter module determines the similarity between the test data and the average of each class, employing a similarity measure. The decision-maker module combines the reports from the previous two modules and selects the final class, utilizing a mix of the max-max criterion and the similarity criterion. The proposed method outperforms existing solutions, achieving improved recall, precision, and F1-score. It effectively addresses memory, speed, and accuracy issues in face identification, surpassing both no-distribution and random methods on Glint360K, VGGFace2, and MS-Celeb-1M datasets. Overall, this method offers a more efficient and accurate approach by distributing the problem into subnetworks, demonstrating superior performance and scalability for large-scale face recognition applications.
Control and monitoring of 1 phase generator automatic voltage regulator internet of things
Authors: Hasibuan Arnawan , Akbar Farhan , Meliala Selamat , Rosdiana Rosdiana , Putri Raihan , Nrartha I Made Ari
Published: October 2024

This research focuses on the importance of maintaining generator output voltage stabilization using automatic voltage regulator (AVR), especially in dealing with load impacts and environmental changes. Through the implementation of internet of things (IoT), this system can be controlled and monitored remotely, enabling real-time monitoring without physical presence at the generator location. The main objective of this research is to increase energy efficiency by optimizing generator operation. System development methods include design, prototyping and testing stages. Test results show that the automatic voltage regulator is effective in maintaining a stable output voltage of around 220 V, even though the current varies according to the existing load. The power produced ranges from 23 to 670 W, with a power factor between 0.7 and 1. Despite a slight voltage drop to 217 V, the power factor increases to 0.93. The system uses NodeMCU to send data to Blynk and Google Spreadsheets servers, as well as servo motors and PZEM-004T sensors for control and monitoring. Overall, this research shows that the internet of things-based automatic voltage regulator system is effective in maintaining stability and increasing generator operational efficiency, with the ability to manage voltage, current, power, and power factor efficiently.
A performance evaluation of the internet of things-message queue telemetry transport protocol based water level warning system
Authors: Sonklin Kachane , Sonklin Chanipa
Published: October 2024

The internet of things (IoT) and message queue telemetry transport (MQTT) play crucial roles in connecting sensor networks, data exchange among diverse devices, and enabling various smart systems. Several studies have been conducted on IoT-MQTT-based applications because of their ease of implementation and deployment. It also offers real-time and reliable communication between a publisher and a subscriber. However, there is a lack of comprehensive studies covering overall performance metrics. Therefore, this paper aims to develop a water level warning system prototype and evaluate its performance through simulation experiments, focusing on critical metrics, such as latency, throughput, packet loss rate (PLR), packet delivery ratio (PDR), and availability at various data transmission rates. The results demonstrate that the proposed system achieves significantly lower latency, compared to existing solutions and achieves up to 98% availability and reliability with minimal packet loss. The experimental findings also reveal that higher data transmission rates lead to higher throughput and latency performance with lower performance in terms of availability, PDR, and sensor accuracy.
Edge internet of things based smart home passwordless authentication
Authors: Helal Maha , Aldawsari Abdullah , Al-Akhras Mousa , Shawar Bayan Abu , Omar Hani
Published: October 2024

The internet of things (IoT) has transformed the way appliances and devices are connected and especially in the case of smart homes, in which smart devices can communicate through networks to improve everyday activities. However, it might be difficult to provide a high level of security for the data produced by these devices. Current security mechanisms might not always function adequately in all circumstances, especially when the number of devices increases. This research proposes an edge IoT-based smart home authentication scheme that adopts IPv6. For devices that use a smartphone application, it also offers a passwordless user authentication approach through the use of the smartphone ID and biometrics. The proposed authentication scheme was simulated to verify its ease of use and security. Security and cost analysis was also performed by reviewing and comparing the proposed scheme with previous research on IoT authentication systems. This research finds that the proposed authentication scheme is efficient at shielding home IoT networks from possible attacks, as well as maintaining a high level of usability.