bluuebunny/crossref_metadata_2025 huggingface dataset:
Large language models-based metric for generative question answering systems
Authors: Abdel Azim Hazem , Tharwat Waheed Mohamed , Mohammed Ammar
Published: November 2024

In the evolving landscape of text generation, which has advanced rapidly in recent years, techniques for evaluating the performance and quality of the generated text lag behind relatively. Traditionally, lexical-based metrics such as bilingual evaluation understudy (BLEU), recall-oriented understudy for gisting evaluation (ROUGE), metric for evaluation of translation with explicit ordering (METEOR), consensus-based image description evaluation (CIDER), and F1 have been utilized, primarily relying on n-gram similarity for evaluation. In recent years, neural and machine-learning-based metrics, like bidirectional encoder representations from transformers (BERT) score, key phrase question answering (KPQA), and BERT supervised training of learned evaluation metric for reading comprehension (LERC) have shown superior performance over traditional metrics but suffered from a lack of generalization towards different domains and requires massive human-labeled training data. The main contribution of the current research is to investigate the use of train-free large language models (LLMs) as scoring metrics, evaluators, and judges within a questionanswering context, encompassing both closed and open-QA scenarios. To validate this idea, we employ a simple zero-shot prompting of Mixtral 8x7 B, a popular and widely used open-source LLM, to score a variety of datasets and domains. The experimental results on ten different benchmark datasets are compared against human judgments, revealing that, on average, simple LLMbased metrics outperformed sophisticated state-of-the-art statistical and neural machine-learning-based metrics by 2-8 points on answer-pairs scoring tasks and up to 15 points on contrastive preferential tasks.
Intelligent cervical cancer detection: empowering healthcare with machine learning algorithms
Authors: Yadav Uma , D. Bondre Vipin , Bondre Shweta V. , Thakre Bhakti , Agrawal Poorva , Thakur Shruti
Published: November 2024

Cervical cancer remains a significant global health issue, particularly in underdeveloped nations, where it contributes to high mortality rates. Early detection is critical for improving treatment outcomes and survival rates. This study employs machine learning (ML) algorithms to predict cervical cancer risk using a dataset from the University of California at Irvine (UCI), which includes demographic and clinical attributes such as age, sexual history, smoking habits, and medical history. After applying data preprocessing techniques, several classification algorithms, including logistic regression (LR), support vector machine (SVM), random forest (RF), decision tree, adaptive boosting (AdaBoost), and artificial neural networks (ANN), were trained and evaluated. The models were assessed using classification metrics such as precision, recall, and F1 score. Among the models, the ANN demonstrated the highest accuracy, achieving a score of 0.95. In addition, correlation analysis revealed significant relationships between various risk factors, providing insights into cervical cancer mechanisms and potential preventive measures. The study highlights the potential of ML in improving cervical cancer detection and patient outcomes, suggesting that advanced ML techniques can be valuable tools in healthcare research and clinical applications.
A novel ensemble-based approach for Windows malware detection
Authors: Verma Vikas , Malik Arun , Batra Isha , Hosen A. S. M. Sanwar
Published: November 2024

The exponential growth of internet-connected devices, particularly accelerated by the COVID-19 pandemic, has brought forth a critical global challenge: safeguarding the security of transmitted information. The integrity and functionality of these devices face significant threats from various forms of malware, leading to behavioral distortions. Consequently, a vital aspect of cybersecurity entails accurately identifying and classifying such malware, enabling the implementation of appropriate countermeasures. Existing literature has explored diverse approaches for malware identification, encompassing static and dynamic analysis techniques like signature-based, behavior-based, and heuristic-based methods. However, these approaches face a key issue of inadequately identifying unknown malware variants, often resulting in misclassifications of new strains as benign. To tackle this challenge, this study introduces a novel ensemble-based approach for identifying and classifying malware on Windows platforms, with a specific focus on detecting new and previously unknown variants. The proposed approach leverages multiple machine learning schemes to identify elusive unknown malware that proves challenging for existing methods. 
DriveNet: A deep learning framework with attention mechanism for early driving maneuver prediction
Authors: M'haouach Mohamed , Sassioui Abdellatif , Bouhoute Afaf , Fardousse Khalid
Published: November 2024

Inappropriate driving maneuvers are the leading cause of many car accidents. These accidents can be prevented if they are identified in advance and the driver is given the necessary assistance. Anticipating maneuvers is crucial for driving assistance systems in order to alert drivers and take appropriate measures to avoid or mitigate danger. In this paper, we introduce DriveNet a new approach that combines information about the driver’s behavior as well as the driving environment to predict the driving maneuvers. DriveNet utilizes a combination of convolutional neural network (CNN) and long short-term memory (LSTM) with attention mechanism to extract spatial information and capture long temporal dependencies. We evaluate DriveNet by performing a series of experiments using the publicly available Brain4Cars dataset. The findings show that the proposed approach achieves state-of-the-art performance and outperforms most previous methods. DriveNet has achieved an accuracy of 91.24%, a precision of 90.13%, and a recall of 91.44% for anticipation 4 seconds before the maneuvers occur.
Revolutionizing cancer classification: the snr-ogscc method for improved gene selection and clustering
Authors: Bouazza Sara Haddou , Bouazza Jihad Haddou
Published: November 2024

This study presents the signal-to-noise ratio optimized gene selection and clustering for cancer classification (SNR-OGSCC) methodology, aimed at enhancing classification accuracy while reducing the dimensionality of gene expression data across various cancer types. Implemented on a standard computational setup, the SNR-OGSCC method combines advanced filtering, clustering, and machine learning techniques, demonstrating significant improvements in classification accuracy on seven cancer datasets: leukemia, colon cancer, prostate cancer, lung cancer, lymphoma, central nervous system (CNS) tumors, and ovarian cancer. Notably, our approach achieved perfect accuracies of 100% for leukemia, lung cancer, and ovarian cancer, with high accuracies of 98.4% for colon cancer, 99.1% for prostate cancer, 98.3% for lymphoma, and 99.7% for CNS tumors, while requiring as few as 4–5 genes for effective classification. These findings highlight the efficiency and robustness of the SNR-OGSCC methodology, suggesting its potential to identify meaningful biomarkers and improve personalized cancer treatment strategies. Further validation with larger datasets and biological experiments is essential to confirm its applicability in clinical settings.
Fastest Moroccan license plate recognition using a lightweight modified YOLOv5 model
Authors: Fadili Abdelhak , El Aroussi Mohammed , Fakhri Youssef
Published: November 2024

Morocco is witnessing an alarming surge in road accidents. Automatic license plate recognition (ALPR) technology is vital in enhancing road safety. It en- ables applications like traffic management, law enforcement, and toll collection by automatically identifying vehicles on the roads. This paper integrated the ShuffleNet V2 architecture into the end-to-end YOLOv5 object detection sys- tem. The goal was to develop a model capable of accurately detecting Moroc- can license plates with an 87% accuracy rate. The proposed model was able to achieve high processing speeds of 60 frames per second (FPS) while maintain- ing a compact size of 1.3 megabytes and a limited computational requirement of 0.44 million floating-point operations. Compared to other models used in similar contexts, this model demonstrates superior performance and high com- patibility with embedded systems, making it a promising solution for addressing road safety challenges in Morocco.
Predicting enhanced diagnostic models: deep learning for multi-label retinal disease classification
Authors: Sundararajan Sridhevi , Ramachandran Harikrishnan , Gupta Harshita , Patil Yashraj
Published: November 2024

In this study, we assess three convolutional neural network (CNN) architectures—VGG16, ResNet50, and InceptionV3 for multi classification of fundus images in the retinal fundus multi-disease image dataset (RFMID2), comprising of 860 images. Focusing on diabetic retinopathy, exudation, and hemorrhagic retinopathy, we preprocessed the dataset for uniformity and balance. Using transfer learning, the models were adapted for feature extraction and fine-tuned to our multi-label classification task. Their performance was measured by subset accuracy, precision, recall, F1-score, hamming loss, and Jaccard score. VGG16 emerged as the top performer, with the highest subset accuracy (84.81%) and macro precision (95.83%), indicating its superior class distinction capabilities. ResNet50 showed commendable accuracy (79.75%) and precision (86.70%), whereas InceptionV3 lagged with lower accuracy (66.67%) and precision (81.21%). These findings suggest VGG16’s depth offers advantages in multi-label classification, highlighting InceptionV3’s limitations in complex scenarios. This analysis helps optimize CNN architecture selection for specific tasks, suggesting future exploration of dataset variability, ensemble methods, and hybrid models for improved performance.
Detecting human fall using internet of things devices for healthcare applications
Authors: Benhaili Zakaria , Balouki Youssef , Moumoun Lahcen
Published: November 2024

Falls pose a significant threat to unintentional injuries, particularly impacting the independence of older individuals. Existing detection methods suffer from drawbacks, including inaccuracies, wearer discomfort, complex setup, resource-intensive computation, and limitations in detecting falls outside a specific setting. In response, our innovative fall detection system integrates with a pneumatic solution, analyzing fundamental human activities like running, walking, and sitting, both indoors and outdoors. This approach combines wearable sensors with a vision-based solution, utilizing a smart belt with embedded accelerometer and gyroscope, alongside wall-installed cameras in a smart house. The system triggers an airbag and sends an emergency alarm upon fall detection. To achieve this, we propose FallMixer a lightweight deep learning model, combined with ‘you only look once’ version 8 (YOLOv8) algorithm, fine-tuned on a collected video dataset to enable real-time detection. We found that the models result in competitive performance, as demonstrated on SisFall, UCI human activity recognition (HAR), and mobile health (MHEALTH) datasets with a remarkable mean average precision. Subsequently, we assess the hardware performance of our solution on edge devices.
Artificial intelligence-driven method for the discovery and prevention of distributed denial of service attacks
Authors: ALDabbas Ashraf , Baniata Laith H. , AlSaaidah Bayan A. , Mustafa Zaid , Alali Muath , Rateb Roqia
Published: November 2024

Distributed denial of service (DDoS) attacks has emerged as a prominent cyber threat in contemporary times. By impeding the machine's capacity to give services to legitimate clients, the impacted system performance and buffer size are reduced. Researchers are working to build sophisticated algorithms that can identify and thwart DDoS violations. An effective approach for DDoS attacks has been proposed in this work. This research presents a model as a potential explanation for DDoS assaults. In order to successfully identify this kind of attacks, which may stop or block the urgent and vital transmission of data, we present a distinctive method that integrates a pair of fully connected layers within an amalgamated deep learning (DL) framework with long short-term memory (LSTM) and a max pooling layer. The acquired accuracy reached 99.58%.
Parallel rapidly exploring random tree method for unmanned aerial vehicles autopilot development using graphics processing unit processing
Authors: Mochurad Lesia , Davidekova Monika , Mitoulis Stergios-Aristoteles
Published: November 2024

Autonomous air movement systems hold great potential for transforming various industries, making their development essential. Autopilot design involves advanced technologies like artificial intelligence, machine learning, and big data. This paper focuses on developing a parallel rapidly-exploring random tree (RRT) algorithm using compute unified device architecture (CUDA) technology for efficient processing on graphics processing units (GPUs). The study evaluates the algorithm's performance in automated trajectory planning for unmanned aerial vehicles (UAVs). Numerical experiments show that the parallel algorithm outperforms the sequential central processing unit (CPU)-based version, especially as task complexity and state space dimensions increase. In scenarios with numerous obstacles, the parallel algorithm maintains stable performance, making it well-suited for various applications. Comparisons with CPU-based methods highlight the advantages of GPU use, particularly in terms of speed and efficiency. Additionally, the performance of two GPU models, NVIDIA RTX 2070 and T4 is compared, with the T4 demonstrating superior performance for similar tasks. Future research should explore integrating multiple algorithms for a more comprehensive UAV autopilot system. The proposed approach stands out for its stability and practical applicability in real-world autopilot implementations.
A page rank-based analytical design of effective search engine optimization
Authors: Srinivas Vinutha Mysore , Halli Cheluvae Gowda Padma Muthalambikasheta
Published: November 2024

Search engine optimization (SEO) is an important internet marketing strategy and process that facilitates maximizing an intended website’s visibility with search engine results. It is widely employed nowadays to improve traffic volume or quality from search engines to a particular website. Even though a significant number of publications imply the essential aspects of SEO, only a few provide generalized ideas to deal with the complex structure of the web. Also, the critical issues of content quality, site popularity, keyword density, and publicity factors were not much considered in the traditional ranking algorithms during SEO processes. This has negatively influenced the retrieval rate in the existing SEO techniques, and consequently, inadequate search results were obtained through search engines. Hence, the study considers web page ranking as a theoretical basis for the research and addresses these limitations in the existing system. It further improves SEO performance by introducing a unique web-page ranking strategic design to gain higher page rank results. The results of the investigational study show that the proposed system effectively contributes towards SEO with an improved page ranking strategy and also provides higher accuracy in calculating the importance score of web pages which is comparable with popular ranking algorithms such as hyperlink-induced topic search (HITS) and PageRank.
An ontology-based knowledge modeling for the rite of Bai Sri Su Kwan: a ritual of the Greater Mekong Subregion
Authors: Hoaihongthong Suwannee , Chaichuay Vispat , Kwiecien Kanyarat
Published: November 2024

The development of ontologies is crucial in digital humanities research. This study focuses on creating a system to extract meaning from knowledge related to the Bai Sri Su Kwan ritual. Addressing semantic gaps, the second phase of our research outlines methods for developing an ontology for Bai Sri Su Kwan rituals. To fully understand this significant ritual in the Mekong Basin, we employed a theoretical framework with seven ontology development steps, using the Hozo Ontology Editor. Our ontology includes nine main classes: Bai Sri Su Kwan (A ritual subclass), persons, chants, belief, purpose, wish, literature, locations, and equipment. The Bai Sri Su Kwan subclass connects with all other classes in the ontology. This ontology forms the basis for a meaning search system for the Bai Sri Su Kwan ceremony in future research stages. The ontology was evaluated syntactically through human assessment and the OOPS! Ontology Pitfall Scanner. Validation results for the Bai Sri Su Kwan Ontology show no pitfalls in critical dimensions, indicating high integrity and reliability.
Serial parallel dataflow-pipelined processing architecture based accelerator for 2D transform-quantization in video coder and decoder
Authors: Shivarudraiah Sumalatha , Rajeswari Rajeswari
Published: November 2024

The video coder and decoder (CODEC) standards from MPEG-4 to the recent versatile video codec (VVC), adopted lossy compression methodologies, which involves transformation, quantization and entropy coding. The growing usage of video data in all means of communication demands more bandwidth and storage requirements. While compression with redundancy removal by transform coefficient coding, the focal point is the crucial sequential data flow and data processing structures. Handling the block wise data near to the processing unit prior and after computation will reduce the data waiting time of the processing unit, hence accelerating the targeted functionality. The proposed serial parallel data-flow pipelined processing architecture (SPDPA) accelerates the speed of processing unit by on chip data availability and parallel data accessing options and also with the pipeline operations of transformation, data transpose and quantization. The post implementation results of the architecture targeted to 16 nm and 28 nm field programmable gate array (FPGA) shows that there is a trade-off between power and frequency of operations for various block sizes. The design targeted to 16 nm works for higher frequencies with an average power consumption 0.64 w as compared to 28 nm FPGA which consumes less average power of 0.15 w.
Early skin disease diagnosis by using artificial neural network for internet of healthcare things
Authors: Wan Bejuri Wan Mohd Yaakob , Mohamad Mohd Murtadha , Tang Michelle , Ahmad Khair Aina Khairina , Adriyansyah Yusuf Athallah , Kasmin Fauziah , Tahir Zulkifli
Published: November 2024

Internet of healthcare things (IoHT) represents a burgeoning field that leverages pervasive technologies to create technology driven environments for healthcare professionals, thereby enhancing the delivery of efficient healthcare services. In remote and isolated areas, such as rural communities and boarding schools, access to healthcare professionals (especially dermatologists) can be particularly challenging. However, these areas often lack the specialized expertise required for effective skin disease consultations. Thus, the purpose of this research is to design a scheme of early skin disease diagnosis for internet of healthcare things that is accessible anywhere and anytime. In this research, the image of skin disease from patient will be taken by using a mobile phone for predicting and identifying the disease. This proposed scheme will diagnose skin disease and convert it be meaningful information. As a result, it show our proposed scheme can be the most consistent in term of accuracy and loss compared to others method. Overall, this research represents a significant step toward improving healthcare accessibility and empowering individuals to manage their own health. Furthermore, the proposed scheme is anticipated to contribute significantly to the IoHT field, benefiting both academia and societal health outcomes.
Privacy preserving ZK-STARK based blockchain for agriculture food supply chain
Authors: Arade Madhuri Sadashiv , Pise Nitin N.
Published: November 2024

The blockchain-based applications such as food supply chain (FSC), healthcare are becoming increasingly popular due to their decentralized nature, distributed structure, and the ability to track products. Still, there are concerns regarding the privacy of transacted data, including personal identities, as all transactions are recorded on a ledger accessible to participating nodes. Existing technologies of privacy preservation are vulnerable to quantum attacks, which will pose a significant threat to blockchain applications in the future. To address this, a proposed model uses modified zero-knowledge scalable transparent argument of knowledge (ZK-STARK) in the blockchain FSC by utilizing three different polynomial interpolation methods. Performance measurements indicate that the fast fourier transform (FFT) outperforms the others. Unlike ZK-SNARK, ZK-STARK does not require a trusted setup, makes scalable and transparent. By defending against quantum attacks, this model enhances the security of the blockchain system. The blockchain-based FSC is implemented using hyperledger composer, with all entities completing transactions privately through ZK-STARK and smart contracts. But, ZK-STARK may add performance overhead into the blockchain FSC. Future work will aim to reduce the performance overhead of ZK-STARK, decide which operations should be off-chain or on-chain, and compare the performance of this new model to the existing system.
Improved Bi-GRU for parkinson’s disease severity analysis
Authors: Arunachalam Malathi , Ramar Ramalakshmi , Gandhi Vaibhav , Ananthan Bhuvanesh
Published: November 2024

Parkinson’s disease (PD) is a common neuro-degenerative issue, evaluated via the continuous deterioration of motor functions over time. This condition leads to a gradual decline in movement capabilities. For diagnosing clinical set of PDs, medical experts utilize medical observations. These observations are highly based on the expert’s experience and can vary among clinicians due to its subjective nature, leading to differences in evaluation. The gait patterns of individuals with PD typically exhibit distinctions from those of adults. Evaluating these gait malformations not only aids in diagnosing PD but can also enable the categorization of severity stages with respect to symptoms of motor movement. Therefore, this paper introduces a classification of gait model based on the optimized deep learning (DL) model bidirectional gated recurrent unit-artificial hummingbird optimizer (BI-GRU-AHO). The training and testing involved the sequential segmentation of the right and left instances from the signals of vertical ground reaction force (VGRF) based on the identified gait cycle. The outcomes of the proposed BI-GRU-AHO exhibits reliable and accurate assessment of PD and achieved better accuracy of 98.7 %. The proposed model is trained and tested satisfactorily; hence it can be implemented in a real-time environment by integrating the model into a software application or system capable of receiving real-time data from PD patients.
EDK-LEACH: improving LEACH protocol-based machine learning in wireless sensor networks
Authors: Lechani Taous , Ourari Samia , Rahmoune Fayçal , Amari Said , Termeche Hayet
Published: November 2024

In wireless sensor networks (WSNs), many sensor devices are spread throughout the environment with the goal of collecting data and sending them to a base station (BS) for further studies. The issue of their limited battery power has aroused the interest of researchers, and several protocols were developed to optimize energy use and thus increase the network’s lifetime. The present research enhances the well-known low-energy adaptive clustering hierarchy (LEACH) protocol with a new artificial intelligence (AI) protocol named energy distance K-means LEACH (EDK-LEACH). For this purpose, an innovative clustering strategy built on the machine learning K-means algorithm is used in WSNs to improve the cluster formation process and maximise network stability. By implementing an objective function that considers each node’s residual energy and distance from the cluster centre when selecting the cluster head (CH) of each cluster, EDK-LEACH also eliminates the inherent randomness in LEACH during the CH election process. The proposed protocol has the advantage of ensuring better CH distribution throughout the network surface with a balanced load across all network nodes. In comparison with the known LEACH, the simulation results demonstrate the efficiency of our approach: the lifetime of the network is extended and the energy consumption is reduced.
Gamification in work-based learning in vocational education to support students' coding abilities
Authors: Jalinus Nizwardi , Ganefri Ganefri , Syahril Syahril , Zaus Mahesi Agni , Islami Syaiful
Published: November 2024

This article studied the integration of gamification in work-based learning within vocational education as a means to support students' coding abilities. By applying game mechanics such as points, badges, leaderboards, and challenges, we aimed to motivate and engage students in coding activities that mirror real-world industry practices. The inclusion of gamified elements into the curriculum was designed to make the learning process more interactive, fostering a competitive yet collaborative environment that enhances students' interest and perseverance in coding tasks. This research employed a quasi-experimental design with pre-test and post-test measures to assess the impact of gamification on coding proficiency, comparing the outcomes of students participating in gamified learning environments with those in traditional settings. The findings indicate a significant improvement in the coding skills of students exposed to gamified work-based learning, suggesting that gamification can serve as an effective pedagogical tool in vocational education, better preparing students for industry demands.
Effective autism spectrum disorder sensory and behavior data collection using internet of things
Authors: Kumar Vittalraju Chetan , Umesh Dadadahalli Ramu
Published: November 2024

Wireless body area networks (WBANs) connected with wearable internet of things (WIoT) offer useful features including sensory information collection, analysis, and transmission for continuous behavior monitoring of autism spectrum disorder (ASD) patients. Due to users’ mobility and time-driven sensed data, data collection becomes very difficult. The current approach employs cluster-based multi-objective path-optimized data collection mechanisms that have experienced hotspot issues leading to loss of energy and coverage problems near the base stations. This work presents the high energy and reliable sensory and behavior data collection (HERSBDC) mechanism to address the research difficulties. To ensure network coverage, the HERSBDC initially provides a new uneven clustering mechanism. Next, multi-objective-based cluster head (CH) selection metrics are proposed. The final step is the creation of a multi-objective routing path to gather vital ASD data more reliably and energy-efficiently. Comparing the proposed HERSBDC algorithm to the low energy adaptive cluster-hierarchy (LEACH)-based, and distributed energy-efficient clustering and routing (DECR) methods, the simulation results demonstrate that the HERSBDC mechanism achieves a much better lifetime by 62.28% and 11.89%, the delivery ratio by 15.04% and 9.51%, with minimal delay by 52.65%, and 9.65%, and routing overhead by 32.05%, and 42.65%, respectively.
IT risks associated with information theft in the financial system - a systematic review
Authors: Cabanillas-Allca Frank , Chaquila-Muñoz Sebastian , Iparraguirre-Villanueva Orlando
Published: November 2024

This research paper systematically reviews the financial system’s computer security risks associated with information theft. The objective is to explore the security risks and their implications concerning information theft in the economic system. Three research questions were formulated to identify these risks, their nature, and potential consequences to achieve this objective. Fifty-five articles obtained from reliable databases linked to both study variables were analyzed using the PRISMA methodology. To ensure the validity and reliability of the information, various filters were applied, such as year, keywords, and elimination of duplicate articles. In addition, an exhaustive reading of the content of each article was carried out, organizing all the information through a systematization matrix. After a thorough review of the research articles, mostly written in English and representing 34.55% of the total in 2023, risks associated with the financial sector were identified, including malware, ransomware, phishing, distributed denial of service (DDoS), hybrid XSS, eavesdropping, and social engineering. Geographically, India leads with 14.55% of the articles, followed by South Korea and the United States, with 12.72% each, while the other countries have lower percentages. In conclusion, these risks coincide with previous research and the consequences they generate, highlighting the importance of this type of study for the basis of scientific research.
Performance analysis of 10 machine learning models in lung cancer prediction
Authors: Zapata-Paulini Joselyn , Cabanillas-Carbonell Michael
Published: November 2024

Lung cancer is one of the diseases with the highest incidence and mortality in the world. Machine learning (ML) models can play an important role in the early detection of this disease. This study aims to identify the ML algorithm that has the best performance in predicting lung cancer. The algorithms that were contrasted were logistic regression (LR), decision tree (DT), k-nearest neighbors (KNN), gaussian Naive Bayes (GNB), multinomial Naive Bayes (MNB), support vector classifier (SVC), random forest (RF), extreme gradient boosting (XGBoost), multilayer perceptron (MLP) and gradient boosting (GB). The dataset used was provided by Kaggle, with a total of 309 records and 16 attributes. The study was developed in several phases, such as the description of the ML models and the analysis of the dataset. In addition, the contrast of the models was performed under the metrics of specificity, sensitivity, F1 count, accuracy, and precision. The results showed that the SVC, RF, MLP, and GB models obtained the best performance metrics, achieving 98% accuracy, 98% precision, and 98% sensitivity.
Design of starting a three phase induction motor using direct on-line, variable frequency drive, soft starting, and auto transformer methods
Authors: Siregar Yulianta , Rotua Oktaviana Siahaan Yosephine , Nabila Binti Mohamed Nur , Candra Riawan Dedet , Yuhendri Muldi
Published: November 2024

The problem with 3-phase induction motors is that when starting the motor, the motor starting current can reach five to seven times the nominal current. This research compares slip, starting current, bus voltage, acceleration torque, motor torque, energy savings, and kVAR from the direct on-line (DOL), variable frequency drive (VFD), soft starting, and autotransformer starting methods in the electrical transient analyzer program (ETAP) software. This research result shows that the fastest VFD slip reaches a steady state, namely at 11+ seconds. The lowest starting/starting current is owned by the VFD method, namely <20% full load amps (FLA) in the first 2 seconds. The lowest decrease in bus voltage at steady state was experienced by the VFD method, namely 0.8152%. The quickest acceleration torque reaches a steady state in the VFD method, namely in 11+ seconds. The soft starting method owns the lowest starting torque, namely 20.75%. The soft starting method has the largest energy savings, namely 148.02 kW. Of the several variables observed, the best starting method is the VFD method.
Optimization machine learning models for selecting transmit antennas in 5G/6G systems
Authors: Ouldammar Abdellah , Moulay Lakhdar Abdelmounaim , Bouida Ahmed , Merit Khaled
Published: November 2024

Transmit antenna selection (TAS) plays a crucial role in improving the performance and spectral efficiency of 5G/6G systems. This study proposes to use the GridSearchCV method for hyperparameter optimization in two supervised learning models, support vector machine (SVM) and K-nearest neighbors (KNN), to optimally select antenna peers based on channel gain. These models were applied to Alamouti’s space-time block coding to improve performance, resulting in increased signal-to-noise ratio (SNR) and reduced bit error rate (BER). The results show that optimizing the hyperparameters led to a significant improvement in the performance of the SVM and KNN models. The SVM and KNN models were evaluated using a variety of metrics, with the SVM demonstrating superior predictive performance in terms of accuracy, average macro recall, average macro precision, average macro F1 score, and cross-validation score. Even before optimization, the SVM outperforms the KNN in terms of performance metrics. After optimization, this gap widens further, demonstrating the robustness of SVM for classification tasks. Although KNN is faster to train.
Hybrid resource optimization strategy in heterogeneous wireless networks
Authors: Gadde Nagaraja , Shivaswamy Rashmi , Halasinanagenahalli Siddamal Ramesh Babu , Gowrishankar Gowrishankar , Thimma Raju Govindaih , Suryakumar Prabhu Vijay Subramani
Published: November 2024

The future generation of heterogeneous wireless networks (HWNs) will combine various radio access technologies for connecting various mobile subscribers (MS) based on the quality of service (QoS) and wireless network parameters, connecting MS to the best possible wireless network (WN) has been a trending research topic in HWNs. Existing resource optimization methods are designed to meet the QoS of network criteria and user preferences are neglected. Very limited work is done for resource optimization considering user preferences. However, these models are designed considering multi-mode terminals (MMTs) running a single service at a time under a low-density network; as a result, cannot be adopted to run multiple services simultaneously and; thus, fail to meet current users’ service dynamics requirement. Further, fails to bring good tradeoffs between reducing interference and improving performance. In addressing the research problem this work introduced a hybrid resource optimization strategy (HROS) to reduce interference by establishing channel availability and enhancing resource utilization through game theory. The HROS proves the existence of nash equilibrium (NE) improves throughput by 16.32% and reduces collision by 26.16% over the existing resource optimization-based network selection (RONS) scheme.
Integration of message queue and drop policy in spray and hop distance protocol for DTNs in smart city scenario
Authors: Agussalim Agussalim , Tsuru Masato , Susrama Mas Diyasa I Gede , Rahmat Basuki
Published: November 2024

Implementing delay tolerant networks (DTNs) in smart cities for developing countries is promising. DTNs offer a low-cost network communication solution without expensive infrastructure, such as 3G/4G or LPWA networks provided by commercial operators. This paper considers sensor data collection in the Surabaya smart city scenario, and the use of DTNs is examined instead of the expensive infrastructure. However, a customized and optimized protocol is required since no existing DTN routing protocol perfectly matches the scenario. This paper proposes integrating a hop count-based message queue (HCMQ) and a message time-to-live (TTL)-based drop policy (MTDP) into the spray and hop distance protocol (SNHD), an enhanced version of the spray and wait (SNW) protocol. The Surabaya smart city scenario was simulated on the one simulator with a wide range of message generation rates at each sensor node. The proposed integration significantly improves the total size of delivered messages, especially when the message generation rate is high, i.e., in congested situations, compared to other routing protocols in this scenario. It also exhibits an average latency lower than other routing protocols. Overall, this integration enhances the DTNs protocol’s performance in a low-cost alternative data collection in the Surabaya smart city scenario.
High-gain circularly polarized metasurface antenna for NR257 band millimeter-wave 5G communication
Authors: Haandi Lakshman Praveen , Thatti Yerriswamy , Kumar Tharehalli Rajanna Puneeth , Mudukavvanavar Shambulinga
Published: November 2024

A metasurface inspired circularly polarized (CP) compact patch antenna with high gain for fifth-generation communication systems is designed and implemented in this article. The proposed structure features a corner truncated patch antenna and a metasurface of 3×3 double sided identical circular metallic patches. The attribute that puts this design distinct is that it minimizes the impact of scattering and edge diffraction at millimeter wave frequencies. The metasurface above a patch with an air gap is designed using the similar substrate material with the same thickness, resulting in a simplified antenna design with high gain and low cost. The antenna’s overall dimension is , with a peak gain of 11.5 dBic and a 3-dB axial ratio bandwidth of 28.45 - 28.88 GHz. The simulated and experimental results show that the metasurface-inspired antenna has better impedance matching and radiation efficiency between 28.23 - 30.01 GHz. Additionally, the experimental results of the proposed antenna exhibits stable right-hand circular polarization in the desired frequency range and a flat gain response with a little variation. The proposed antenna design could be well suited for millimeter-wave communication systems, in scenarios requiring robust long-range performance and high data throughput.
Novel method for multi-user collaborative spectral decision in decentralized cognitive radio networks
Authors: Hernández Cesar , Giral Diego , Salgado Camila
Published: November 2024

Cognitive radio networks positively impact the performance of wireless communications and have proven to be an excellent alternative for efficient and effective use of the radio spectrum. However, few proposals collaboratively work on decision-making in decentralized cognitive radio networks. The present work refers to a novel method and device that reduces the rate of channel changes during secondary user communications in decentralized cognitive radio networks through a collaborative spectral decision between several secondary users while allowing multiple secondary users access to the network. This proposal consists of a multi-user unit that regulates the access of multiple secondary users (SUs) to the spectrum, a priority unit that guarantees timely access to the SUs according to their level of importance, and a prediction unit that forecasts the arrival time of the primary user (PU). This multichannel unit regulates the assignment of multiple spectral opportunities to the SU according to the type of application it is using and a unit of deep learning that determines which spectral opportunity(s) are most suitable for each SU and spectral allocation. The results obtained allow us to satisfactorily validate the proposal developed and corroborate the importance of collaborative work in decision-making to select spectral opportunities.
Machine learning-based emotions recognition model using peripheral signals
Authors: Kumar Tarun , Kumar Rajendra , Chandra Singh Ram
Published: November 2024

This work proposes a system for emotion recognition using four peripheral signals electromyography, galvanic skin response, blood volume pulse, and respiration. Peripheral signals cannot be modified, unlike other expression like voice and facial expression. The proposed method is applied to the DEAP datasets to verify the accuracy of emotion recognition. The proposed model focuses on accuracy and F1-score. DEAP dataset has more signals but only thirty-seven features from four peripheral signals were extracted for each trail and each video. On the DEAP datasets, the implementation found that the classification accuracy for arousal, valence, liking, and dominance was, respectively, 80%, 75%, 71%, and 78%. For two classes of problems, the corresponding F1-scores for arousal, valence, liking, and dominance are 0.50, 0.49, 0.47, and 0.47. The proposed model was implemented in MATLAB R2017a.
Sex-Specific Association Between Genetic Risk of Psychiatric Disorders and Cardiovascular Diseases
Authors: Jiang Jiayue-Clara , Singh Kritika , Nitin Rachana , Davis Lea K. , Wray Naomi R. , Shah Sonia
Published: November 2024

BACKGROUND:

Though epidemiological studies show increased cardiovascular disease (CVD) risks among individuals with psychiatric disorders, findings on sex differences in comorbidity have been inconsistent.

METHODS:

This genetic epidemiology study examined the sex-specific association between the genetic risk of 3 psychiatric disorders (major depression [MD], schizophrenia, and bipolar disorder), estimated using polygenic scores (PGSs), and risks of 3 CVDs (atrial fibrillation [AF], coronary artery disease [CAD], and heart failure [HF]) in 345 169 European-ancestry individuals (UK Biobank), with analyses replicated in an independent BioVU cohort (n=49 057). Mediation analysis was conducted to determine whether traditional CVD risk factors could explain any observed sex difference.

RESULTS:

In the UK Biobank, a 1-SD increase in PGS
MD
was significantly associated with the incident risks of all 3 CVDs in females after multiple testing corrections (hazard ratio [HR]
AF-female
=1.04 [95% CI, 1.02–1.06];
P
=1.5×10
−
4
; HR
CAD-female
=1.07 [95% CI, 1.04–1.11];
P
=2.6×10
−
6
; and HR
HF-female
=1.09 [95% CI, 1.06–1.13];
P
=9.7×10
−
10
), but not in males. These female-specific associations remained even in the absence of any psychiatric disorder diagnosis or psychiatric medication use. Although mediation analysis demonstrated that the association between PGS
MD
and CVDs in females was partly mediated by baseline body mass index, hypercholesterolemia, hypertension, and smoking, these risk factors did not explain the higher risk compared with males. The association between PGS
MD
and CAD was consistent between females who were premenopausal and postmenopausal at baseline, while the association with AF and HF was only observed in the baseline postmenopausal cohort. No significant association with CVD risks was observed for the PGS of schizophrenia or bipolar disorder. The female-specific positive association of PGS
MD
with CAD risk was replicated in BioVU.

CONCLUSIONS:

Genetic predisposition to MD confers a greater risk of CVDs in females versus males, even in the absence of any depression diagnosis. This study warrants further investigation into whether genetic predisposition to depression could be useful for improving cardiovascular risk prediction, especially in women.
Loss of Endothelial
APOE4
Dysregulates Neural Function In Vivo
Authors: Marottoli Felecia M. , Balu Deebika , Flores‐Barrera Eden , de la Villarmois Emilce Artur , Zhang Hui , Chaudhary Rohan , Talati Ruju , Tseng Kuei Y. , Tai Leon M.
Published: November 2024

Background

We recently found that loss of endothelial cell
APOE3
disrupts neurovascular and synaptic function. However, whether endothelial
APOE4
is detrimental or protective for neural function under physiological conditions is unknown. Therefore, the goal of this study was to determine the role of endothelial cell
APOE4
in regulating brain function in vivo.

Methods and Results

We developed
APOE4
fl/fl
/Cdh5(PAC)‐CreERT2
+/−
and
APOE4
fl/fl
/Cdh5(PAC)‐CreERT2
−/−
(control) mice. Knockdown of endothelial cell
APOE4
was induced at ≈4 to 5 weeks of age. Experiments were conducted at 9 months of age to evaluate neurovascular and neuronal function via biochemistry, immunohistochemistry, behavior tests, and electrophysiology. Endothelial cell
APOE4
knockdown resulted in higher neurovascular permeability, lower claudin‐5 vessel coverage, impaired trace fear memory extinction, and disruption of cortical excitatory‐inhibitory balance of synaptic activity.

Conclusions

Our data support the novel concept that endothelial cell
APOE4
is protective for brain function when other cell types express
APOE4
.
Çalışan Yetişkinlerde Çalışma İradesi, Kariyer Uyumluluğu ve Dengeli Yaşam: İş İhtiyaçları Doyumunun Aracılık Rolü
Authors: Çeven Hamidiye Betül , Büyükgöze-kavas Ayşenur , Kirazcı Furkan
Published: November 2024

Bu araştırmada Çalışma Psikolojisi Kuramı temel alınarak dengeli yaşamın üzerinde anlamlı role sahip olabileceği düşünülen insana yakışır işin psikolojik yordayıcılarından çalışma iradesi ve kariyer uyumluluğu ile insana yakışır işin sonuçlarından iş ihtiyaçları doyumu, ara değişkenli nedensel bir model üzerinde incelenmiştir. Önerilen modelin test edilmesi amacıyla 403 tam zamanlı çalışan yetişkinden Çalışma İradesi Ölçeği, Kariyer Uyum Yetenekleri Ölçeği, İş İhtiyaçları Doyumu Ölçeği ve Dengeli Yaşam Ölçeği kullanılarak veri toplanmıştır. Toplanan verilere yönelik yapılan yol analizi sonucunda çalışma iradesi ve kariyer uyumluluğunun iş ihtiyaçları doyumu aracılığında dengeli yaşamı pozitif yönde ve anlamlı düzeyde, ayrıca kariyer uyumluluğunun dengeli yaşamı doğrudan pozitif yönde ve anlamlı düzeyde yordadığı ve tüm modelin dengeli yaşamı %43 oranında açıkladığı sonucuna ulaşılmıştır. Araştırma kapsamında bulgular tartışılmış ve bu bulgulara dayalı olarak önerilerde bulunulmuştur.
Multifactorial effects of probiotic Parasutterella excrementihominis on gestational inflammation, offspring behaviour and prenatal-stress induced disruptions in tryptophan metabolism
Authors: Galley J.D. , Rajasekera T.A. , Bennouna D.J. , Batabyal A. , Verosky B. , Woodke S. , Stokes J. , Brown A.K. , Murthy S. , Kopec R.E. , Gur T.L.
Published: November 2024

Prenatal stress (PNS) has widespread effects on offspring, including aberrant immune development and behavioural deficits. The microbiome is a mediator of the dissemination of stress effects to the offspring through immunomodulation and metabolite production. Metabolites derived from the mother and their gut microbiota pass to the foetus and can affect immune and nervous development. Stress affects the abundance of such metabolites, including the tryptophan (Trp) pathway, which are involved in immune and nervous system function. We hypothesized that the PNS is associated with dysregulation of Trp metabolism. We further posited that treatment with a Trp-metaboliser Parasutterella excrementihominis would abrogate PNS-associated deleterious effects on offspring development. To test this hypothesis, pregnant mice were exposed to restraint stress and administered P. excrementihominis (Dam n = 3-9; Offspring n = 5-10). PNS increased maternal gut Trp and both maternal and offspring inflammation. P. excrementihominis treatment reduced the PNS-induced excess pool of maternal gut Trp. Some PNS effects on foetal neuroinflammation were reduced in severity due to handling effects from bacterial gavage. However, P. excrementihominis was anti-inflammatory in dam and offspring and anxiolytic in offspring of Pe-treated dams. These data illustrate that elevated Trp levels are associated PNS and its downstream deleterious offspring inflammatory and behavioural outcomes while P. excrementihominis, a Trp-metabolizer, can ameliorate these effects and improve offspring outcomes.