bluuebunny/crossref_metadata_2025 huggingface dataset:
Android-based smart digital marketplace application on agricultural commodities using a new variant recommendation system
Authors: Subiyanto Subiyanto , Prajanti Sucihatiningsih Dian Wisika , Salim Nur Azis , Prabowo Setya Budi Arif , Sutrisno Deyndrawan , Anantyo Andika , Anggriani Dewi
Published: January 2025

In the marketing of agricultural products, addressing the challenges associated with extensive distribution chains is essential, as these directly affect sellers. Additionally, the vast array of available product options often overwhelms customers, complicating their efforts to identify and purchase items that align with their preferences. This work aims to develop a smart e-commerce application for agribusiness, specifically designed for agricultural products on the Android platform. The application integrates a recommendation system that utilizes geolocation-aware neural graph collaborative filtering (GA-NGCF), which facilitates product marketing for farmers and streamlines the product search and selection process for users based on personalized preferences. The development process encompassed various stages, from planning to rigorous testing. The application’s recommendation system, which implements GA-NGCF, operates based on three primary elements: the creation of a geolocation graph of user-item data, the integration of information between neighboring nodes, and the prediction of user preferences. The resulting smart agribusiness e-commerce application, enhanced by GA-NGCF, demonstrated marked improvements in recommendation accuracy and overall application performance during testing. Empirical results indicated substantial enhancements in recommendation metrics, with GA-NGCF achieving a recall of 0.34, a precision of 0.36, and normalized discounted cumulative gain of 0.37, thereby outperforming existing models.
Kafka-machine learning based storage benchmark kit for estimation of large file storage performance
Authors: Rao Sanjay Kumar Naazre Vittal , Kumar Anitha Chikkanayakanahalli Lokesh , Kamble Subhash
Published: January 2025

Efficient storage and maintenance of big data is important with respect to assuring accessibility and cost-friendliness to improve risk management and achieve an effective comprehension of the user requirements. Managing the extensive data volumes and optimizing storage performance poses a significant challenge. To address this challenge, this research proposes the Kafka-machine learning (ML) based storage benchmark kit (SBK) designed to evaluate the performance of the file storage system. The proposed method employs Kafka-ML and a drill-down feature to optimize storage performance and enhance throughput. Kafka-ML-based SBK has the capability to optimize storage efficiency and system performance through space requirements and enhance data handling. The drill-down search feature precisely contributes through reducing disk space usage, enabling faster data retrieval and more efficient real-time processing within the Kafka-ML framework. The SBK aims to provide transparency and ease of utilization for benchmarking purposes. The proposed method attains maximum throughput and minimum latency of 20 MBs and 70 ms, respectively on the number of data bytes is 10, as opposed to the existing method SBK Kafka.
Enhancing spatiotemporal weather forecasting accuracy with 3D convolutional kernel through the sequence to sequence model
Authors: Fredyan Renaldy , Setiawan Karli Eka , Minor Kelvin Asclepius
Published: January 2025

Accurate weather forecasting is important when dealing with various sectors, such as retail, agriculture, and aviation, especially during extreme weather events like heat waves, droughts, and storms to prevent disaster impact. Traditional methods rely on complex, physics-based models to predict the Earth's stochastic systems. However, some technological advancements and the availability of extensive satellite data from beyond Earth have enhanced meteorological predictions and sent them to Earth's antennae. Deep learning models using this historical data show promise in improving forecast accuracy to enhance how models learn the data pattern. This study introduces a novel architecture, convolutional sequence to sequence (ConvSeq2Seq) network, which employs 3D convolutional neural networks (CNN) to address the challenges of spatiotemporal forecasting. Unlike recurrent neural network (RNN)--based models, which are time-consuming due to sequential processing, 3D CNNs capture spatial context more efficiently. ConvSeq2Seq overcomes the limitations of traditional CNN models by ensuring causal constraints and generating flexible length output sequences. Our experimental results demonstrate that ConvSeq2Seq outperforms traditional and modern RNN-based architectures in both prediction accuracy and time efficiency, leveraging historical meteorological data to provide a robust solution for weather forecasting applications. The proposed architecture outperforms the previous method, giving new insight when dealing with spatiotemporal with high density.
Target image validation modeling using deep neural network algorithm
Authors: Mubarakah Naemah , Sihombing Poltak , Efendi Syahril , Fahmi Fahmi
Published: January 2025

Research on image validation models is an interesting topic. The application of deep learning (DL) for object detection has been demonstrated to effectively and efficiently address the challenges in this field. Deep neural networks (DNN) are deep learning algorithms capable of handling large datasets and effectively solving complex problems due to their robust learning capacity. Despite their ability to address complex problems, DNN encounter challenges related to the necessity for intricate architectures and a large number of hidden layers. The objective of this research is to identify the most effective model for achieving optimal performance in image validation. This study investigates target image validation using DNN algorithms, examining architectures with 3, 4, 5, and 6 hidden layers. This study also evaluates the performance of image validation across various activation functions, batch sizes, and numbers of neurons. The results of the study show that the best performance for image validation is achieved using the Leaky-ReLU and Sigmoid activation functions, with a batch size of 64, and an architecture consisting of 3 hidden layers with neuron sizes of 256, 128, and 64. This model is capable of providing real-time target image validation with an accuracy of up to 94.31%.
Object retrieval analysis on plastic bottle waste recycling-based image control using convex hull algorithm and autoregressive integrated moving average prediction method
Authors: Marisa Marisa , Azhar Ramli Azizul , Fudzee Mohd Farhan Md , Abdullah Zubaile
Published: January 2025

In Indonesia, plastic garbage bottles are the most common sort of waste. Given that waste is expected to grow annually, managing plastic waste is a major challenge. The results of the study were achieved by comparing the reference, which was a collection of manually created contour images, with 50 sets of vortex images with different forms and vortex areas as experimental objects. The results indicate that the suggested approach reports a mean error of 2.84%, a correlation coefficient of 0.9965, and a root mean square error of 0.2903 when compared to the manual extraction method. These findings imply that the extract area determined by the procedure outlined in this research is more accurate and nearer to the actual values. The proposed method can therefore be used in place of the traditional process for investigating cooling parameters through manual testing. With measurement values mean absolute percentage error (MAPE)=121,842, mean absolute deviation (MAD)=20,140, and mean squared deviation (MSD)=776,712, the trend analysis of plastic bottles for autoregressive integrated moving average (ARIMA) modeling leads to the conclusion that the waste from plastic bottles will continue to rise annually and that efforts must be made to address this trend with knowledge and waste recycling technology. Plastic that is advantageous to industry and society.
Ensemble of winning tickets: pruning bidirectional encoder from the transformers attention heads for enhanced model efficiency
Authors: Smarts Nyalalani , Selvaraj Rajalakshmi , Kuthadi Venu Madhav
Published: January 2025

The advanced models of deep neural networks like bidirectional encoder from the transformers (BERT) and others, poses challenges in terms of computational resources and model size. In order to tackle these issues, techniques of model pruning have surfaced as the most useful methods in addressing the issues of model complexity. This research paper explores the concept of pruning BERT attention heads across the ensemble of winning tickets in order to enhance the efficiency of the model without sacrificing performance. Experimental evaluations show how effective the approach is, in achieving significant model compression while still maintaining competitive performance across different natural language processing tasks. The key findings of this study include model size that has been reduced by 36%, with our ensemble model reaching greater performance as compared to the baseline BERT model on both Stanford Sentiment Treebank v2 (SST-2) and Corpus of Linguistic Acceptability (CoLA) datasets. The results further show a F1-score of 94% and 96%, respectively, and accuracy scores of 95% and 96% on the two datasets. The findings of this research paper contribute to the ongoing efforts in enhancing the efficiency of large-scale language models.
Enhancing plant disease detection using machine learning approaches for improved agricultural productivity
Authors: Piska Ganga , Janaskar Swarali , Chandgadkar Ojas , Bhawsar Paras , Vishwakarma Pinki Prakash
Published: January 2025

India's agricultural sector faces persistent challenges due to the prevalence of plant diseases, which severely impact crop quality and productivity, exacerbating the ongoing food supply crisis. Traditional methods of diagnosing plant diseases are often time-consuming, labor-intensive, and prone to inaccuracies, making it difficult for farmers to implement timely interventions. To address these issues, a forward-looking strategy utilizing artificial intelligence (AI) and machine learning (ML) has been proposed, aiming to revolutionize disease detection and management in agriculture. This involves the development of a comprehensive novel dataset named Leafsnap, which is uniquely sourced directly from real-world agricultural environments. This dataset ensures the authenticity and relevance of the data, reflecting the actual conditions faced by farmers. Leafsnap serves as a foundation for training advanced algorithmic models designed to identify patterns and symptoms indicative of various leaf diseases. The proposed system leverages a combination of cutting-edge AI and ML techniques, including convolutional neural networks (CNN), random forest (RF), support vector machines (SVM), and extreme gradient boosting (XGBoost) and logistic regression (LR). By integrating these advanced computational techniques into agricultural practices, the system aims to provide farmers with an efficient, reliable, and scalable solution for disease management. The ultimate goal is to foster agricultural sustainability by minimizing crop losses due to disease, thereby bolstering food security and supporting the livelihoods of farmers across India.
Markov processes in Bayesian network computation
Authors: Shayakhmetova Assem , Tasbolatuly Nurbolat , Akhmetova Ardak , Abdildayeva Assel , Shurenov Marat , Sultangaziyeva Anar
Published: January 2025

The article examines the influence of Markov processes on computations in Bayesian networks (BN), an important area of research within probabilistic graphical models. The concept of Bayesian Markov networks (BMN) is introduced, an extension of traditional Bayesian networks with the addition of a Markov constraint, according to which the probability in a node can only depend on the state of neighboring nodes. This constraint makes the model more realistic for many practical tasks, as most graphical models that reflect real-world processes possess the Markov property. The article also discusses that Bayesian networks, in the absence of evidence, actually exhibit the Markov property. However, when evidence (additional information) is introduced into the model, challenges arise that require more complex computational methods. In response, the article proposes algorithms adapted for working with Bayesian Markov networks in the presence of evidence. These algorithms are aimed at optimizing computations and reducing computational complexity. Additionally, a comparative analysis of calculations in Bayesian networks without Markov constraints and with them is conducted, highlighting the advantages and disadvantages of each approach. Special attention is paid to the practical applications of the proposed methods and their effectiveness in various scenarios.
A novel technique for selecting financial parameters and technical indicators to predict stock prices
Authors: Bagalkot Sneha S. , H. A. Dinesha , Naik Nagaraj
Published: January 2025

Stock price predictions are crucial in financial markets due to their inherent volatility. Investors aim to forecast stock prices to maximize returns, but accurate predictions are challenging due to frequent price fluctuations. Most literature focuses on technical indicators, which rely on historical data. This study integrates both financial parameters and technical indicators to predict stock prices. It involves three main steps: identifying essential financial parameters using recursive feature elimination (RFE), selecting quality stocks with a decision tree (DT), and forecasting stock prices using artificial neural networks (ANN), deep neural networks (DNN), and extreme gradient boosting (XGBoost). The models’ performance is evaluated with root mean square error (RMSE) and mean absolute error (MAE) scores. ANN and DNN models showed superior performance compared to the XGBoost model. The experiments utilized Indian stock data.
4HAN: hypergraph-based hierarchical attention network for fake news prediction
Authors: Borse Alpana A. , Kharate Gajanan K. , Kharate Namrata G.
Published: January 2025

Fake News presents significant threats to both society and individuals, highlighting the urgent need for improved news authenticity verification. To deal with this challenge, we provide a novel strategy called the 4-level hierarchical attention network (4HAN), designed to enhance fake news detection through an advanced integration of hypergraph convolution and attention neural network mechanisms. The 4HAN model operates across four hierarchical levels: paragraphs, sentences, words, and contextual information (metadata). At the highest level, the model employs hypergraph-based attention and convolution neural networks to create a contextual information vector, utilizing a SoftMax activation function. This vector is then combined with a news content vector generated through word and sentence-level attention mechanisms. This architecture enables the 4HAN model to effectively prioritize the relevance of specific words and contextual information, thereby improving the overall representation and accuracy of news content. We evaluate the 4HAN model using the LIAR dataset to demonstrate its efficacy in enhancing Fake News prediction accuracy. Comparative analysis shows that the 4HAN model outperforms several of cutting-edge techniques, like recurrent neural networks (RNN), ensemble techniques, and attention mechanisms techniques. Our results indicate 4HAN model accomplishes a notable accuracy of 96%, showcasing its potential for significantly advancing fake news prediction.
The role of disruptive technologies in the metaverse worlds: state of the art survey
Authors: Al-Karaki Jamal N. , Gawanmeh Amjad , Awad Ahmed , Zerai Teklesenbet Natnael
Published: January 2025

The metaverse has emerged as an immersive and interactive virtual world that has the potential to revolutionize various industries. The use of disruptive technologies, such as blockchain, artificial intelligence (AI), digital twin, internet of things (IoT), cloud, big data, and cybersecurity, has and will play a significant role in enhancing the capabilities of the metaverse. This paper provides a state-of-the-art survey on the role of disruptive technologies in the metaverse. The paper presents a taxonomy of the use of disruptive technologies in the metaverse and a comprehensive literature review on the application areas of the metaverse in education, healthcare, tourism, gaming, and smart cities. The paper compares the adoption of technologies in the metaverse and identifies current and future research directions. The paper contributes to understanding disruptive technologies’ potential in the metaverse. It provides insights for researchers, practitioners, and policymakers to explore the opportunities and challenges of the metaverse.
Towards a standardized enterprise architecture: enhancing decision-making in oncology multidisciplinary team meetings
Authors: Bout Nassim , Belhadaoui Hicham , Afifi Nadia , Abik Mounia , El-Hfid Mohamed , Azougaghe Ali
Published: January 2025

This study proposes a novel enterprise architecture (EA) designed to enhance the efficiency and decision-making processes of multidisciplinary team meetings (MDTMs) in oncology by integrating advanced artificial intelligence (AI) technologies. The architecture addresses current inefficiencies in MDTMs, particularly the lack of real-time data integration and limited decision support, by providing a structured framework that improves interoperability and standardizes clinical workflows. Developed using the open group architecture framework (TOGAF) framework and the ArchiMate modelling language, this conceptual architecture lays the groundwork for future empirical research, offering a scalable solution that can be adapted to various healthcare settings. The AI component, centered on generative pretrained transformer (GPT) models, is designed to support oncologists by providing evidence-based treatment recommendations tailored to individual patient cases. Although the study focusses on the theoretical development of this architecture, it opens the door for subsequent empirical testing and validation, with the aim of ultimately improving patient outcomes and streamlined oncology care through enhanced decision support systems.
An adaptive audio wave steganography using simulated annealing algorithm
Authors: Obeidat Atef Ahmed , Bawaneh Mohmmed Jazi , Shqair Sawsan Yousef Abu , Al-Omari Hamdi A. , Al-shalabi Emad Fawzi
Published: January 2025

The science of information security has increased in importance to encounter the espionage and information theft. This research proposes a new steganography framework that utilizes simulated annealing (SA) as an artificial intelligence algorithm to support the process of hiding a binary secret message file within an audio wave file. The best path for embedding the secret data inside the audio file is determined through SA that searches for the preferred path according to the content of the host audio file and secret message to be hidden. The least significant bit (LSB) technique was employed to hide message bytes, in which each audio-chosen byte will hold one bit from a secret message byte. The hiding process constructs the stego audio file and extraction key that will be required in an extraction process. The authorized user requires an extraction key and a decryption key to retrieve the hidden message. On the other hand, the attacker requires knowledge of the aforementioned keys and working algorithms that were employed in the hidden process. Robustness against data extraction, detection, imperceptibility (phonological hearing), security, peak signal to noise ratio (PSNR), mean square error (MSE) and capacity as security performance measures were used to evaluate the system. The maximum size of the data to be hidden may reach 12.5% of the data size of the host audio file, in which the average value of MSE and PSNR are (0.0041, 74.73), respectively.
Amharic event text classification from social media using hybrid deep learning
Authors: Ayalew Amogne Andualem , Tegegne Melaku Lake , Manivannan Bommy , Suresh Tamilarasi , Kumar Napa Komal , Prasad Battula Krishna , Assegie Tsehay Admassu , Salau Ayodeji Olalekan
Published: January 2025

This study aims to develop a hybrid deep-learning model for detecting and classifying Amharic text. Various natural language applications, such as information extraction, event extraction, conversation, text summarization, and require an automatic event classification. However, existing studies focused on classification, giving little attention to the preprocessing and feature extraction techniques. To address this problem, this work proposed a hybridized deep learning-based Amharic social media text event classification model. The model consists of word-to-vector (Word2vecv) word embedding techniques to capture the semantic and syntactic representation. Convolutional neural network (CNN) is used to extract short-length text features. Additionally, bidirectional long-short memory (Bi-LSTM) is used to extract features from long Amharic sentences and classify those events based on their classes. The dataset used for training and testing consists of 6,740 labeled Amharic text sentences, collected from social media. The result shows an accuracy of 94.8% in detecting and classifying Amharic text events.
Handwritten text recognition system using Raspberry Pi with OpenCV TensorFlow
Authors: Alsayaydeh Jamil Abedalrahim Jamil , Jie Tommy Lee Chuin , Bacarra Rex , Ogunshola Benny , Yaacob Noorayisahbe Mohd
Published: January 2025

Handwritten text recognition (HTR) technology has brought about a revolution in the way handwritten data is converted and analyzed. This proposed work focuses on developing a HTR system using deep learning through advanced deep learning architecture and techniques. The aim is to create a model for real-time analysis and detection of handwritten texts. The proposed deep learning architecture that is convolutional neural networks (CNNs), is investigated and implemented with tools like OpenCV and TensorFlow. The model is trained on large handwritten datasets to enhance recognition accuracy. The system’s performance is evaluated based on accuracy, precision, real-time capabilities, and potential for deployment on platforms like Raspberry Pi. The actual outcome is a robust HTR system that can convert handwritten text to digital formats accurately. The developed system has achieved a high accuracy rate of 91.58% in recognizing English alphabets and digits and outperformed other models with 81.77% mAP, 78.85% precision, 79.32% recall, 79.46% F1-Score, and 82.4% receiver operating characteristic (ROC). This research contributes to the advancement of HTR technology by enhancing its precision and utility.
Novel technique to deblurring and blur detection techniques for enhanced visual clarity of ancient images
Authors: Pawar Poonam , Ainapure Bharati
Published: January 2025

Digital image quality often degrades due to various factors such as noise and blur. Many images are affected by these issues, reducing their clarity and accuracy. This degradation is especially problematic for ancient images, significantly hampers the ability to analyze historical documents and artworks. This paper presents a novel approach to both blur detection and deblur ancient images, enhancing their clarity and readability. This research introduces a technique that combines wavelet transform and convolutional neural networks (CNNs) for effective blur identification and deblurring, specifically aimed at restoring blurred ancient images, regardless of the type of blur degradation. This novel approach demonstrated an average accuracy of 98.3% in blur detection on ancient image datasets. The performance of deblurring algorithms is typically evaluated using metrics such as peak signal-to-noise ratio (PSNR), mean squared error (MSE), and structural similarity index (SSIM) which quantify fidelity and quality of the deblurred images. In the deblurring, this approach produced PSNR values of 55.5 to 68.3 dB, MSE values of 2.99 to 11.1, and an SSIM of 0.9 across different types of blurs. These results show significant promise for the restoration of ancient images, providing researchers, historians, and archaeologists with valuable tool for conservation cultural heritage.
The advances in natural language processing technology and its impact on modern society
Authors: Borisova Nadezhda , Karashtranova Elena , Atanasova Irena
Published: January 2025

A wide range of information, such as books, news, reports, and other content, is constantly being produced, much of which is available online. Machine translation, spam detection, natural language interfaces, and question-answering applications have become increasingly popular. Natural language processing (NLP) is at the core of the automatic retrieval of information stored on computers. This article discusses NLP and its applications in daily activities. It covers the main stages of NLP and provides examples of its advances in various higher-level tasks. The objective is to highlight the significance of NLP in processing online content and in efficient interactions between humans and computers across various applications. As an essential element of artificial intelligence, NLP provides solutions for real-world problems and has the potential to transform the way companies operate.
Plant disease detection using vision transformers
Authors: Ali Mhaned , Salma Mouatassim , Haji Mounia El , Jamal Benhra
Published: January 2025

Plant diseases present a major risk to worldwide food security and the sustainability of agriculture, leading to substantial economic losses and hindering rural livelihoods. Conventional methods for disease detection, including visual inspection and laboratory-based techniques, are limited in their scalability, efficiency, and accuracy. This paper addresses the critical problem of accurately detecting and diagnosing plant diseases using advanced machine learning techniques, specifically vision transformers (ViTs), to overcome these limitations. ViTs leverage self-attention mechanisms to capture intricate patterns in plant images, enabling accurate and efficient disease classification. This paper reviews the literature on deep learning techniques in agriculture, emphasizing the growing interest in ViTs for plant disease detection. Additionally, it presents a comprehensive methodology for training and evaluating ViT models for plant disease classification tasks. Experimental results demonstrate the effectiveness of ViTs in accurately identifying various plant diseases across a balanced 55 classes dataset, highlighting their potential to revolutionize precision agriculture and promote sustainable farming practices.
Architecture of multi-agent systems for generative automatic matching among heterogeneous systems
Authors: Batouta Zouhair Ibn , Dehbi Rachid , Talea Mohamed
Published: January 2025

This paper presents the generative automatic matching (GAM) approach, implemented through a multi-agent system (MAS), to address the challenges of heterogeneity across meta-models. GAM integrates automatic meta-model matching with model generation, offering a comprehensive solution to complex systems involving diverse architectures. The key innovation lies in its ability to automate both the detection of correspondences and the transformation of models, improving the precision and recall of matching processes. The system's scalability and adaptability are enhanced by MAS, allowing for efficient management of diverse meta-models. The approach was evaluated through relational to big data UML meta-models (RBDU) case study. The results demonstrated high accuracy, with precision and recall metrics approaching 1, underscoring the robustness of GAM in managing heterogeneous systems. Compared to traditional methods, GAM offers significant advantages, including automated matching and generation, adaptability to various domains, and superior performance metrics. The study contributes to the field of model-driven engineering (MDE) by formalizing a method that effectively bridges the gap between heterogeneous meta-models. Future research will focus on refining matching heuristics, expanding case studies.
Enhancing accuracy in greenhouse microclimate forecasting through a hybrid long short-term memory light gradient boosting machine ensemble approach
Authors: Abdelmadjid Mokeddem Kamal , Noureddine Seddiki , Amina Bourouis , Khelifa Benahmed
Published: January 2025

Greenhouse cultivation is one of the main methods for improving agricultural yield and quality. With the world needing more and more production, improving greenhouses using innovative technology becomes a must. These high-tech, aka, smart greenhouses depend much on the accuracy and availability of sensor data to perform at their best. In challenging situations such as sensor malfunctions or data gaps, utilizing historical data to predict microclimate parameters within the greenhouse is essential for maintaining optimal growing conditions and effective sustainable resource management control. In this work, and by employing a synthesis technique across various time series models, we forecast internal temperature and humidity, the two main parameters for a greenhouse, by incorporating diverse characteristics as input into a customized forecasting model. The selected architecture integrates deep learning and nonlinear learning models, specifically long short-term memory (LSTM) and light gradient boosting machine (LightGBM) as an ensemble approach, providing a comprehensive framework for time-series prediction, evaluated through mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R²) metrics. With a focus on improving accuracy in anticipating environmental changes, we have achieved high precision in predicting temperature (98.45%) and humidity (99.61%).
Optimizing convolutional neural networks-based ensemble learning for effective herbal leaf disease detection
Authors: Ginantra Ni Luh Wiwik Sri Rahayu , Yanti Christina Purnama , Ariantini Made Suci
Published: January 2025

This study aims to optimize convolutional neural networks (CNN)-based ensemble learning models to enhance accuracy and stability in detecting herbal leaf diseases. The dataset used in this study is sourced from the “Lontar Taru Pramana” collection, which includes various images of herbal leaves affected by different diseases such as Ancak Bacterial Spot, Dapdap Mosaic Virus, and Kelor Powdery Mildew. Several CNN models, including VGG16, AlexNet, ResNet50, DenseNet121, MobileNetV2, and InceptionV2, were evaluated. Among these, the ensemble models combining VGG16, DenseNet121, and MobileNetV2 were selected due to their superior performance. The ensemble model achieved precision scores of 0.81 for class 1, 0.76 for class 2, and 0.78 for class 3, with corresponding recall scores of 0.8167, 0.74, and 0.7633, and F1-scores of 0.8133, 0.75, and 0.7717 respectively. These results indicate significant improvements in model performance and robustness.
Electrical power submeter for quality and energy monitoring using Wemos microcontroller
Authors: Supriyono Heru , Hogantara Azra Reza Satria , Budiman Aris
Published: January 2025

Voltage, current, and frequency are three electrical energy variables that need to be monitored because if they do not comply with established standards, they can cause damage to electronic devices that use electrical energy. The objective of this article is to develop a submeter that can be used for monitoring both energy consumption and three power quality variables. The system was developed by using commercially available instruments involving the PZEM 004t sensor, the Wemos D1 mini microcontroller, and the Blynk platform on the smartphone. The use of the Blynk platform enables the system to log the monitored variables continuously in the form of a spreadsheet file and send them via email in order to be downloaded and used for further analysis. The results of calibration tests carried out using varying loads showed that the developed system has voltage measurement results with a difference of 1.35% when compared to measurement results using a commercial multimeter, while the difference for current measurements is 0.85%.
A secure and cloud-based patient management system using attribute-based encryption algorithm
Authors: Kalarani Senthilkumar , Shobana Mahalingam , Shankari Edamakanti Uma , Praveena Bolly Joshi , Shanthi Subramaniam , Ramadevi Rathinasabapathy , Sandiri Rajendar
Published: January 2025

Using attribute-based encryption (ABE), cloud-based patient management systems may be made more secure and efficient. The goal is to provide a scalable encryption infrastructure with dynamic attribute handling and context-aware access control for safe data access. Encryption procedures should directly comply with regulatory criteria to secure healthcare data and ensure data privacy and integrity. Secure attribute issuance and revocation are achieved using advanced key management and real-time auditing and monitoring to identify and react to unauthorised access. To help healthcare providers handle data, user-centric security measures including extensive training and adaptive security procedures are used. The encryption system is implemented and maintained using cost-effective cloud and open-source methodologies to ensure seamless integration and operational effectiveness in healthcare contexts. First, secure patient management system dataset results reveal ABE algorithm encryption. The encrypted values are 8F5D6A..., 7C4A3B..., 6E3B2C..., 9D8A7B..., 5E4D3C.... in the second instance, derived from role-based access control of ABE. The patients are 25-60 years old, have medical codes 101-105, 201-205, and 301-305. For roles from different fields, attribute code is 401-406, level code is 501-505.
An improved key scheduling for advanced encryption standard with expanded round constants and non-linear property of cubic polynomials
Authors: Ganesan Muthu Meenakshi , Selvaraj Sabeen
Published: January 2025

The advanced encryption standard (AES) offers strong symmetric key encryption, ensuring data security in cloud computing environments during transmission and storage. However, its key scheduling algorithm is known to have flaws, including vulnerabilities to related-key attacks, inadequate nonlinearity, less complicated key expansion, and possible side-channel attack susceptibilities. This study aims to strengthen the independence among round keys generated by the key expansion process of AES—that is, the value of one round key does not reveal anything about the value of another round key—by improving the key scheduling process. Data sets of random, low, and high-density initial secret keys were used to evaluate the strength of the improved key scheduling algorithm through the National Institute of Standards and Technology (NIST) frequency test, the avalanche effect, and the Hamming distance between two consecutive round keys. A related-key analysis was performed to assess the robustness of the proposed key scheduling algorithm, revealing improved resistance to key-related cryptanalysis.
Intrusion detection based on generative adversarial network with random forest for cloud networks
Authors: Rosline Gnanam Jeba , Rani Pushpa
Published: January 2025

The development of cloud computing enables individuals and organizations to access a wide range of online programs and services. Because of its nature, numerous users can access and distribute cloud infrastructure. In cloud computing several security threats change the data and operations. A network's ability to detect malicious activity and possible threats is greatly aided by intrusion detection. To solve these issues, intrusion detection based on generative adversarial network with random forest (GAN-RF) for cloud networks is introduced. The function of the generative adversarial networks (GANs) based network abnormality recognition system is evaluated. It uses the CICIDS2018 dataset to detect intrusion. GAN is utilized to improve network anomaly detection in conjunction with an ensemble random forest (RF) classifier. The GAN-RF model achieved 95.01% of accuracy for intrusion detection and obtain better recall and F1-score. Extensive assessments and valuations illustrate the efficiency of the GAN-RF approach in accurately identifying network issues.
Design and implementation of an internet of things enabled stress level detection system using fuzzy logic method for enhanced accuracy and real-time monitoring
Authors: Nurhayati Nurhayati , Hidayah Nur , Ahyar Muh. , Asriyadi Asriyadi , Yuniarti Yuniarti , Faraby Muhira Dzar , Mustika Mustika , Akhriana Asmah , Mukhlisin Mukhlisin
Published: January 2025

Stress can affect individuals of all ages, from the young to the elderly, leading to a compromised immune system and increased susceptibility to illness. This study addresses this issue by developing an internet of things (IoT) based stress level detection system utilizing fuzzy logic methods. The device measures multiple physiological parameters and processes the data using an ESP32 microcontroller. This allows individuals to monitor and understand their stress levels efficiently and automatically through a liquid crystal display (LCD) display and Android devices. The system integrates various sensors to capture vital signs such as heart rate (HR), respiration rate, and body temperature. These readings are then analyzed using its algorithms to determine the stress level, which is displayed on both the onboard LCD and the connected Android device via an IoT interface. This real-time feedback mechanism empowers users to take proactive measures in stress management. Testing and validation of the device were conducted by comparing its readings with the depression anxiety stress scales (DASS-42) test results. The comparison showed an 80% correlation, demonstrating the device’s accuracy and reliability in detecting stress levels. This innovative approach leverages the advantages of IoT and fuzzy logic to provide a practical and effective solution for stress monitoring and management.
Efficient power optimized very-large-scale integration architecture of proportionate least mean square adaptive filter
Authors: Lakshmaiah Gangadharaiah Soralamavu , Krishnappa Narayanappa Chikkajala , Ramappa Poornima Golluchinnappanahalli , Narasimhaiah Divya Muddenahally , Radder Umesharaddy , Chandrasekhar Chakali
Published: January 2025

The focus on power optimization in embedded systems is especially important for embedded applications since it has brought in many methods and factors that are necessary for developing systems that are both power- and area-efficient. In contrast to the current delayed wavelet μ-law proportionate least mean square (DWMPLMS) and delayed least mean square (DLMS) algorithms, this work offers the development of adaptive filters based on the least mean square (LMS) method, which improves power and timing performance. In order to improve area and time efficiency, the proportionate least mean square (PLMS) algorithm's architecture has been modified to remove delay, add a proportionate gain block, design for a fixed length, include an approximate multiplier block, and swap out standard blocks for floating-point adder and divider blocks. According to a power and temporal comparison with the DWMPLMS and DLMS algorithms, field-programmable gate array (FPGA) synthesis reduces power usage by 95% for a 32-bit filter length in PLMS when compared to the above methods.
A review of convolutional neural networks for classifying power quality problems using Keras API
Authors: Sa'idu Adamu , Kadandani Nasiru B. , Bukata Bala Boyi
Published: January 2025

The major causes of electric power quality (PQ) problems are mainly due to the increased utilization of nonlinear loads, capacitor and load switching events, transformer energization, and occurrence of assorted faults at the distribution corridor. The problems often introduce harmonics and other waveform anomalies like voltage sags, voltage swells and interruptions along the power systems. A timely classification of such problems is important in understanding their impact on costly power system economy. The paper explores comprehensive review of PQ issues, operational concept of convolutional neural network (CNN) and its utilization in solving PQ problems. Novel deep learning (DL) approach using variant of DenseNet CNN technique in Keras API platform is deployed to extract the features of, and classify PQ problems. The proposed technique improves classification performance with an accuracy of 99.96%. It shows remarkable improvement over the traditional techniques in the literature which were 73.53% to 99.92% accurate for a period from 2018 to 2023. The most promising part of the method is the improvement shown in the classification performance when compared with that obtained in the literature. The technique can also be applied in real time to cater for real PQ problems.
Designing stair climbing wheelchairs with surface prediction using theoretical analysis and machine learning
Authors: Chawaphan Pharan , Maneetham Dechrit , Crisnapati Padma Nyoman
Published: January 2025

Urban settings present considerable obstacles for those use personal mobility wheelchairs, especially when it comes to manoeuvring stairs. The objective of this study is to improve the safety and ease of use of wheelchairs designed for ascending stairs. The study aims to tackle the significant issue of instability and limited ability to adjust to different types of terrain. This research employs a holistic methodology that combines theoretical dynamic analysis, hardware design and simulation, and field testing, in addition to advanced machine learning approaches for surface prediction. Theoretical models guarantee the stability of the wheelchair, while hardware simulations offer valuable insights into its structural integrity. The data obtained from inertial measurement unit (IMU) sensors during field tests is analysed and categorised using models like random forest and gradient boosting, which exhibit exceptional accuracy in forecasting movement circumstances. The results demonstrate that the implementation of these combined techniques greatly enhances the wheelchair’s capacity to safely manoeuvre over urban barriers. The study finds that the suggested solutions show great potential for creating intelligent mobility aids, which might be used to improve accessibility for those with mobility limitations.
SMOTE tree-based autoencoder multi-stage detection for man-in-the-middle in SCADA
Authors: Rolansa Freska , Istiyanto Jazi Eko , Afiahayati Afiahayati , Kusuma Frisky Aufaclav Zatu
Published: January 2025

Security incidents targeting supervisory control and data acquisition (SCADA) infrastructure are increasing, which can lead to disasters such as pipeline fires or even lost of lives. Man-in-the-middle (MITM) attacks represent a significant threat to the security and reliability of SCADA. Detecting MITM attacks on the Modbus SCADA networks is the objective of this work. In addition, this work introduces SMOTE tree-based autoencoder multi-stage detection (STAM) using the Electra dataset. This work proposes a four-stage approach involving data preprocessing, data balancing, an autoencoder, and tree classification for anomaly detection and multi-class classification. In terms of attack identification, the proposed model performs with highest precision, detection rate/recall, and F1 score. In particular, the model achieves an F1 score of 100% for anomaly detection and an F1 score of 99.37% for multi-class classification, which is preeminence to other models. Moreover, the enhanced performance of multi-class classification with STAM on minority attack classes (replay and read) has shown similar characteristics in features and a reduced number of misclassifications in these classes.
Video mosaic: employing an efficient ORB feature extraction technique with hamming distance matching for enhanced performance
Authors: H Shridhar , Harakannanavar Sunil S. , Kanabur Vidyashree , H Jayalaxmi
Published: January 2025

Video mosaicing is a computer vision and image processing technique used to create a panoramic or wide-angle view from a sequence of video frames. The goal is to seamlessly combine multiple video frames to form a larger and more comprehensive view of a scene. In recent years, the field of image processing has witnessed a growing interest in video mosaic research owing to its application in surveillance and defense applications. This paper introduces an automatic algorithm for video mosaic creation, addressing the alignment and blending of non-overlapping frames within each input video. The proposed algorithm navigates through several key steps to achieve a seamless and continuous mosaic, particularly tackling issues related to camera motion and content variations across frames. The effect of the good number of matches to be chosen while performing frame stitching is evaluated. The proposed algorithm effectively produces a video mosaic with aligned and blended non-overlapping frames, resulting in a visually continuous mosaic. The output video serves as a testament to the algorithm’s prowess in addressing challenges related to video frame alignment and blending.
Enhancing BEMD decomposition using adaptive support size for CSRBF functions
Authors: Arrazaki Mohammed , El Ouahabi Othman , Zohry Mohamed , Babbah Adel
Published: January 2025

Despite their widespread development, the Fourier transform and wavelet transform are still unsuitable for analyzing non-stationary and non-linear signals. To address this limitation, bidimensional empirical mode decomposition (BEMD) has emerged as a promising technique. BEMD effectively extracts structures at various scales and frequencies but faces significant computational complexity, primarily during the extremum interpolation phase. To mitigate this, different interpolation functions were presented and suggested, with BEMD using compactly supported radial basis functions (BEMD-CSRBF) showing promising results in reducing computational cost while maintaining decomposition quality. However, the choice of support size for CSRBF functions significantly impacts the quality of BEMD. This article presents an enhancement to the BEMD-CSRBF algorithm by adjusting the CSRBF support size based on the extrema distribution of the image. Our method’s results show a significant improvement in the BEMD-CSRBF algorithm’s quality. Furthermore, when compared to the other two approaches to BEMD, it shows higher accuracy in terms of both intrinsic mode function (IMF) quality and computational efficiency.
Machine learning based stator-winding fault severity detection in induction motors
Authors: Mishra Partha , Sarkar Shubhasish , Chowdhury Sandip Saha , Das Santanu
Published: January 2025

Approximately 35% of all induction motor defects are caused by stator inter-turn faults. In this paper a novel algorithm has been proposed to analyze the three-phase stator current signals captured from the motor while it is in operation. The suggested method seeks to identify stator inter-turn short circuit faults in early stage and take the appropriate action to prevent the motor's condition from getting worse. Three-phase current signals have been captured under healthy and faulty conditions of the motor. Involving discrete wavelet transform (DWT) based decomposition followed by reconstruction using inverse DWT (IDWT), 50 Hz fundamental component has been removed from the captured raw current signals. Subsequently, from each phase current 15 statistical parameters have been retrieved. The statistical parameters include mean, standard deviation, skewness, kurtosis, peak-to-peak, root mean square (RMS), energy, crest factor, form factor, impulse factor, and margin factor. At the end, a standard machine learning algorithm namely error correcting output codes-support vector machine (ECOC-SVM) has been employed to classify six different severity of stator winding faults. The proposed fault diagnosis method is load and motor-rating independent.