bluuebunny/crossref_metadata_2025 huggingface dataset:
Performance enhancement of a terahertz patch antenna with metamaterials for 6G and biomedical applications
Authors: Younes Siraj , Alaoui Kaoutar Saidi , Foshi Jaouad
Published: January 2025

This paper introduces a novel approach for enhancing the performance of a terahertz (THz) patch antenna through the integration of metamaterials (MTM). The proposed design features a rectangular slotted patch antenna with a partial ground structure (DGS) that operates at 3.56 THz. The radiating element is situated on a substrate composed of silicon dioxide (SiO2) with a dielectric of 4 and a thickness of 2 µm. The proposed MTM is a 6×5 elements with a FR4 substrate characterized by a dielectric of 4.2 and a thickness of 2 µm. The MTM is integrated beneath the antenna as a strategic technique to enhance its performance. The results confirm the significant impact of this integration. The MTM improves impedance matching and makes the antenna more directional. Consequently, the reflection coefficient is improved from -18.06 dB to -52.50 dB, the gain is increased from 1.72 dB to 3.49 dB, and the directivity also is enhanced from 3.69 dB to 5.10 dB. All results were obtained using HFSS software.
Engraved hexagonal metamaterials resonators antenna for bio-implantable ISM-band applic
Authors: Safaa Belkheir , Naima Sabri Ghoutia
Published: January 2025

This study will introduce a metamaterial antenna designing for use in biomedical implants. The antenna is compact and utilizes four slot complementary metamaterial hexagonal resonators of uniform shape and size. By incorporating the metamaterial into the antenna design, its size is reduced while the performance is enhanced. Simulation results show that the antenna achieves satisfactory peak gain values of -22.6 dBi and a 34.5% increase in bandwidth. Operating within the 2.4-2.5 GHz industrial, scientific, and medical (ISM) frequency bands, the antenna measures 7×7×1.27 mm3 and consists of substrate layers with patch radiation, four metamaterials hexagonal resonators on the upper surface, a ground layer, and a second superstrate layer. The study also addresses the challenges and problems associated with the interaction between the antenna and human tissue, while aiming to maintain antenna performance, properties, and minimize its impact on tissues. Evaluation of when using a 2.45 GHz operating frequency, the specific absorption rate (SAR) shows values of 489.87 W/kg for 1 g of averaged tissue and 53.738 W/kg for 10 g of averaged tissue. The results of placing the antenna in human skin tissue are safe for use in the human body and appropriate for biomedical applications. Simulations conducted using computer simulation technology (CST) and high frequency structure simulator (HFSS) software emphasize the excellent performance of the engraved metamaterial antenna.
Flexible hybrid graphene-based NFC tag antenna for temperature monitoring application
Authors: Mohd Faudzi Najwa , Razali Ahmad Rashidy , Abd Manaf Asrulnizam , Abd Rahman Nurul Huda , Ab Aziz Ahmad Azlan , Hafiz Syed Muhammad , Sulaiman Suraya , Rashid Nora’zah Abdul , Ibrahim Amirudin , Mozi Aiza Mahyuni
Published: January 2025

A hybrid graphene-based material, composed of reduced graphene oxide (rGO) and silver nanoparticle (AgNP), has been proposed for a near field communication (NFC) tag antenna with an integrated, flexible temperature monitoring circuit. The limited availability of high-conductivity graphene-based materials in the market has restricted the use of graphene in NFC tag applications. Therefore, this paper proposes a hybrid graphene-based composition featuring a high conductivity of 3.95×106 S/m. The feasibility of this material for NFC tags had not been validated previously, which is the main motivation for this research. The synthesis of the materials, along with the design, fabrication, and characterization of the NFC tag, is also presented. Results show that the inkjet-printed tag achieves a good reading range of up to 3 cm and demonstrates robustness against bending from 60⁰ to 190⁰, maintaining a maximum reading range of 1.3 cm. Performance on various materials, such as plastic, paper, and carton, also shows minimal impact on frequency shifting. Additionally, the graphene-based NFC tag integrates well with the temperature circuit, effectively monitoring temperatures in the 20-60 ⁰C range in real-time. This makes the developed tag suitable for applications such as food safety monitoring systems through NFC-integrated packaging.
A multi-scale convolutional neural network and discrete wavelet transform based retinal image compression
Authors: Chikhaoui Dalila , Beladgham Mohammed , Benaissa Mohamed , Taleb-Ahmed Abdelmalik
Published: January 2025

The different applications of medical images have contributed significantly to the growing amount of image data. As a result, compression techniques become essential to allow real-time transmission and storage within limited network bandwidth and storage space. Deep learning, particularly convolutional neural networks (CNN) have marked rapid advances in many computer vision tasks and have progressively drawn attention for being used in image compression. Therefore, we present a method for compressing retinal images based on deep CNN and discrete wavelet transform (DWT). To further enhance CNN capabilities, multi-scale convolutions are introduced into the network architecture. In this proposed method, multiscale CNNs are used to extract useful features to provide a compact representation at the encoding stage and guarantee a better reconstruction quality of the image at the decoding stage. Based on compression efficiency and reconstructed image quality, a wide range of experiments have been conducted to validate the proposed technique performance compared with popular image compression standards and existing deep learning-based methods. At a compression ratio (CR) of 80, the proposed method achieved an average peak signal-to-noise ratio (PSNR) value of 38.98 dB and 96.8% similarity in terms of multi-scale structural similarity (MS-SSIM), demonstrating its effectiveness.
Link adaptation techniques for throughput enhancement in LEO satellites: a survey
Authors: Idmouida Habib , Minaoui Khalid Minaoui
Published: January 2025

In addition to the rapid geometric change of low earth orbit (LEO) satellites, the earth-to-space channel suffers from various attenuations that affect the communication link. To overcome this challenge, the link adaptation technique emerges as a key solution to optimize the transmission performance of LEO satellites, especially the data throughput. The existing contributions in the literature remain scattered across the research board, and a comprehensive survey of this research area still lacks at this stage. The present survey examines various link adaptation methods, mainly variable coding and modulation, adaptive coding and modulation, and hybrid methods using artificial intelligence. In addition, this study explains how this technique leverages a set of recommended standards and cost-effective technologies, such as software-defined radio (SDR) and field programmable gate arrays (FPGA), to fine-tune transmission strategies. Lastly, the paper provides a comparative study of the current research on this field and sheds light on future directions, where the need for higher data throughput makes emerging learning-based techniques and new experimental standards a necessity.
TALOS: optimization of the CNN for the detection of the tomato leaf diseases
Authors: Subramanya Shruthi Kikkeri , Bettahalli Naveen , Bhoganna Naveen Kalenahalli
Published: January 2025

Early detection of plant diseases using convolutional neural network (CNN)is crucial for maximizing crop yield and minimizing economic losses. Manual inspection, the frequent technique, is inefficient and error prone. While CNN’s offer potential for accurate and quick disease recognition, their performance is highly dependent on effective hyperparameter tuning. This process is time consuming, resource intensive, and needs significant expertise due to the vast hyperparameter space, since it can be hard to figure out which is ideal for optimal performance. An effective optimization tool, tunable automated hyperparameter learning optimization system (TALOS), is proposed, which automates the tuning of hyperparameters by systematically exploring the hyperparameter space and evaluates different combinations of parameters to find the optimal configuration that maximize the model’s performance. The performance of this approach is recognizable through its exploration of five different hyperparameters across a search space of 32 combinations, yielding optimal parameters by the second round. Using 3030 tomato leaf images from a benchmark data set, the model achieves a remarkable 94.7% validation accuracy with 33647 trainable parameters. Thus, automated hyperparameter tuning approach not only optimizes model performance but also reduces manual effort and resource requirements, paving the way for more effective and scalable solutions in agricultural technology.
Improvement the cogging torque reduction methods by combining the magnet slotted and gradually inclined surface end in permanent magnet generator
Authors: Abduh Syamsir , Fikri Miftahul , Nur Tajuddin
Published: January 2025

Cogging torque (CT) in permanent magnet synchronous (PMS) machine, generator or electric motor should be reduced to increase the preperformance in application. Many CT reduction techniques has been proposed in the last few years. This research dealt with the study of techniques for reduction of the CT in PMSG. The PMS generator investigated in this paper is the integral slot number type with 18 slots and 6 poles. The CT has been analyzed to be reduced by employ the slot opening width variation, magnet edges slotting, and gradually inclined surface end. This paper also has analyzed the effect of combination of slot opening width and slotting permanent magnets. The finite element method magnetics (FEMM) is used in this work to perform electromagnetic simulations of the PMSG. Using the FEMM, the CT reduction of permanent magnet synchronous generators studied is analyzed and the CT peak value is compared. It is found that by combining of reduced of slot opening and slotting the permanent magnets can reduce the CT of PMS generator significantly abound 98.55% compared with the base line model.
Advancing supply chain management through artificial intelligence: a systematic literature review
Authors: Younesse Ouahbi , Soumia Ziti , Souad Lagmiri Najoua
Published: January 2025

This study evaluates the role and impact of artificial intelligence (AI) in supply chain management (SCM). Following a five-step process, the review covered academic publications from 2000 to 2024, drawing from different databases. The review identified 426 relevant articles for analysis, focusing on AI techniques. The analysis explored their applications, advantages, and barriers to adoption in SCM. The study also discussed key challenges, including financial, organizational, strategic, technological, and legal barriers. The findings suggest that while AI techniques offer significant potential for improving SCM, several obstacles hinder their broader implementation. Addressing these obstacles requires investments in infrastructure, skills development, and effective change management.
Machine learning-based classification of corn seed viability using electrical impedance spectroscopy
Authors: Perocho Perrie Lance , Concepcion II Ronnie
Published: January 2025

Corn (Zea mays L.), an essential global commodity, plays an ever-increasing role in agri-food systems. To support growing demand, rapid and noninvasive methods for determining seed germination rates are crucial alongside invasive techniques such as dissection, germination paper tests, and chemical assays. This study introduces electrical impedance spectroscopy (EIS) as a novel, non-invasive approach for classifying viable and non-viable corn seeds. Non-viable corn seeds were prepared by exposing them to 100 °C convection heat for 30 minutes. Impedance spectra were measured using the EVAL-AD5933EBZ evaluation board from 400 kHz to 1 MHz frequency range within 30 seconds. Furthermore, a comparison of six optimized supervised machine learning (ML) algorithms, including shallow and deep networks, was performed, setting this apart from other studies. The trained model was deployed to assess the viability of new seed samples effectively. Key impedance metrics, including their frequencies, were extracted to train and test the algorithms. The regression tree (RTree) model outperformed deep learning classifiers, achieving 95% accuracy, 90% precision, and 100% sensitivity. The results indicated an upward trend in viable seed impedance, increasing by 0.000164 Ω/Hz, peaking at 990 kHz. This approach offers a rapid, non-invasive solution for seed viability assessment, with significant potential to enhance agricultural productivity.
A comprehensive overview of LLM-based approaches for machine translation
Authors: Kumar Bhuvaneswari , Murugesan Varalakshmi
Published: January 2025

Statistical machine translation (SMT) used parallel corpora and statistical models, to identify translation patterns and probabilities. Although this method had advantages, it had trouble with idiomatic expressions, context-specific subtleties, and intricate linguistic structures. The subsequent introduction of deep neural networks such as recurrent neural networks (RNNs), long short-term memory (LSTMs), transformers with attention mechanisms, and the emergence of large language model (LLM) frameworks has marked a paradigm shift in machine translation in recent years and has entirely replaced the traditional statistical approaches. The LLMs are able to capture complex language patterns, semantics, and context because they have been trained on enormous volumes of text data. Our study summarizes the most significant contributions in the literature related to LLM prompting, fine-tuning, retrieval augmented generation, improved transformer variants for faster translation, multilingual LLMs, and quality estimation with LLMs. This new research direction guides the development of more efficient and innovative solutions to address the current challenges of LLMs, including hallucinations, translation bias, information leakage, and inaccuracy due to language inconsistencies.
Virtual exhibition systems using virtual reality technology
Authors: Winarno P. M. , Istiono Wirawan , Abhinaya Rajendra
Published: January 2025

Exhibitions are an activity that can bring a lot of benefits to a company. By participating in an exhibition, a company can carry out promotions to increase their sales and improve their company image. However, there are several shortcomings that can be found with conventional exhibitions held in a face-to-face manner. These exhibitions cost a lot of money, run for only a relatively short period of time, and are limited by the location of the exhibition. Because of this, the idea came up to create a virtual exhibition system which could be used as an alternative to conventional exhibitions. The development of a virtual exhibition system for this research was carried out using the Unity game engine. At the virtual exhibition, users can choose which exhibition they want to visit and enter the exhibition room view products and find information about them. Evaluation is carried out using a user acceptance test with Likert scale questions. The evaluation results show a user satisfaction level of 92.7% among the 18 users who have tested the application. With this, it can be said that the virtual exhibition system based on virtual reality technology has been successfully built.
IDCCD: evaluation of deep learning for early detection caries based on ICDAS
Authors: Noer Fadilah Rina Putri , Rikmasari Rasmi , Akbar Saiful , Setiawan Arlette Suzy
Published: January 2025

Dental caries is a common oral disease in children, influenced by environmental, psychological, behavioral, and biological factors. The American academy of pediatric dentistry recommends screening from the time the first tooth erupts or at one year of age to prevent caries, which mostly affects children from racial and ethnic minorities. In Indonesia, the 2023 health survey reported a caries prevalence of 84.8% in children aged 5-9 years. This research introduces early caries detection using three deep learning models: faster-RCNN, you only look once (YOLO) V8, and detection transformer (DETR), using Indonesian dental caries characteristic datasets (IDCCD) focused on Indonesian data with international caries detection and assessment system (ICDAS) classification D0 to D6. The results showed that YOLO V8-s and DETR gave good results, with mean average precision (mAP) of 41.8% and 41.3% for intersection over union (IoU) 50, and 24.3% and 26.2% for IoU 50:90. Precision-recall (PR) curves show that both models have high precision at low recall (0 to 0.2), but precision decreases sharply as recall increases. YOLO V8-s showed a slower and more regular decrease in precision, indicating a more stable performance compared to DETR.
G2M weighting: a new approach based on multi-objective assessment data (case study of MOORA method in determining supplier performance evaluation)
Authors: Hendrastuty Nirwana , Setiawansyah Setiawansyah , An’ars M. Ghufroni , Rahmadianti Fitrah Amalia , Saputra Very Hendra , Rahman Miftahur
Published: January 2025

Criteria weighting methods in decision support system (DSS) face various challenges and limitations that can affect their accuracy and reliability. One of the main challenges is subjectivity, this subjective assessment can reduce the objectivity and consistency of results. The main objective of the new weighting method grey geometric mean (G2M) weighting is to provide more objective and robust criteria weights under conditions of uncertainty and incomplete data. The new G2M weighting approach has a significant potential impact on the DSS field, it has the potential to generate more effective and efficient decisions, which can improve organizational performance, reduce risk and optimize outcomes. Pearson correlation test results of two sets of rankings generated by DSS methods namely grey relational analysis (GRA), simple additive weighting (SAW), multi-attributive ideal-real comparative analysis (MAIRCA), weighted product (WP), combined compromise solution (COCOSO), vlsekriterijumska optimizacija i kompromisno resenje (VIKOR), and a new additive ratio assessment (ARAS) that there is a strong positive correlation between the two methods using G2M weighting criteria. The high correlation value indicates that the rankings of the methods used tend to move together, giving confidence in the consistency and validity of the resulting ranking results. This gives confidence that both methods can be used simultaneously or interchangeably with consistent results. The use of G2M weighting in the DSS method used can support better decision-making by providing consistent information and validity of ranking results.
An enhanced least recently used page replacement algorithm
Authors: Tareef Afaf , Al-Tarawneh Khawla , Alhuniti Omar
Published: January 2025

Page replacement algorithms play a crucial role in enhancing the performance issue brought on by variations in processor speeds and memory by effectively removing pages from computer memory to improve overall efficiency. The majority of these algorithms can address the page replacement problems, but their implementation is challenging. This paper introduces a new efficient page replacement algorithm, i.e., enhanced least-replacement (E-LRU) based on two introduced features used to select the victim page. By incorporating elements of traditional algorithms such as first in first out (FIFO) and least recently used (LRU), E-LRU presents itself as a new approach with potential benefits for memory management. This study evaluates the effectiveness of E-LRU in reducing power consumption by reducing cache faults and compares its performance to existing algorithms in various settings. The results provide insight into the advantages and disadvantages of E-LRU and essential perspectives on its potential benefits for contemporary memory management algorithms. Furthermore, the study puts E-LRU into the perspective of evolving algorithms and provides directions for future investigation and improvement in the ever-changing field of memory management. The study proved that E-LRU works better than FIFO and LRU algorithms.
Multimodal perception for enhancing human computer interaction through real-world affect recognition
Authors: Raut Karishma , Kulkarni Sujata , Sawant Ashwini
Published: January 2025

Human-Computer Interaction can benefit from real-world affect recognition in applications like healthcare and assistive robotics. Human express emotions through various modalities, with audio-visual being the most significant. Using a unimodal approach, such as only speech or visual, is challenging in natural, dynamic environments. The proposed methodology integrated a pretrained model with a convolution neural network (CNN) to provide a robust initialization point and address the limited availability of facial expression data. The multimodal framework enhances discriminative power by combining visual scores with speech. This work addresses the challenges at each stage of the real-world affect recognition framework, including data preprocessing, feature extraction, feature fusion, and final classification. A 1D-CNN is employed for training on spectral and prosodic audio features, while deep visual features are processed using a 2D-CNN. The proposed system's performance was evaluated on the extended Cohn-Kanade (CK+), acted-facial-expressions in-the-wild (AFEW), and real-world-affective-face-database (RAF) datasets, which are commonly used in face recognition research. Experimental results indicate that 2% to 5% of visual data from natural settings were undetected, and the inclusion of the audio modality improved performance by providing relevant and supplementary information.
Recognition of plant leaf diseases based on deep learning and the chemical reaction optimization algorithm
Authors: Ba Nghien Nguyen , Thi Nhung Nguyen , Quoc Dung Vuong , Cong Cuong Nguyen
Published: January 2025

Agriculture plays a crucial role in developing countries such as Vietnam, where 70 percent of the population is employed in agriculture, and 57 percent of the social labor force works in the agricultural sector. Therefore, crop productivity directly affects the lives of many people. One of the primary reasons for reduced crop yields is plant leaf diseases caused by bacteria, fungi, and viruses. Hence, there is a need for a method to help farmers identify leaf diseases early to take appropriate action to protect crops and shift to smart agricultural production. This paper proposes lightweight deep learning (DL) models combined with a support vector machine (SVM), with hyperparameters fine-tuned by chemical reaction optimization (CRO), for detecting plant leaf diseases. The main advantage of the method is the simplicity of the architecture and optimization of the DL model’s hyperparameters, making it easily deployable on low hardware devices. To test the performance of the proposed method, experiments are performed on the PlantVillage dataset using Python. The superiority of the proposed method over the well-known visual geometry group-16 (VGG-16) and MobileNetV2 models is demonstrated by a 10% increase in accuracy prediction and a decrease of 5% and 66% in training time, respectively.
Fake review detection using enhanced ensemble support vector machine system on e-commerce platform
Authors: Joseph Seenia , Hemalatha S.
Published: January 2025

Due to the quick growth of online marketing transactions, including buying and selling, fake reviews are created to promote the product market and mislead new customers. E-commerce customers can post reviews and comments on the goods or services they obtained. Before making a purchase, new customers frequently read the feedback and comments posted on the website. Nowadays customers find it very difficult to identify whether the reviews are fake or not, but doing so is essential. So, it's very crucial to develop an online spam detection system to help both consumers and producers in their decision-making. The reviewer's behaviour and important review characteristics can help you identify fake reviews. The importance of this study is to develop a fake review detection system on e-commerce platforms using an enhanced ensemble support vector machine system in which the Euclidean distance is replaced with the Mahalanobis distance metric. Review texts collected from Amazon and Yelp were given as input data sets into the constructed model and classified as fake or real.
Computer simulation of transition modes in flow reactors considering the multistage and reactions non-perfectness
Authors: Kalbayeva Aizhan Tazhikhanovna , Umarova Zhanat Rysbayevna , Kurakbayeva Sevara Dzhumagaliyevna , Musabekova Leyla Mukhamedjanovna , Amandikov Madamin A. , Arystanbayev Kuttybek E.
Published: January 2025

Due to the variety of reaction types and schemes in chemical-technological apparatuses, a general engineering methodology to assessing how the transient modes and reactions multi-stage act the kinetics in conditions of occurrence of moving reaction fronts in flow apparatuses has not yet been developed. The paper devotes to constructing the mathematical models for several important cases of the problem mentioned, namely: for theoretically study the kinetic dynamics of the conversion process in a three-stage chemical reaction with an autocatalytic first stage and the presence of a mass source of one of the components. An original mathematical model for describing the chemisorption dynamics at the initial stage of forming a moving reaction front in flow-through apparatuses has been developed. A special algorithm and numerical solution for the initial absorption period have been constructed, and appropriate computer simulation has been implemented. The significant influence of multistage on the formation and on stability types of stationary states has been established. Expressions to evaluate the characteristics of the emerging oscillatory modes have been obtained too. The results can be used to assess the influence of control parameters on the reaction front movement speed.
An efficient frequent itemsets finding in distributed datasets with minimum communication overhead
Authors: Essalmi Houda , El Affar Anass
Published: January 2025

Finding frequent itemsets is an essential researched technique and a challenging task of data mining. Traditional approaches for distributed frequent itemsets require massive communication overhead among different distributed datasets. In this paper, we adopt a new strategy for optimizing the time of communications/synchronizations from large datasets and, we present a novel algorithm for discovering frequent itemsets in different distributed datasets on the slave sites called finding efficient distributed frequent itemsets (FEDFI). The proposed algorithm is capable of generating the important frequent itemsets by applying an efficient technique for pruning the candidate itemsets. The experimental results confirm that our algorithm FEDFI performs better than Apriori and candidate distribution (CD) algorithms in terms of communication and computation costs.
Advanced tourist arrival forecasting: a synergistic approach using LSTM, Hilbert-Huang transform, and random forest
Authors: Mukhtar Harun , Remli Muhammad Akmal , Mohamad Mohd Saberi , Wan Salihin Wong Khairul Nizar Syazwan , Ridhollah Farhan , Deprizon Deprizon , Soni Soni , Lisman Muhammad , Amran Hasanatul Fu'adah , Sunanto Sunanto , Ismanto Edi
Published: January 2025

An advanced synergistic approach for forecasting tourist arrivals is presented, integrating long short-term memory (LSTM), Hilbert-Huang transform (HHT), and random forest (RF). LSTM is leveraged for its capability to capture long-term dependencies in sequential data. Additional data from Google Trends (GT) is processed with HHT for feature extraction, followed by feature selection using the RF algorithm. The combined HHT-RF-LSTM model delivers highly accurate forecasts. Evaluation employs regression analysis with metrics such as root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE), highlighting the effectiveness of this innovative approach in predicting tourist arrivals. This methodology provides a robust framework for handling limited datasets and improving forecast reliability. By incorporating diverse data sources and advanced preprocessing techniques, the model enhances prediction performance, demonstrating the strong performance of RF in feature selection.
An intelligent intrusion detection system to prevent URL redirection attack
Authors: Sadanand Vijaya Shetty , Naidu Palamaneni Ramesh , Bolla Dileep Reddy , Neeli Jyoti , Prakash Ramya
Published: January 2025

In today’s digital age, the widespread use of social networking platforms like Facebook, Twitter, and Instagram, alongside messaging services such as Email and WhatsApp, has increased the convenience of communication. However, this accessibility has also provided a fertile ground for cybercriminals and spammers to exploit these platforms through URL redirection attacks, which are often used to steal sensitive user information. Existing solutions, including machine learning (ML), deep learning (DL), and ensemble methods have been employed to combat such threats. Despite their effectiveness, these approaches struggle to detect emerging types of attacks and suffer from limitations when dealing with imbalanced data, leading to reduced detection performance. To address these challenges, this research introduces an improved extreme gradient boosting (IXGB) algorithm that optimizes the weight adjustments in the model, aiming to enhance the detection of malicious URLs. The proposed method focuses on improving classification accuracy, especially for new or unseen types of attacks. Experimental results on a standard dataset demonstrate that IXGB achieves superior accuracy compared to traditional models, making it a promising approach for enhancing cybersecurity on social media and messaging platforms.
Energy and cost-aware workload scheduler for heterogeneous cloud platform
Authors: Shivanandappa Manjunatha , Chowdaiah Naveen Kumar , Devaraje Gowda Swetha Mysore , Shivaswamy Rashmi , Ramasamy Vadivel , Prabhu Vijay Subramani Suryakumar
Published: January 2025

Parallel scientific workloads, often represented as directed acyclic graphs (DAGs), consist of interdependent tasks that require significant data exchange and are executed on distributed clusters. The communication overhead between tasks running on different nodes can lead to substantial increases in makespan, energy usage, and monetary costs. Therefore, there is potential to balance communication and computation to reduce these costs. In this paper, we introduce an energy and cost-aware workload scheduler (ECAWS) tailored for executing parallel scientific workloads, generated by the internet of things (IoT), in a heterogeneous cloud environment. The performance of the proposed ECAWS model is evaluated against existing models using the Inspiral scientific workload. Results indicate that ECAWS outperforms other models in reducing makespan, costs, and energy consumption.
HorseNet: a novel deep learning approach for horse health classification
Authors: Atitallah Nesrine , Abdel-Wahab Ahmed , Hadi Anas A. , Abdel-Jaber Hussein , Mohamed Ali Wagdy , Elsersy Mohamed , Mansour Yusuf
Published: January 2025

In equestrian sports and veterinary medicine, horse welfare is paramount. Horse tiredness, lameness, colic, and anemia can be identified and classified using deep learning (DL) models. These technologies analyze horse images and videos to help vets and researchers find symptoms and trends that are hard to see. Early detection and better treatment of certain disorders can improve horses’ health. DL models can also improve with new data, improving diagnosis accuracy and efficiency. This study comprehensively evaluates three convolutional neural network (CNN) models to distinguish normal and abnormal horses using the generated horse dataset. For this study, a unique dataset of horse breeds and their normal and abnormal states was collected. The dataset includes mobility patterns from this study’s initial data collection. DL models like CNNs and transfer learning (TL) models (visual geometry group (VGG)16, InceptionV3) were employed for categorization. The InceptionV3 model outperformed CNN and VGG16 with over 97% accuracy. Its depth and multi-level structure allow the InceptionV3 model to recognize characteristics in images of varied scales and complexities, explaining its excellent performance.
HCRF: an improved random forest algorithm based on hierarchical clustering
Authors: Zhuo Wang , Ahmad Azlin
Published: January 2025

Random forest (RF) selects feature subsets randomly. Useless and redundant features will lower the quality of the selected features and subsequently affect the overall classification accuracy of the RF. This study proposes an improved RF algorithm based on hierarchical clustering (HCRF). The algorithm uses hierarchical clustering algorithms to optimize the feature selection process, by establishing similar feature groups based on the GINI index, and then selecting features from each group proportionally to construct the feature subset. The feature subset is then used to construct a single classifier. This process increases the filtering of feature subsets, reducing the negative impact of useless and redundant features on the model, and improving the model's generalization ability and overall performance. In the experimental verification, ten datasets of different sizes and domains were selected, and the accuracy, precision, recall, F1 score, and running time of HCRF, support vector machine (SVM), RF, classification and regression tree (CART) were compared using 10-fold cross-validation. Combining all the results, the HCRF algorithm showed significant improvements in all evaluation indicators, proving that its performance is superior to the other three classifiers. Therefore, this algorithm has broad application areas and value, and effectively improves the overall performance of the classifier within a lower complexity range.
Secure financial application using homomorphic encryption
Authors: Bidve Vijaykumar , Pavate Aruna , Raut Rahul , Kediya Shailesh , Sarasu Pakiriswamy , Rao Anne Koteswara , Gangadhara Aryani , Shaikh Ashfaq
Published: January 2025

In today’s digital age, the security and privacy of financial transactions are paramount. With the advent of technologies like homomorphic encryption, it is now possible to perform computations on encrypted data without the need to decrypt it first, offering a promising avenue for secure financial applications. This research paper explores the implementation and implications of utilizing homomorphic encryption in financial applications to safeguard sensitive data while maintaining computational integrity. By employing homomorphic encryption techniques, financial institutions can enhance the confidentiality of their clients’ information, protect against data breaches, and enable secure computations on encrypted data. The paper discusses the principles of homomorphic encryption, its applications in financial systems, challenges, and potential solutions. Additionally, it examines real-world examples and case studies where homomorphic encryption has been employed successfully, highlighting its effectiveness in ensuring the privacy and security of financial transactions. Overall, this paper aims to provide insights into the role of homomorphic encryption in creating secure financial applications and its potential to revolutionize the way sensitive financial data is handled and processed.
Cluster based water leakage detection frame work for the improvement of water management using WSN
Authors: Shivashankar Shivashankar , Rajgopal Manjunath , Karani Krishna Prasad , Basavaraju Nandeeswar Sampigehalli , Giddappa Erappa , Swamy Shivakumar
Published: January 2025

Water is a vital resource that is essential for human survival and economic development. In any case, water shortage and wastage have become significant difficulties that undermine economical turn of distribution network. A critical reason for water wastage is water leakage in the distribution system, which prompts an extensive loss of water assets and energy. Conventional manual techniques for identifying water leakage are tedious, work serious, and frequently ineffectual. Hence, there is a need for an automated system that can efficiently detect, control and monitor water leakage to improve water management. In this paper, cluster-based water leakage detection (CBWLAD) algorithm for the improvement of water management using wireless sensor network (WSN) is proposed. The design system contains sensor nodes which are conveyed all through the water distribution networks and associated with central control unit. The sensor nodes can distinguish changes in the water pressure and flow rate, which are indicative of water leakage. The real time monitoring feature also enables timely maintenance and repair of the network of water distribution prolongs the lifespan of infrastructure. Further, the research and development are required to optimize the system’s performance and adapt it to real-world scenarios.
Identification of ocular disease from fundus images using CNN with transfer learning
Authors: Berrichi Fatima Zohra , Belmadani Abderrahim
Published: January 2025

Eye diseases are one of the serious health problems affecting human life. Detecting and diagnosing them early is critical to prompt treatment and preventing vision loss. However, all studies in the field of eye disease classification using machine learning models are limited to the detection of single diseases, and the accuracy rate is still low in multi-class systems. In this study, we propose a multi-class classification model using four pre-trained CNNs (DenseNet121, ResNet50, EfficientNetB3 and VGG16). The model classified eye diseases into four categories: diabetic retinopathy, cataract, glaucoma, and normal. To improve the training process, another data augmentation technique is applied to increase the amount of data. The performance metrics of the system are calculated using the confusion matrix. DenseNet-121 shows excellent performance in retinal disease classification in 30 epochs of training, with training and test accuracy reaching 99.97% and 96.21% respectively. The implementation of this system should be considered as a very useful means to help ophthalmologists to rapid and precision detection of various eye diseases in the future.
Study on postal life insurance attributes and its growth prediction using machine learning algorithms
Authors: Rajasekaran Thangavelu Ananadaraj , Vijayalakshmi Pichamuthu , Rajendran Velayutham
Published: January 2025

The oldest insurer in the country, since 1884, is Postal Insurance. For today's livelihood, the citizens of India's life-saving coverage and insurance have become necessary. For customers to overcome difficult situations, life insurance is crucial in creating confidence. This is one of the highlights of the Postal organization. Under postal life insurance (PLI), the volume of new policies is enrolled throughout India, and a supervised machine learning (ML) process for finding the business cluster is carried out based on this data, which is discussed. A ML algorithm that predicts the growth for the future, using a suitable algorithm for accessing the features and process to identify the prediction model, has been developed, which is the main goal of this study. Simulation results show that expected is one of the most important variables used to predict and that both random forest (RF) and logistic regression outperformed the other two models. The RF model is the most effective and fastest in predicting the system's future state, and it shows the highest value for the PLI product.
Exploration of various approaches for detection of autism spectrum disorder
Authors: Gangaraju Kavitha , H K Yogisha
Published: January 2025

Autism spectrum disorder (ASD) presents a complex and diverse set of challenges, necessitating innovative and data-driven approaches for effective understanding, diagnosis, and intervention. This review explores recent advancements in methodologies, technologies, and frameworks aimed at addressing ASD and also highlights novel data collection methods, focusing on the integration of wearable internet of things (IoT) sensors for real-time behavioral monitoring and data capture from individuals with ASD. Additionally, the utilization of machine learning (ML), deep learning (DL), and hybrid techniques for data analysis, feature optimization, and prediction of ASD are extensively discussed, showcasing significant progress in early diagnosis and personalized intervention planning. The challenges such as class imbalance, feature selection, and data collection efficiency are identified and addressed using the proposed ASD framework. The review also emphasizes the development of recommendation systems designed to the unique behavioral profiles and needs of individuals with ASD. The findings reveal that integrating these advanced technologies and methodologies can lead to more accurate diagnoses and effective interventions, contributing to the broader field of ASD research.
Enhancing attack detection in IoT through integration of weighted emphasis formula with XGBoost
Authors: Al Amien Januar , Ab Ghani Hadhrami , Md Saleh Nurul Izrin , Soni Soni
Published: January 2025

This research addresses the challenge of detecting attacks in the internet of things (IoT) environment, where minority classes often go unnoticed due to the dominance of majority classes. The primary objective is to introduce and integrate the imbalance ratio formula (IRF) into the XGBoost algorithm, aiming to provide greater emphasis on minority classes and ensure the model's focus on attack detection, particularly in binary and multiclass scenarios. Experimental validation using the IoTID20 dataset demonstrates the significant enhancement in attack detection accuracy achieved by integrating IRF into XGBoost. This enhancement contributes to the consistent improvement in distinguishing attacks from normal traffic, thereby resulting in a more reliable attack detection system in complex IoT environments. Moreover, the implementation of IRF enhances the robustness of the XGBoost model, enabling effective handling of imbalanced datasets commonly encountered in IoT security applications. This approach advances intrusion detection systems by addressing the challenge of class imbalance, leading to more accurate and efficient detection of malicious activities in IoT networks. The practical implications of these findings include the enhancement of cybersecurity measures in IoT deployments, potentially mitigating the risks associated with cyber threats in interconnected smart environments.
Automated handwriting analysis and personality attribute discernment using self-attention multi-resolution analysis
Authors: Dhumal Yashomati R. , Shinde Arundhati A. , Sapkal Roshnadevi Jaising , Bhairannawar Satish
Published: January 2025

Handwritten document analysis is a method used in academia that examines the patterns and strokes of a person’s handwriting in order to get a deeper understanding of that person’s personality and character. In spite of the fact that there are a number of models and methods that may be used in the investigation of automated graphology, there are a few challenges that need to be solved. Among these challenges is the identification of efficient classification techniques that provide the highest possible degree of accuracy. Within the scope of this study, we propose automated handwriting analysis and personality attribute discernment using self-attention multi-resolution analysis (MRA) where the data is preprocessed using histogram equalization and the spurious line segment section is attached to the genuine line segment portion in order to segment the succeeding line from the authentic picture of the document. A deep dense network is combined with self-attention MRA in order to provide a novel approach to the investigation of authentic handwritten text. Using the most recent and cutting-edge standards that are currently in use, an evaluation is performed to determine whether or not the proposed strategy is feasible. It is observed that the proposed method obtained nearly 98% accuracy with precision of 99%.
An ensemble approach for detection of diabetes using SVM and DT
Authors: Vamsikrishna Mangalapalli , Gupta Manu , Bagade Jayashri , Bhimanpallewar Ratnmala , Shelke Priya , Bodapati Jagadeesh , Komali Govindu , Mande Praveen
Published: January 2025

As diabetes affects the health of the entire population, it is a chronic disease that is still an important worldwide health issue. Diabetes increases the possibility of long-term complications, such as kidney failure and heart disease. If this disease is discovered early, people may live longer and in better health. In order to detect and prevent particular diseases, machine learning (ML) has become essential. An ensemble approach for detection of diabetes using support vector machine (SVM) and decision tree (DT) presents in this paper. In this case, to identify diabetes, two ML techniques are DT and SVM have been combined with an ensemble classifier. They obtain the information, they require from the Public Health Institute’s statistics area. There are 270 records, or instances, in the collection. This dataset includes the following attributes: age, a body mass index (BMI) glucose, and insulin. The development of a system that predictions a patient’s risk of diabetes is the goal of this analysis. Several performance metrics, including F1-score, recall, accuracy, and precision, were used to achieve this. From overall results, 96% of precision, 97% of accuracy, 96% of F1-score, and 97% of recall values are the results achieved for the ensemble model (SVM+DT) which is more effective than other individual ML models as DT and SVM.
Implementation of a prototype to prevent childhood accidents in dangerous domestic environments using ESP 32 Wi-Fi module
Authors: Lavalle-Sandoval Jenner , Córdova-Cardenas Paul , Rivera-Quispe Sheyla , Andrade-Arenas Laberiano
Published: January 2025

Robotics has significantly advanced human evolution by optimizing tasks in fields such as medicine, engineering, and mechanics, enhancing daily life through various robotic prototypes. These innovations help prevent accidents and injuries, whether at home or in hazardous environments. For instance, sensors can detect gas leaks, fires, and other potential disasters. This research aims to design a prototype adaptable to any home environment that poses risks to infants, such as kitchens, bathrooms, or stairs. The proposed prototype incorporates gas, motion, and sound sensors connected to a Wi-Fi ESP 32 module, which alerts parents to any potential danger to their children. The research is developed in six phases: component selection, circuit simulation, prototype design, three-dimensional (3D) printing, code programming, and final testing. The results demonstrate a positive impact, improving the control and care of infants by alerting parents to hazards such as gas leaks, crying, or movement in risky areas. The conclusion confirms the effectiveness of the prototype in providing timely alerts to safeguard infants in potentially dangerous situations.