bluuebunny/crossref_metadata_2025 huggingface dataset:
Performance analysis of a neuromodel for breast histopathology decision support system
Authors: Ojo Adedayo Olukayode , Sola Adetoro Mayowa , Ojo Florence Omolara , Onibonoje Oluwafemi Moses
Published: November 2024

Breast cancer detection and diagnosis are crucial in reducing mortality rates among women globally. This research article explores an artificial intelligence technique for early breast cancer detection, aiding doctors in making informed decisions for improved patient management. The study employs histopathological analysis of breast tissue microscopically to detect abnormalities, with the aim of categorizing normal tissue, benign lesions, in situ carcinoma, and invasive carcinoma. The proposed technique utilizes an artificial neural network trained using the resilient backpropagation algorithm (RP_ANN). The study further compares the observed performance with those of three other algorithms, including gradient descent algorithm (GDA_ANN), Levenberg-Marquardt algorithm (LM_ANN), and layer sensitivity-based (LSB_ANN) algorithm based on various evaluation metrics. RP_ANN and LSB_ANN demonstrated superior performance, with high validation and training variance accounted for (VAF) and low root mean squared error (RMSE). The results underscore the potential of deep learning-based algorithms for improving breast cancer detection, promising better patient outcomes and enhanced diagnostic accuracy.
Improved adaptive multi-threshold method for automatic identification of rhinosinusitis in paranasal sinus images
Authors: Putra Ondra Eka , Sumijan Sumijan , Tajuddin Muhammad
Published: November 2024

Rhinosinusitis, characterized by inflammation of the mucosa or mucous membrane within the paranasal sinuses, anatomical cavities situated in the facial bones, is the focus of this investigation. This study employs computed tomography (CT)-scan images comprising sagittal slices of the paranasal sinuses, acquired through a CT device featuring a Philips Ingenuity CT model MRC880 tube type, identified by tube serial number 163889, with a pixel value resolution of 0.24 mm. The primary objective of this research is to automatically identify and delineate rhizosinusitis-affected areas. This involves the application of multi-threshold values during the segmentation process, utilizing the improved adaptive multi-threshold (IAMT) segmentation method. The research dataset encompasses 380 slices of CTscans derived from 10 patients displaying indications of rhinosinusitis. Analysis of the test results reveals that the smallest observed rhinosinusitis size in this study is 0.05 cm2 on the right side, while the largest size measures 1.81 cm2 , yielding an accuracy rate of 96.66%. The magnitude of rhinosinusitis sizes serves as an indicative measure of the extent of inflammation within the paranasal sinus region, thereby suggesting a potential need for more intensive treatment interventions for the affected patients.
High body temperature detection solution through touchless machine for health monitoring
Authors: Swami Siddharth , Mohan Joshi Lalit , Ismail Iqbal Mohammed , Sharma Meera , Jeet Rawat Amar , Dev Sharma Sameer , Singh Rajesh
Published: November 2024

The demand for reliable health monitoring systems has surged in today's health-conscious society. Body temperature monitoring is crucial for preserving health and preventing infectious disease outbreaks. In this study an Arduino uno hardware board with a touchless temperature sensor is proposed to detect elevated body temperature, indicating fever and early signs of illness. The system prioritizes real-time health surveillance, accessibility, and usability, blending seamlessly with normal life. Arduino's versatility allows the system to function covertly, uphold privacy and autonomy, and foster wellbeing. The goal is to highlight the system's ability to function covertly, uphold privacy and autonomy, and foster wellbeing. This technology exemplifies the synergy between personal wellness and contemporary technologies, offering a useful and adaptable fever detection solution for various contexts, including homes and public areas.
Levenberg-Marquardt-optimized neural network for rainfall forecasting
Authors: Rudrappa Gujanatti , Vijapur Nataraj , Hosamane Sateesh
Published: November 2024

Rainfall is a crucial meteorological indicator with applications in agriculture, aviation, and military. Forecasting is essential due to unpredictable environmental changes. Current methods use complex statistical models, which are timeconsuming. The present study is targeted for forecasting rainfall with the help of meteorological parameters, viz., temperature, humidity, wind speed, wind direction, and rain, using a specialized artificial intelligence (AI) method and real-time data captured over the study area. The weather station installed at KLE Dr. M. S. Sheshgiri College of Engineering and Technology in Karnataka, India, collects meteorological data. The models used were principal component regression (PCR) and Levenberg-Marquardt -optimized neural network (LMAONN). The Levenberg-Marquardt (LMA) backpropagation (BP) algorithm performed better than other BP algorithms. The coefficient of determination (R2) observed for the PCR and LMAONN models were 0.57 and 0.87, respectively. The LMAONN model provided a better fit for rainfall forecasting than the PCR model, with an index of agreement (IoA) of 0.96, indicating good forecasting.
Implementation and evaluation of Heskes self organizing map counter propagation network for face recognition
Authors: Olagunju Kazeem Michael , Oke Alice Oluwafunke , Falohun Adeleye Samuel , Adebiyi Marion O.
Published: November 2024

Face recognition has attracted a lot of interest in the fields of computer vision and pattern recognition given its extensive applications in security, surveillance, and human-computer interaction. Many linear and non-linear classifiers have been introduced to bring about effectiveness in face recognition, however, the problem of occlusion, light conditions and changes in face persist. The Heskes-self-organizing map (SOM) counter propagation network (CPN) model leverages the competitive learning and self-organizing features of SOM CPN with Heskes layer to improve the effectiveness and accuracy of face recognition systems. Heskes-SOM CPN was implemented and evaluated on MATLAB R2016a using 600 images captured with the aim of digital camera. The implemented model was trained with 360 face images and tested with 240 face images using accuracy, sensitivity, specificity, and false positive rate as performance metrics at four distinct threshold values of 0.23, 0.35, 0.50, and 0.75. The major objective of the research was achieved by investigating with 50×50 and 200×200 face dimensions. Empirical results and statistical evidence established that Heskes-SOM CPN has high accuracy of approximately 97.92%, high specificity of 98.33%, high sensitivity of 99.44%, and a very low filter performance rating (FPR) of 1.67%. Therefore, Heskes-SOM CPN is presented as a novel CPN model for face recognition.
Unmanned aircraft vehicles/unmanned aerial systems digital twinning: Data-driven lift and drag prediction for airfoil design
Authors: Aghazadeh Ardebili Ali , Martella Angelo , Khalil Adem , Khalil Sabri , Longo Antonella , Ficarella Antonio
Published: November 2024

This study investigates the innovative application of neural networks algorithms in the aviation industry's mechanical design process, motivated by the pursuit of creating a more accurate and efficient method for performance prediction. Traditional approaches, such as computational fluid dynamics (CFD) simulations based on solving Navier-Stokes’s equations, demand substantial computational power and often exhibit limited accuracy, particularly when compared with complex geometries. The state-of-the-art review unveils a growing research trend advocating for data-driven methodologies to revolutionize design practices, addressing the limitations of conventional techniques. The primary objective of this study is to explore how neural network algorithms can overcome the drawbacks of CFD simulations, offering a more effective alternative for predicting the performance of airfoils. To achieve this objective, we conducted a performance analysis of airfoils using neural network algorithms. The results presented a promising avenue for a more accurate and efficient performance prediction method through digital twinning. The study highlights the advantageous features of neural network methods in unmanned aircraft vehicles (UAV) component mechanical design, showcasing their potential to outperform traditional methods and offering practical recommendations for integration into the design process.
Bias in artificial intelligence: smart solutions for detection, mitigation, and ethical strategies in real-world applications
Authors: Samala Agariadne Dwinggo , Rawas Soha
Published: November 2024

Artificial intelligence (AI) technologies have revolutionized numerous sectors, enhancing efficiency, innovation, and convenience. However, AI's rise has highlighted a critical concern: bias within AI algorithms. This study uses a systematic literature review and analysis of real-world case studies to explore the forms, underlying causes, and methods for detecting and mitigating bias in AI. We identify key sources of bias, such as skewed training data and societal influences, and analyze their impact on marginalized communities. Our findings reveal that algorithmic transparency and fairnessaware learning are among the most effective strategies for reducing bias. Additionally, we address the challenges of regulatory frameworks and ethical considerations, advocating for robust accountability mechanisms and ethical development practices. By highlighting future research directions and encouraging collective efforts toward fairness and equity, this study underscores the importance of addressing bias in AI algorithms and upholding ethical standards in AI technologies.
A multi-algorithm approach for phishing uniform resource locator’s detection
Authors: Sree Guddanti Devi , Chiramdasu Rupa , Gayathri Uppuluri Mahalakshmi
Published: November 2024

Nowadays, the internet is used to organise a wide range of cybersecurity risks. Threats to cybersecurity include a broad spectrum of malevolent actions and possible hazards that affect data, networks, and digital systems. Cybersecurity dangers that are commonly encountered are distributed denial-of-service (DDoS) attacks, phishing, and malware. Phishing attempts frequently use text messages, email, and uniform resource locators (URLs) to target specific people while impersonating trustworthy sourcesin an effort to trick the victim. Consequently, machine learning plays a critical role in stopping cybercrimes, especially those that involve phishing assaults. The suggested model is based on a well constructed dataset that has been enhanced with 32 features. By combining the features of several machine learning methods, such as random forest, CatBoost, AdaBoost, and multilayer perceptron, the suggested model greatly increases the precision of phishing URL detection. Evaluation indicators that highlight the model's effectiveness in defending against cyber threats include precision, recall, accuracy, and F1-score. These metrics also highlight the urgent need for proactive cybersecurity measures.
Two-step convolutional neural network classification of plant disease
Authors: Lumbantoruan Rosni , Rajagukguk Nico , Lubis Anju Ucok , Claudia Marwani , Simanjuntak Humasak
Published: November 2024

Indonesia is primarily an agricultural country, with farming being the primary source of income for most of its people. Unfortunately, crop production is vulnerable to plant diseases, which are usually caused by plant pests, resulting in a reduction in both the quantity and quality of the expected harvest. In addition to the large number of classes to predict, detecting and accurately classifying each disease on different plants can be difficult. We believe that limiting the number of classes to identify may improve classification accuracy. Thus, in this research, we propose a new approach, two-step convolutional neural network (CNN), which reduces the number of classes with a two-step classification approach. To begin, we identify the number of classes that can be reduced by categorizing them into different characteristics, namely, plant type classification and plant condition classification. Second, we deal with unbalanced datasets, which can result in poor performance, if overlooked. Finally, we compare the proposed two-step CNN to baseline CNN in terms of efficiency and effectiveness. Extensive experiments show that the two-step CNN outperforms the baselines, CNN and jellyfish-residual network (JF-ResNet), increasing accuracy by 4% and 2% to 99%, respectively. In addition, we also provide a simulation evaluation to ensure that this approach is applicable.
Deep learning architectures for location and identification in storage systems
Authors: Espitia Cubillos Anny Astrid , Jimenez Moreno Robinson , Rodríguez Carmona Esperanza
Published: November 2024

This document exposes the application of two deep learning models based on ResNet-18 architectures, intended for the location and identification of products in storage areas. One model obeys a tree structure and the other a structure under an ouroboron cycle. The performance of both models is evaluated using the metrics of training time, processing time and level of learning precision, which allows recommendations to be made regarding which one should be used for order preparation purposes, based on multilevel feature extraction. The total training time of the first model is 34.65 minutes and the second 40.43 minutes. The analysis of results allowed the detection parameters to be adjusted, finally with the refined models, through confusion matrices, precision results greater than 90% and processing times are obtained, which for model 1 is 6.8565 seconds and for model 2 is 4.884 seconds. For practical purposes, training times are not relevant, as are the precision and processing times for selecting the most convenient model according to the end user's objectives.
Utilization of convolutional neural network in image interpretation techniques for detecting kidney disease
Authors: Sulaksono Nanang , Adi Kusworo , Isnanto Rizal
Published: November 2024

This research is conducted with deep learning for kidney stone disease detection including cysts, stones, normal, and tumors using axial computerized tomography (CT) scan images. The author uses augmentation, generative adversarial networks (GANs), original, and synthetic minority over-sampling technique (SMOTE) to classify kidney disease (cyst, stone, normal, and tumor). This study uses the public dataset nazmul0087 and primary data/data from the hospital, using convolutional neural network (CNN) models, namely augmentation, GANs, original, and SMOTE by training and testing. The results of the accuracy value of the training model (dataset nazmul0087) in the detection of kidney cysts, stones, tumors, and normal. The results of augmentation value are 99.93%, GANs 100%, original 100%, and SMOTE 99.93%. In the results of the training model, a very high accuracy value is obtained, with perfect results. The testing model's accuracy value in detecting kidney cysts, stones, tumors, and normal kidney tissue in the original dataset and hospital data. The results of augmentation value are 11.48%, GANs 17.96%, original 21.76%, and SMOTE 20.41%. In the results of the training model, the highest accuracy value is obtained in the original model. For the testing model to automatically diagnose kidney illness and obtain a high accuracy value, which can enhance patient outcomes and save health care costs, we advise using it in conjunction with the original model.
Chinese paper classification based on pre-trained language model and hybrid deep learning method
Authors: Luo Xin , Mutalib Sofianita , Syed Aris Syaripah Ruzaini
Published: November 2024

With the explosive growth in the number of published papers, researchers must filter papers by category to improve retrieval efficiency. The features of data can be learned through complex network structures of deep learning models without the need for manual definition and extraction in advance, resulting in better processing performance for large datasets. In our study, the pre-trained language model bidirectional encoder representations from transformers (BERT) and other deep learning models were applied to paper classification. A large-scale chinese scientific literature dataset was used, including abstracts, keywords, titles, disciplines, and categories from 396 k papers. Currently, there is little in-depth research on the role of titles, abstracts, and keywords in classification and how they are used in combination. To address this issue, we evaluated classification results by employing different title, abstract, and keywords concatenation methods to generate model input data, and compared the effects of a single sentence or sentence pair data input methods. We also adopted an ensemble learning approach to integrate the results of models that processed titles, keywords, and abstracts independently to find the best combination. Finally, we studied the combination of different types of models, such as the combination of BERT and convolutional neural networks (CNN), and measured the performance by accuracy, weighted average precision, weighted average recall, and weighted average F1 score.
Reliable backdoor attack detection for various size of backdoor triggers
Authors: Rah Yeongrok , Cho Youngho
Published: November 2024

Backdoor attack techniques have evolved toward compromising the integrity of deep learning (DL) models. To defend against backdoor attacks, neural cleanse (NC) has been proposed as a promising backdoor attack detection method. NC detects the existence of a backdoor trigger by inserting perturbation into a benign image and then capturing the abnormality of inserted perturbation. However, NC has a significant limitation such that it fails to detect a backdoor trigger when its size exceeds a certain threshold that can be measured in anomaly index (AI). To overcome such limitation, in this paper, we propose a reliable backdoor attack detection method that successfully detects backdoor attacks regardless of the backdoor trigger size. Specifically, our proposed method inserts perturbation to backdoor images to induce them to be classified into different labels and measures the abnormality of perturbation. Thus, we assume that the amount of perturbation required to reclassify the label of backdoor images to the ground-truth label will be abnormally small compared to them for other labels. By implementing and conducting comparative experiments, we confirmed that our idea is valid, and our proposed method outperforms an existing backdoor detection method (NC) by 30%p on average in terms of backdoor detection accuracy (BDA).
Enhancement of YOLOv5 for automatic weed detection through backbone optimization
Authors: Habib Mohammed , Sekhra Salma , Tannouche Adil , Ounejjar Youssef
Published: November 2024

In the context of our research project, which involves developing a robotic system capable of eliminating weeds using deep learning technics, the selection of powerful object detection model is essential. Object detectors typically consist of three components: backbone, neck, and prediction head. In this study, we propose an enhancement to the you only look once version 5 (YOLOv5) network by using the most popular convolutional neural networks (CNN) networks (such as DarkNet and MobileNet) as backbones. The objective of this study is to identify the best backbone that can improve YOLOv5 's performance while preserving its other layers (neck and head). In terms of detecting and ultra-localizing pea crops. Additionally, we compared their results with those of the most commonly used object detectors. Our findings indicate that the fastest models among the networks studied were MobileNet, YOLO-tiny, and YOLOv5, with speeds ranging from 5 to 14 milliseconds per image. Among these models, MobileNetv1 demonstrated the highest accuracy, achieving average precision (AP) score of 89.3% for intersection over union (IoU) threshold of 0.5. However, the accuracy of this model decreased when we increased the threshold, suggesting that it does not provide perfect crop delineation. On the other hand, while YOLOv5 had a lower AP score than MobileNetv1 at an IoU threshold of 0.5, it exhibited greater stability when faced with variations in this threshold.
Arabic text diacritization using transformers: a comparative study
Authors: Zubeiri Iman , Souri Adnan , El Mohajir Badr Eddine
Published: November 2024

The Arabic language presents challenges for natural language processing (NLP) tasks. One such challenge is diacritization, which involves adding diacritical marks to Arabic text to enhance readability and disambiguation. Diacritics play a crucial role in determining the correct pronunciation, meaning, and grammatical structure of words and sentences. However, Arabic texts are often written without diacritics, making NLP tasks more complex. This study investigates the efficacy of advanced machine learning models in automatic Arabic text diacritization, with a concentrated focus on the Arabic bidirectional encoder representations from transformers (AraBERT) and bidirectional long short-term memory (Bi-LSTM) models. AraBERT, a bidirectional encoder representation from transformers (BERT) derivative, leverages the transformer architecture to exploit contextual subtleties and discern linguistic patterns within a substantial corpus. Our comprehensive evaluation benchmarks the performance of these models, revealing that AraBERT significantly outperforms the Bi-LSTM with a diacritic error rate (DER) of only 0.81% and an accuracy rate of 98.15%, against the Bi-LSTM's DER of 1.02% and accuracy of 93.88%. The study also explores various optimization strategies to amplify model performance, setting a precedent for future research to enhance Arabic diacritization and contribute to the advancement of Arabic NLP.
Detection and avoidance of black-hole attack in mobile adhoc network using bee-ad-hoc on-demand distance vector
Authors: Pala Srikanth , Maddula Prasad , Pokkuluri Kiran Sree , Pattem Sunil , Kurada Ramachandra Rao , Yadavalli Ramu
Published: November 2024

Mobile adhoc networks (MANETs) are self-configuring networks with a dynamic infrastructure suit for real world applications. Due to the exponential increase in the network devices an efficient routing algorithm for dynamic network adhering the security issues is a critical challenge needs to be addressed. This article attempts to address this issue with the implemention of ad-hoc on-demand distance vector (AODV) routing approach, which is the best of its kind in the dynamic network design of MANETs. The primary goal is to address security attack weaknesses through the implementation of dynamic topologies and reactive routing. To this end, a bio-inspired swarm intelligence algorithm called Bees algorithm is used to emulate the AODV technique. In order to provide a lightweight solution that integrates the Bee algorithm and AODV routing, this study presents a unique algorithm called Bee-AODC. The proposed Bee-AODC algorithm possess the both the AODV's dynamic topology construction capabilities and the Bee algorithm's foraging strategy which effectively address security weaknesses by creating a dynamic network topology for ad hoc routing. By using the suggested Bee-AODC algorithm instead of the traditional AODV routing method, throughput is increased by 12.87% while packet loss, latency, and energy consumption are reduced by 20%, 40%, and 18%, respectively.
A novel secure and energy aware LOADng routing protocol for IoT: an application to smart agriculture
Authors: Sana Touhami , Mohamed Belghachi
Published: November 2024

In the burgeoning domain of the internet of things (IoT), efficient and secure communication protocols are crucial for the seamless operation of diverse applications. This paper proposes a novel routing protocol, termed secure and energy aware LOADng (SEA-LOADng), tailored for IoT deployments in the context of smart agriculture. The protocol is designed to address the unique challenges posed by agricultural environments, including limited energy resources and the need for robust security measures. The proposed protocol leverages LOADng, a lightweight and efficient routing protocol suitable for low-power and lossy networks characteristic of IoT deployments. Through innovative energy-aware mechanisms, it optimizes the power usage of IoT devices, thus prolonging their operational lifespan and reducing maintenance overhead. Moreover, stringent security measures are integrated into the protocol to safeguard sensitive data transmitted within the IoT network. To assess the efficacy of the proposed protocol, comprehensive simulations are carried out using realistic smart agriculture scenarios. The results demonstrate significant improvements in energy efficiency compared to LOADng protocol, while maintaining robust security against hello flood attack.
Enhancing data cleaning process on accounting data for fraud detection
Authors: Abdul Malek Mohamad Affendi , Abd Jalil Kamarularifin
Published: November 2024

Data cleaning is a crucial step in fraud detection as it involves identifying and correcting any inaccuracies or inconsistencies in the data. This can help to ensure that the data being used for fraud detection is reliable and accurate, which in turn can improve the effectiveness of fraud detection algorithms. Due to the overwhelming amount of data, data cleaning specific for fraud detection is a very important activity for the auditor to find the appropriate information. Therefore, a new accounting data cleaning for fraud detection is needed. In this paper, an enhancement of the process of fraud detection by accounting auditors through the implementation of accounting data cleaning technique is proposed. The proposed technique was embedded in a prototype system called accounting data cleaning for fraud detection (ADCFD). Through experiment, the performance of the proposed technique through ADCF is compared with those obtained from the IDEA system, using the same dataset. The results show that the proposed enhanced technique through ADCFD system performed better than the IDEA system.
Advancing chronic pain relief cloud-based remote management with machine learning in healthcare
Authors: Mohankumar Nagarajan , Reddy Narani Sandeep , Asha Soundararajan , Arivazhagan Selvam , Rajanarayanan Subramanian , Padmanaban Kuppan , Murugan Subbiah
Published: November 2024

Healthcare providers face a significant challenge in the treatment of chronic pain, requiring creative responses to enhance patient outcomes and streamline healthcare delivery. It suggests using cloud-based remote management with machine learning (ML) to alleviate chronic pain. Wearable device data, electronic health record (EHR) data, and patient-reported outcomes are all inputs into the suggested system’s data analysis pipeline, which combines support vector machines (SVM) with recurrent neural networks (RNN). SVM’s powerful classification skills make it possible to classify patients’ risks and predict how they will react to therapy. RNNs are very good at processing sequential data, which means they may identify trends in patient symptoms and drug adherence over time. By integrating these algorithms, healthcare professionals may create individualized treatment programs that consider each patient’s preferences and specific requirements. Early intervention and proactive treatment of pain symptoms are made possible by the system’s ability to monitor patients in real-time remotely. The system is further improved by using predictive analytics to identify patients who could benefit from extra support services and to forecast when they will have acute pain episodes. The proposed approach can change the game regarding managing chronic pain. It provides data-driven, individualized treatment that improves patient outcomes while cutting healthcare expenses.
Navigating the smart contract threat landscape: a systematic review
Authors: Ibekwe Unyime Ufok , Mbanaso Uche M. , Nnanna Nwojo Agwu , Ibrahim Umar Adam
Published: November 2024

Smart contracts have emerged as a transformative technology within the blockchain ecosystem, facilitating the automated and trustless execution of agreements. Their adoption spans diverse sectors such as education, agriculture, healthcare, government, real estate, transportation, supply chain, and global initiatives like Central Bank Digital Currencies (CBDCs). However, the security of smart contracts has become a significant concern, as vulnerabilities in their design and implementation can lead to severe consequences such as financial losses and system failures. This systematic review consolidates findings from 78 selected research articles, identifying key vulnerabilities affecting smart contracts and categorizing them into a taxonomy encompassing code-level, environment-dependent, and user-related vulnerabilities. It also examines the threats that exploit these vulnerabilities and the most effective detection techniques. The domain-based classification presented in this review aims to assist researchers, software engineers, and developers in identifying and mitigating significant security flaws related to the design, implementation, and deployment of smart contracts. A comprehensive understanding of these issues is essential for enhancing the security and reliability of the blockchain ecosystem, ultimately fostering the development of more secure and robust decentralized applications for end users.
Detection of diabetic retinopathy and classification of its stages by using convolutional neural network
Authors: Gaur Sachin , Kandwal Anirudh , Pandey Bhaskar
Published: November 2024

Diabetes detection is pivotal in disease management and complication prevention. Traditional screening methods, like blood tests, are invasive and time-consuming. Deep learning has emerged as a non-invasive and automated alternative for diabetes detection. Convolutional neural networks (CNNs) excel in image analysis tasks, making them ideal for this purpose. This paper employs a CNN-based method for diabetes prediction using retinal images, utilizing the DenseNet169 architecture for feature extraction and diabetic retinopathy (DR) prediction. The APTOS 2019 blindness detection dataset from Kaggle, containing around 13,000 retinal images, is used for training. Pre-processing and normalization precede feature extraction, followed by the prediction of the DR stage. The model aims to classify retinal images into five stages of DR (0 to 4), ranging from no DR to proliferative DR. Our model achieved over 82% accuracy, outperforming advanced algorithms. Model evaluation includes accuracy, precision, recall, and F1 score measures.
Hydrophobicity signal analysis for robust SARS-CoV-2 classification
Authors: Jamhuri Mohammad , Irawan Mohammad Isa , Mukhlash Imam , Tri Puspaningsih Ni Nyoman
Published: November 2024

Rapid and accurate classification of viral pathogens is critical for effective public health interventions. This study introduces a novel approach using convolutional neural networks (CNN) to classify SARS-CoV-2 and non-SARS-CoV-2 viruses via hydrophobicity signal derived from DNA sequences. Conventional machine learning methods grapple with the variability of viral genetic material, requiring fixed-length sequences and extensive preprocessing. The proposed method transforms genetic sequences into image-based representations, enabling CNNs to handle complexity and variability without these constraints. The dataset includes 8,143 DNA sequences from seven coronaviruses, translated into amino acid sequences and evaluated for hydrophobicity. Experimental results demonstrate that the CNN model achieves superior performance, with an accuracy of over 99.84% in the classification task. The model also performs well with extended sequence lengths, showcasing robustness and adaptability. Compared to previous studies, this method offers higher accuracy and computational efficiency, providing a reliable solution for rapid virus detection with potential applications in bioinformatics and clinical settings.
Challenges of implementing protection systems in smart grids: a review
Authors: Anwari Sabat , Fauziah Dini , Lidyawati Lita
Published: November 2024

Based on the emergence of increasingly advanced technology, the conventional power grid can be upgraded to a smart grid by adding bidirectional communication, computer algorithms, and equipment that uses artificial intelligence (AI). A smart grid is a revolution in the current electricity network that can control the two-way generation and transmission process by utilizing an intelligent system so that the distribution of electric power can be handled optimally and in real time. The challenge of the smart grid is that there are distributed generators and microgrids that must be controlled in real time with rapidly changing loads. To meet these criteria, several points are proposed, i.e., finding an effective procedure to construct self-healing capability; developing a protection system based on AI; and proposing a systematic procedure to realize self-healing and protection systems with the help of a multi-agent system (MAS). Multi-agent systems are one of the AI approaches. Each agent can work independently and can also communicate with one another and with other devices on the network. Agents used as models can be classified into several categories, such as grid component agents, distributed resource agents, end-user agents, failure control agents, data analysis agents, and graphical visualization agents.
Optimizing dynamic response and stability of pressure-controlled swash plate type axial piston pump
Authors: Verma Vivek , Kumar Sachin , Anand Apurva
Published: November 2024

The main objective of the paper is to explore the role of lead-lag compensators in improving the performance of control systems for variable delivery hydraulic axial piston pumps (VAPP). These compensators offer a range of benefits, including stability enhancement, transient response optimization, frequency response modification, disturbance rejection, and robustness improvement. A mathematical model of the hydro-mechanical system is developed, and the transfer function for the dynamic system is established. The simulation of the model with lead-lag compensator significantly enhanced the phase margin to 55.70 and gain margin to 12.3 dB ensuring robust control for pressure-controlled VAPP, whereas the uncompensated system is marginally stable. The compensated system exhibits better transient and steady-state response. The optimized lead-lag compensated system achieves a maximum percentage overshoot of 12.1% and a settling time of 1.95 sec. This is a substantial improvement compared to the uncompensated system with a maximum % overshoot of 20.5% and a settling time of 2.39 sec. The improved response tends to induce greater damping (ζ) in the compensated system from 0.015 to 0.108 and increases leakage coefficient (K) from 3.38×10-12 m3/Pa.s to 24.34×10-12 m3/Pa.s. Optimized lag-lead compensator ensures stability, responsiveness adapting effectively to dynamic operating conditions of VAPP for aerospace application.
A deep learning model with an inductive transfer learning for forgery image detection
Authors: Bevinamarad Prabhu , H. Unki Prakash , Bhandage Venkatesh
Published: November 2024

ue to the availability of affordable electronic devices and several advanced online and offline multimedia content editing applications, the frequency of image manipulation has increased. In addition, the manipulated images are presented as evidence in courtrooms, circulated on social media and uploaded upon authentication to deceive the situation. This study implements a deep learning (DL) framework with inductive transfer learning (ITL) by using a pre-trained network to benefit from the discovered feature maps rather than starting from scratch and fine-tuning the process to check and classify whether the suspected image is authenticated or forged effectively. To experiment with the proposed model, we used both Columbian uncompressed image splicing detection (CUISD) and the CoMoFoD dataset for training and testing. We measured the model’s performance by changing hyperparameters and confirmed the better selection of values for the hyperparameter to yield compromised results. As per the evaluation results, our model showed improved results by classifying new instances of images with an average precision of 89.00%, recall of 86.43%, F1-score of 87.32, and accuracy of 87.72% and consistently performed better compared to other methods currently in use.
Modified-LSTM and feed forward neural network enabled resource allocation for 6G wireless networks
Authors: Kamble Pradnya , N. Shaikh Alam
Published: November 2024

The 6G wireless networks utilize terahertz (THz) frequency and intended to tremendously dynamic and diverse applications with deep learning enabled network, harvested significant attention and able to solve complex problems. Efficient resource allocation is a key requirement of next generation wireless networks. This research focuses on the resource allocation optimization challenge which includes storage, computing power, bandwidth and memory in the milieu of 6G wireless networks with device-to-device (D2D) communication enabled. The proposed model uses modified long short-term memory (mLSTM) and feed forward neural network to allocate resources to various tasks as per requirement such as information access, audio/video streaming, information access and productivity activity applications. The proposed work focuses on network parameters like channel noise, signal to noise ratio (SNR), distance from base station and includes D2D communication decisions to improve network performance. This research gives a novelty learning based solution for resource allocation for 6G wireless networks which contributes to the enhancement of next generation wireless communication networks. The lowest computing power utilized is 1%, Bandwidth utilized is 3% of total bandwidth and 2% storage.
Adversarially robust federated deep learning models for intrusion detection in IoT
Authors: Ennaji El Mahfoud , El Hajla Salah , Maleh Yassine , Mounir Soufyane
Published: November 2024

Ensuring the robustness, security, and privacy of machine learning is a pivotal objective, crucial for unlocking the complete potential of the internet of things (IoT). Deep neural networks have proven to be vulnerable to adversarial perturbations imperceptible to humans. These perturbations can give rise to adversarial attacks, leading to erroneous predictions by deep neural networks, particularly in intrusion detection within the IoT environment. This paper introduces a federated adversarial learning framework designed to protect both data privacy and deep neural network models. This framework consists of federated learning for data privacy and adversarial training on IoT devices to enhance model robustness. The experiments show that adversarial training at the Fog node devices significantly improves the robustness of a federated learning model against adversarial attacks when compared to normal training. Furthermore, the proposed adversarial deep federated learning model is validated using the Edge-IIoTset dataset, achieving an accuracy rate of 91.23% in the detection of attacks.
A novel CNN-ANN fusion approach for improved facial emotion detection
Authors: Sawant Viraj , Shaikh Husna , Palkar Bhakti , Kazi Sanam , Jasani Wasim , Rampurawala Lamiya , Naser Shaikh Mohammed
Published: November 2024

In recent years, the field of emotion recognition has witnessed an increased interest due to the rise of deep learning techniques. However, one of the persistent difficulties in this domain, which we have attempted to address, is the variability in image sizes utilized. In this study, we have reviewed the work by different researchers and summarized their key findings. In our research, we introduce a novel technique that integrates the strengths of 1D convolutional neural networks (CNNs) and artificial neural networks (ANNs) through a late fusion model, leveraging CNNs' shared weights and automatic feature learning for spatial and temporal data, alongside ANN's comprehensive feature consideration. Our research findings highlight the effectiveness of this approach, which achieves a remarkable accuracy of 92.42%, along with other evaluation metrics demonstrating notable results. Furthermore, we conduct a comprehensive analysis of the proposed method, comparing it with advanced methods in the field of facial emotion recognition. Through this comparative analysis, we demonstrate the superiority of our proposed approach, addressing challenges that have not yet been addressed till date, thus leading to progress in this field.
BEHIND THE LECTURE HALL OF THE ACADEME
Authors: Gumarang Bernardo K
Published: November 2024

"This book deals with the unspoken realities that confront not only the educators and students but the administration as well. Based on my sights and experiences, as well as issues presented by different parties, this book tackles the detrimental and oppressive bureaucracy that curtails the freedom in teaching and learning, the rat race for quick publications, and the high job-induced tension prevalent among most educators.
The book also addresses toxic professional environments found in institutions of higher education such as toxic behavior, a culture of perfectionism and related psychological problems. Each chapter addresses a distinct issue, examining both the nature of the problem and the extent to which it has developed within the academic context.
But it is not the only issues that will be lighted in this book—rather, it is a call to action. I encourage the readers to be more aware of these discrepancies and to help rectify the situation in the field of education. By talking about the different problems and personal experiences of people within a specific community, the book hopes to create awareness about the issue that needs addressing.These are problems that need to be faced and resolved collectively if we are to build an environment within the academic sphere that is more just and equitable."
The myth(s) of a ‘balanced’ approach
Authors: Munter Charles
Published: December 2024

How we should teach mathematics has long been debated by advocates of different instructional approaches. These debates can lead to tensions that are difficult to navigate for those who are new to (or even veterans of) the profession. Educators often propose a “balance” of different approaches as a solution. Charles Munter considers whether such a balance is actually possible — and also considers whether some other questions may be more urgent.
CAREER CONFIDENTIAL: How do we help students overcome the 'I can'ts'?
Authors: Sackstein Starr
Published: December 2024

In Career Confidential, Starr Sackstein offers professional advice for educators. In this edition, an instructional coach wants to help an art teacher whose students think they’re no good at art.
“This Is Not Our Culture”: Probing the African Bishops’ Use of the Cultural Argument
Authors: Mben Joseph L.
Published: December 2024

In their response to the declaration Fiducia Supplicans, many African bishops used culture as an argument to reject the possibility of blessing same-sex couples. This article probes and shows the extent of the inconsistency of the appeal to culture by the African bishops. It uses the issue of polygamy as a point of contrast: appeals to African culture are never used to justify this widespread practice among Africans or any pastoral flexibility toward it. The article also compares Vatican II’s understanding of culture ( Gaudium et Spes) with that of the African bishops. It shows that the African bishops’ understanding of culture tends to be narrow and focused on the past. Finally, the article looks at culture as a source of Christian morals and demonstrates that culture can be only a secondary source.
From Disappearance to Hope: The Construction of the Brazilian Indigenous Movement’s Imaginary (1974-1977)
Authors: Benitez Trinidad Carlos , Soares dos Santos Bicalho Poliene
Published: December 2024

This article analyzes the construction of the imaginary created by the Brazilian Indigenous Movement against the historical representations imposed by the non-indigenous, of disappearance, and backwardness. It is based on the study of the speeches of the assemblies of Indigenous chiefs between 1974 and 1977. The crisis of institutional Indigenism, military authoritarianism, and developmentalism announced the extinction of Indigenous peoples. Faced with ethnocidal integrationism, the Indigenous chiefs had to deal with the challenge of ethnic differences, external influence, and dehumanizing stereotypes to build a new ideological framework. This research focuses on the mechanisms that led from an imaginary of disappearance to one of hope in a context of aggressive growth of neoliberal threats against Indigenous lands.
Coparenting perceptions of (Grand)parents and associations with demographic characteristics and early adolescent problem behavior in China
Authors: Luo Na , Bai Bin , Van Leeuwen Karla
Published: December 2024

Prior studies on coparenting have primarily focused on mothers and fathers, neglecting coparenting dynamics in multi-generational families. This study aimed to investigate coparenting perceptions among mothers, fathers and grandparents, and explore the associations between the coparenting relationship and adolescent problem behavior, as well as demographic characteristics. The sample included 1163 families with children aged 10–13 years. Mothers ( n = 997) and fathers ( n = 863) provided ratings of their coparenting relationship toward their partners and one grandparent, while grandparents ( n = 249) reported on the coparenting relationship with mothers. Results showed that compared to mothers, fathers reported more undermining of their own parenting by their partner. They were more supportive of their partner’s parenting and more positive about the division of labor. Fathers rated the overall coparenting relationship more positively than mothers, as indicated by the General Coparenting Scale. Notably, grandparents reported significantly more positive coparenting relations with mothers than mothers’ reports of coparenting with grandparents, expressing affirmation of parental competency and alignment with parenting practices. More positive coparenting was associated with less problem behaviors, which was true for coparenting across parents and grandparents. Furthermore, older adolescent age and higher socioeconomic status were significantly correlated with increased coparenting agreement and closeness. Maternal and paternal grandmothers did not differ in their ratings of the coparenting relationship with the child’s mother. These findings underscore the significance of recognizing differential perceptions among family members regarding coparenting behaviors, providing valuable insights for tailoring specific recommendations to enhance coparenting quality within diverse families.