bluuebunny/crossref_metadata_2025 huggingface dataset:
The Neuropeptide HGAP Regulates Growth, Reproduction, Metamorphosis, Tissue Damage Repair, and Response against Starvation in Pacific Abalone
Authors: Hanif Md Abu , Hossen Shaharior , Cho Doo Hyun , Kho Kang Hee
Published: December 2023

Introduction: Neuropeptides regulate vital physiological processes in multicellular organisms, including growth, reproduction, metamorphosis, and feeding. Recent transcriptome analyses have revealed neuropeptide genes with potential roles in vertebrate and invertebrate growth and reproduction. Among these genes, haliotid growth-associated peptide (HGAP) was identified as a novel gene in abalone. Methods: This study focused on HGAP in Pacific abalone (Haliotis discus hannai), where the complete cDNA sequence named Hdh-HGAP was identified and characterized. Samples from different experiments, such as metamorphosis, juvenile abalone growth, gonad development stages, muscle remodeling, and starvation, were collected for mRNA expression analysis. Results: The sequence spans 552 bp, encoding 96 amino acids with a molecular weight of 10.96 kDa. Expression analysis revealed that Hdh-HGAP exhibited higher levels in muscle tissue. Notably, during metamorphosis, Hdh-HGAP exhibited greater expression in the trochophore, veliger, and juvenile stages than in the cell division stages. Regarding growth patterns, Hdh-HGAP was highly expressed during rapid growth compared to stunted, minimal, and normal growth. In gonadal development, Hdh-HGAP mRNA reached its highest expression level during the ripening stage, indicating a potential role in gonadal cell proliferation and maturation. The in vivo effects of GnRH on gonad development and the expression of the Hdh-HGAP neuropeptide indicate its involvement in regulating reproduction in Pacific abalone. While tissue remodeling is primarily governed by immune genes, Hdh-HGAP was also upregulated during muscle tissue remodeling. Conversely, Hdh-HGAP was downregulated during prolonged starvation. Conclusion: This study marks the first comprehensive exploration of the Hdh-HGAP neuropeptide gene in Pacific abalone, shedding light on its involvement in growth, reproduction, metamorphosis, tissue remodeling, and response to starvation, although regulatory mechanisms are mostly unknown.
Advancing Treatments for Rare Renal Diseases: New Hopes and Opportunities to Address a High Unmet Need
Authors: Garrisi Davide , Bevan Andrew , Angeles Carmichael
Published: December 2023

Introduction: The etiology of chronic kidney disease (CKD) is varied and complex. Diabetes and hypertension comprise 2/3 of cases and rare conditions, including inherited genetic diseases, comprise 1/3. We previously reported a 54% increase in clinical studies in CKD in the last 10 years. Hypothesizing a greater increase in rare renal disease studies, we undertook further analysis of metadata from Clinicaltrials.gov. Methods: CT.gov was searched for 49 conditions determined to be rare renal diseases posted between Jan-2003 and Dec-2022. Studies were divided into 2 time periods: P1 (2003–2012) and P2 (2013–2022) and analyzed by study type, phase, indication, primary endpoint, population, and funding. Results: Studies increased significantly in P2 versus P1 (123%, p < 0.001) with the greatest rise in observational studies (283%, p < 0.001). Interventional studies increased 93% (p < 0.01), with the greatest rise in early phases (205%, p < 0.001). The most frequent indications were LN, ADPKD, and IgA nephropathy; all increased 77–166% in P2 (p < 0.05 – p < 0.001). Proteinuria was the most frequent primary endpoint, which increased 63% (p = 0.054). Studies with pediatric populations increased 78% (p < 0.01). Most studies were nonindustry funded; however, industry-funded studies increased by 225% (p < 0.001). Conclusion: Clinical research in rare renal diseases has increased significantly in the last 10 years, particularly in glomerular diseases (GDs) and ADPKD. Proteinuria correlates with outcomes in GD, which explains the high percentage of studies with this primary endpoint. Rare renal diseases disproportionately affect children and the rise in studies with pediatric populations is encouraging. The rise in observational studies may signal an increased focus on understanding the natural course and pathophysiology of disease, which may lead to new potential therapeutic targets and future interventional studies. The increase in industry-funded studies suggests basic science is translating into industry-sponsored research.
Attention mechanism-based model for cardiomegaly recognition in chest X-Ray images
Authors: El Omary Sara , Lahrache Souad , El Ouazzani Rajae
Published: December 2023

Recently, cardiovascular diseases (CVDs) have become a rapidly growing problem in the world, especially in developing countries. The latter are facing a lifestyle change that introduces new risk factors for heart disease, that requires a particular and urgent interest. Besides, cardiomegaly is a sign of cardiovascular diseases that refers to various conditions; it is associated with the heart enlargement that can be either transient or permanent depending on certain conditions.Furthermore, cardiomegaly is visible on any imaging test including Chest X-Radiation (X-Ray) images; which are one of the most common tools used by Cardiologists to detect and diagnose many diseases. In this paper, we propose an innovative deep learning (DL) model based on an attention module and MobileNet architecture to recognize Cardiomegaly patients using the popular Chest X-Ray8 dataset. Actually, the attention module captures the spatial relationship between the relevant regions in Chest X-Ray images. The experimental results show that the proposed model achieved interesting results with an accuracy rate of 81% which makes it suitable for detecting cardiomegaly disease.
Speaker identification under noisy conditions using hybrid convolutional neural network and gated recurrent unit
Authors: Anito Wondimu Lambamo , Srinivasagan Ramasamy , Jifara Worku , Alzahrani Ali
Published: December 2023

Speaker identification is biometrics that classifies or identifies a person from other speakers based on speech characteristics. Recently, deep learning models outperformed conventional machine learning models in speaker identification. Spectrograms of the speech have been used as input in deep learning-based speaker identification using clean speech. However, the performance of speaker identification systems gets degraded under noisy conditions. Cochleograms have shown better results than spectrograms in deep learning-based speaker recognition under noisy and mismatched conditions. Moreover, hybrid convolutional neural network (CNN) and recurrent neural network (RNN) variants have shown better performance than CNN or RNN variants in recent studies. However, there is no attempt conducted to use a hybrid CNN and enhanced RNN variants in speaker identification using cochleogram input to enhance the performance under noisy and mismatched conditions. In this study, a speaker identification using hybrid CNN and the gated recurrent unit (GRU) is proposed for noisy conditions using cochleogram input. VoxCeleb1 audio dataset with real-world noises, white Gaussian noises (WGN) and without additive noises were employed for experiments. The experiment results and the comparison with existing works show that the proposed model performs better than other models in this study and existing works.
Photoplethysmogram signal reconstruction through integrated compression sensing and basis function aware shallow learning
Authors: Muniraju Usha , Kumaran Thangamuthu Senthil
Published: December 2023

The transmission of photoplethysmogram (PPG) signals in real-time is extremely challenging and facilitates the use of an internet of things (IoT) environment for healthcare- monitoring. This paper proposes an approach for PPG signal reconstruction through integrated compression sensing and basis function aware shallow learning (CSBSL). Integrated-CSBSL approach for combined compression of PPG signals via multiple channels thereby improving the reconstruction accuracy for the PPG signals essential in healthcare monitoring. An optimal basis function aware shallow learning procedure is employed on PPG signals with prior initialization; this is further fine-tuned by utilizing the knowledge of various other channels, which exploit the further sparsity of the PPG signals. The proposed method for learning combined with PPG signals retains the knowledge of spatial and temporal correlation. The proposed Integrated-CSBSL approach consists of two steps, in the first step the shallow learning based on basis function is carried out through training the PPG signals. The proposed method is evaluated using multichannel PPG signal reconstruction, which potentially benefits clinical applications through PPG monitoring and diagnosis.
Hybrid channel and spatial attention-UNet for skin lesion segmentation
Authors: Gadag Soumya , Palraj Pradeepa
Published: December 2023

Melanoma is a type of skin cancer which has affected many lives globally. The American Cancer Society research has suggested that it a serious type of skin cancer and lead to mortality but it is almost 100% curable if it is detected and treated in its early stages. Currently automated computer vision-based schemes are widely adopted but these systems suffer from poor segmentation accuracy. To overcome these issue, deep learning (DL) has become the promising solution which performs extensive training for pattern learning and provide better classification accuracy. However, skin lesion segmentation is affected due to skin hair, unclear boundaries, pigmentation, and mole. To overcome this issue, we adopt UNet based deep learning scheme and incorporated attention mechanism which considers low level statistics and high-level statistics combined with feedback and skip connection module. This helps to obtain the robust features without neglecting the channel information. Further, we use channel attention, spatial attention modulation to achieve the final segmentation. The proposed DL based scheme is instigated on publically available dataset and experimental investigation shows that the proposed Hybrid Attention UNet approach achieves average performance as 0.9715, 0.9962, 0.9710.
Deep learning method for lung cancer identification and classification
Authors: Jamdar Sahil , Vaddin Jayashree , B. Nargundkar Sachidanand , Patil Shrinivasa
Published: December 2023

Lung cancer (LC) is calming many lives and is becoming a serious cause of concern. The detection of LC at an early stage assists the chances of recovery. Accuracy of detection of LC at an early stage can be improved with the help of a convolutional neural network (CNN) based deep learning approach. In this paper, we present two methodologies for Lung cancer detection (LCD) applied on Lung image database consortium (LIDC) and image database resource initiative (IDRI) data sets. Classification of these LC images is carried out using support vector machine (SVM), and deep CNN. The CNN is trained with i) multiple batches and ii) single batch for LC image classification as non cancer and cancer image. All these methods are being implemented in MATLAB. The accuracy of classification obtained by SVM is 65%, whereas deep CNN produced detection accuracy of 80% and 100% respectively for multiple and single batch training. The novelty of our experimentation is near 100% classification accuracy obtained by our deep CNN model when tested on 25 Lung computed tomography (CT) test images each of size 512×512 pixels in less than 20 iterations as compared to the research work carried out by other researchers using cropped LC nodule images.
K-centroid convergence clustering identification in one-label per type for disease prediction
Authors: Hoang Minh Long , Delmonte Nicola
Published: December 2023

Disease prediction is a high demand field which requires significant support from machine learning (ML) to enhance the result efficiency. The research works on application of K-means clustering supervised classification in disease prediction where each class only has one labeled data. The K-centroid convergence clustering identification (KC3I) system is based on semi-K-means clustering but only requires single labeled data per class for the training process with the training dataset to update the centroid. The KC3I model also includes a dictionary box to index all the input centroids before and after the updating process. Each centroid matches with a corresponding label inside this box. After the training process, each time the input features arrive, the trained centroid will put them to its cluster depending on the Euclidean distance, then convert them into the specific class name, which is coherent to that centroid index. Two validation stages were carried out and accomplished the expectation in terms of precision, recall, F1-score, and absolute accuracy. The last part demonstrates the possibility of feature reduction by selecting the most crucial feature with the extra tree classifier method. Total data are fed into the KC3I system with the most important features and remain the same accuracy.
A novel ensemble model for detecting fake news
Authors: Bensouda Nissrine , El Fkihi Sanaa , Faizi Rdouan
Published: December 2023

Due the growing proliferation of fake news over the past couple of years, our

objective in this paper is to propose an ensemble model for the automatic classification of article news as being either real or fake. For this purpose, we opt

for a blending technique that combines three models, namely bidirectional long

short-term memory (Bi-LSTM), stochastic gradient descent classifier and ridge

classifier. The implementation of the proposed model (i.e. BI-LSR) on real

world datasets, has shown outstanding results. In fact, it achieved an accuracy

score of 99.16%. Accordingly, this ensemble learning has proven to do perform

better than individual conventional machine learning and deep learning models

as well as many ensemble learning approaches cited in the literature.
Convolutional neural network with binary moth flame optimization for emotion detection in electroencephalogram
Authors: Alwan Tuib Tabarek , Saoudi Baydaa Hadi , Hussein Yaqdhan Mahmood , Mandeel Thulfiqar H. , Al-Dhief Fahad Taha
Published: December 2023

Electroencephalograph (EEG) signals have the ability of real-time reflecting brain activities. Utilizing the EEG signal for analyzing human emotional states is a common study. The EEG signals of the emotions aren’t distinctive and it is different from one person to another as every one of them has different emotional responses to same stimuli. Which is why, the signals of the EEG are subject dependent and proven to be effective for the subject dependent detection of the Emotions. For the purpose of achieving enhanced accuracy and high true positive rate, the suggested system proposed a binary moth flame optimization (BMFO) algorithm for the process of feature selection and convolutional neural networks (CNNs) for classifications. In this proposal, optimum features are chosen with the use of accuracy as objective function. Ultimately, optimally chosen features are classified after that with the use of a CNN for the purpose of discriminating different emotion states. 
Artificial intelligence research in Nigeria: Topic modelling and scientometric analysis
Authors: Tolulope Afolabi Ibukun , Isaac Martins , Timileyin Owoseni , Seth Samuel , Kingsley Oputa
Published: December 2023

In developing countries such as Nigeria, artificial intelligence (AI) research has the potential to drive rapid advancement in various aspects of development, including the economy and technology. However, it is crucial to understand the focus of Nigerian AI researchers and identify unexplored areas of research that could lead to unprecedented development. To address this need, we used natural language processing, machine learning, and statistical algorithms to investigate the main areas of interest of Nigerian AI researchers. We identified ten topics and used scientometric analyses to reveal key concepts, keyword co-occurrences, and authorship networks. Our study found that Covenant University was the most prolific institution, with 375 publications, followed by the Federal University of Technology with 135 publications and the University of Ibadan with 121 publications. Overall, our research provides valuable insights into the structure and progression of AI research in Nigeria and highlights areas for improvement.
Application of machine learning in chemical engineering: outlook and perspectives
Authors: Al Sharah Ashraf , Abu Owida Hamza , Alnaimat Feras , Abuowaida Suhaila
Published: December 2023

Chemical engineers' formulation, development, and stance processes all heavily rely on models. The physical and economic consequences of these decisions can have disastrous effects. Attempts to employ a hybrid form of artificial intelligence for modeling in various disciplines. However, they fell short of expectations. Due to a rise in the amount of data and computational resources during the previous five years. A lot of recent work has gone into developing new data sources, indexes, chemical interface designs, and machine learning algorithms in an effort to facilitate the adoption of these techniques in the research community. However, there are some important downsides to machine learning gains. The most promising uses for machine learning are in time-critical tasks like real-time optimization and planning that require extreme precision and can build on models that can self-learn to recognize patterns, draw conclusions from data, and become more intelligent over time. Due to their limited exposure to computer science and data analysis, the majority of chemical engineers are potentially vulnerable to the development of artificial intelligence. But in the not-too-distant future, chemical engineers' modeling toolbox will include a reliable machine learning component.
Segmentation and classification techniques used to detect early stroke diagnosis using brain magnetic resonance imaging: a review
Authors: Kandaya Shaarmila , Abdullah Abdul Rahim , Saad Norhashimah Mohd , Muda Ahmad Sobri , Ahmad Sabri Muhammad Izzat
Published: December 2023

Stroke is a leading cause of disability and death worldwide. Early diagnosis and treatment are crucial in reducing the risk of stroke-related complications. Brain magnetic resonance imaging (MRI) is a common diagnostic tool used for stroke evaluation. However, manual interpretation of MRI images can be time-consuming and subjective. Machine learning (ML) algorithms have shown promise in automating and improving stroke diagnosis accuracy. This article focuses on classification and segmentation techniques used to detect early stroke diagnosis using brain magnetic imaging. The diagnosis, treatment, and prognosis of complications and patient outcomes in a number of neurological diseases are currently made possible by ML through pattern recognition algorithms. However, the use of MRI is limited because of MRI plays an important role in diagnosing lumbar disc disease. However, the use of MRI is limited due to its high cost and significant operational and processing time. More importantly, MRI is contraindicated in some patients who are claustrophobic or have pacemakers due to the potential for damage. Recent studies have shown that treatment within six hours of a stroke can save a patient's life. Unfortunately, Malaysia is facing a shortage of neuroradiologists, hampering efforts to treat its growing number of stroke patients.
Potential directions on coronary artery disease prediction using machine learning algorithms: A survey
Authors: Vijayaraj Anu Ragavi , Pasupathi Subbulakshmi
Published: December 2023

Coronary artery disease (CAD) is the most ubiquitous and protuberant cause of fatal death. The hit in mortality rate is because of certain lifestyle variables including unhealthy diet, usage of tobaccos and drugs, physical inactivity, and environmental pollution. Traditional screening tests including computed tomography, angiography, electrocardiography, and magnetic resonance imaging are employed for diagnosis and would necessitate more manpower. Machine learning (ML) has been utilized in healthcare to create early predictions from massive volumes of data. The Scopus, Web of Science databases were exhaustively searched utilizing a search strategy that comprised CAD prediction, cardiac illness detection, and heart disease categorization. After applying the inclusion and exclusion criteria to the 99 articles obtained, the population of the study was composed of 30 articles. This review study offers an organized look at the articles published in ML-based CAD detection and classification models that include clinical variables. The use of ML could produce amazing results in CAD detection, as evidenced by the classifiers random forest, decision tree, and k-nearest-neighbour with accuracy being >90%. The use of ML in CAD diagnosis lowers false-positive, and false-negative errors, and presents a special opportunity by providing patients quick, and affordable diagnostic services.
Enhancements in the world of digital forensics
Authors: Vaddi Krishna Sanjay , Kamble Dhwaniket , Vaingankar Raj , Khatri Tushar , Bhalerao Pranil
Published: December 2023

Currently, the rapid advancement of computer systems and mobile phones has resulted in their utilization in unlawful acts. Ensuring adequate and effective security measures poses a difficult task due to the intricate nature of these devices, thereby exacerbating the challenges associated with investigating crimes involving them. Digital forensics, which involves investigating cyber crimes, plays a crucial role in this realm. Extensive research has been conducted in this field to aid forensic investigations in addressing contemporary obstacles. This paper aims to explore the progress made in the applications of digital forensics and security, encompassing various aspects, and provide insights into the evolution of digital forensics over the past five years.
Deep self-taught learning framework for intrusion detection in cloud computing environment
Authors: Vaiyapuri Thavavel , Binbusayyis Adel
Published: December 2023

Cloud has become a target-rich environment for malicious attacks by cyber intruders. Security is a major concern and remains an obstacle to the adoption of cloud computing. The intrusion detection system (IDS) is regarded as defense-in-depth. Unfortunately, most machine learning approaches designed for cloud intrusion detection require large amounts of labeled attack samples, but in real practice, they are limited. Therefore, the key impetus of this work is to introduce self-taught learning (STL) combining stacked sparse autoencoder (SSAE) with long short-term memory (LSTM) as a candidate solution to learn the robust feature representation and efficiently improve the performance of IDS with respect to false alarm rate (FAR) and detection rate (DR). Accordingly, the proposed approach as a first step employs SSAE to achieve dimensional reduction by learning the discriminative features from network traffic. The approach adopts LSTM to recognize the intrusion with the features encoded by SSAE. To evaluate the detective performance of our model, a comprehensive set of experiments are conducted on NSL-KDD. Also, ablation experiments are conducted to show the contribution of each component of our approach. Further, the comparative analysis shows the efficacy of our approach against the existing approaches with an accuracy of 86.31%.
Performance analysis of congestion-aware Q-routing algorithm for network on chip
Authors: Srivastava Smriti , Moharir Minal , Gunisetty Shivaneetha
Published: December 2023

A network on chip’s performance is greatly impacted by network congestion due to the substantial increase in latency and energy utilized. Designing routing strategies that keep the network informed of the status of traffic is made easier by machine learning techniques. In this work, a reinforcement-based congestion-aware Q-routing (CAQR) technique has been presented. The proposed algorithm performed better in comparison to the conventional XY routing method tested against the SPEC CPU2006 benchmark suite in the gem5 NoC simulator tool. The suite used has 4 benchmarks, namely, namd, lbm, leslie3d and bzip2 which can be used for the cores in the network in any combination. The tests were run with 16 cores on a 44 network with the maximum instruction count supported by the system (here 5,000). The proposed Q-routing algorithm showed an average of 19% reduction for benchmark simulation as compared to the Dimension-ordered (X-Y) routing for readings of average packet latency which is a crucial factor in determining a network’s efficiency. The analysis also shows an average reduction of 24%, 10%, 23% and 47% in terms of average packet network latency, average flit latency, average flit network latency and average energy consumption across various benchmarks.
A computational intelligent analysis of autism spectrum disorder using machine learning techniques
Authors: Mareeswaran Murali Anand , Selvarajan Kanchana
Published: December 2023

Children between the ages of 12 and 24 months who have autism spectrum disorder (ASD) experience abnormalities in the brain that result in undesirable symptoms. Children with ASD struggle to comprehend what others are trying to say and or feel, and they experience extreme anxiety in social situations. Additionally, they have a hard time making friends and even living independently. The defective genes, which control the brain and govern how brain cells communicate with one another, are the primary cause of ASD because they alter brain function. Our primary goal is to assist therapists and parents of children with ASD in using current technologies, such as human intelligence and artificial intelligence, to treat ASD and assist those youngsters in obtaining better social interaction and societal integration. For the purpose of doing an early analysis of ASD, the data is divided into the following three categories: age, gender, and jaundice symptoms. The performance of machine learning algorithms can be influenced by a variety of factors, such as the size of the dataset and quality of the dataset, the choice of features, and the tuning of hyper-parameters. In this work, the support vector machine (SVM) yields 96% as the highest classification accuracy.
Predicting the outcome of regional development projects using machine learning
Authors: Satri Jihad , El Mokhi Chakib , Hachimi Hanaa
Published: December 2023

Morocco, in its pursuit of inclusive and sustainable territorial development, initiated the advanced regionalization experiment over six years ago. The primary challenge facing government officials today is the management of a burgeoning number of regional development projects. In this article we developed a predictive model based on artificial intelligence and Machine Learning to predict the outcomes of regional development projects, in order to identify the risks associated with their potential failure, and anticipate their impact. To accomplish this, we implemented various data mining techniques and classification algorithms. We collected and analyzed data from past and ongoing regional development projects, considering diverse factors that influence their success or failure. Through rigorous experimentation, we assessed the effectiveness of different predictive models. Our findings reveal that the Random Forest classifier stands out as an efficient algorithm for predicting the outcomes of regional development projects. This research contributes to the broader discourse on the practical implementation of artificial intelligence in public policy and regional development, showcasing its potential to optimize resource allocation, and alleviate the burden of repetitive administrative tasks for organizationsoperating with limited resources.
Optimized deep learning-based dual segmentation framework for diagnosing health of apple farming with the internet of things
Authors: Raju Harsha , Narasimhaiah Veena Kalludi
Published: December 2023

The high disease prevalence in apple farms results in decreased yield and income. This research addresses these issues by integrating internet of things (IoT) applications and deep neural networks to automate disease detection. Existing methods often suffer from high false positives and lack global image similarity. This study proposes a conceptual framework using IoT visual sensors to mitigate apple diseases' severity and presents an intelligent disease detection system. The system employs the augmented Otsu technique for region-aware segmentation and a colour-conversion algorithm for generating feature maps. These maps are input into U-net models, optimized using a genetic algorithm, which results in the generation of suitable masks for all input leaf images. The obtained masks are then used as feature maps to train the convolution neural network (CNN) model for detecting and classifying leaf diseases. Experimental outcomes and comparative assessments demonstrate the proposed scheme's practical utility, yielding high accuracy and low false-positive results in multiclass disease detection tasks.
A method for missing values imputation of machine learning datasets
Authors: Hanyf Youssef , Silkan Hassan
Published: December 2023

In machine learning applications, handling missing data is often required in the pre-processing phase of datasets to train and test models. The class center missing value imputation (CCMVI) is among the best imputation literature methods in terms of prediction accuracy and computing cost. The main drawback of this method is that it is inadequate for test datasets as long as it uses class centers to impute incomplete instances because their classes should be assumed as unknown in real-world classification situations. This work aims to extend the CCMVI method to handle missing values of test datasets. To this end, we propose three techniques: the first technique combines the CCMVI with other literature methods, the second technique imputes incomplete test instances based on their nearest class center, and the third technique uses the mean of centers of classes. The comparison of classification accuracies shows that the second and third proposed techniques ensure accuracy close to that of the combination of CCMVI with literature imputation methods, namely k-nearest neighbors (KNN) and mean methods. Moreover, they significantly decrease the time and memory space required for imputing test datasets.
Source printer identification using convolutional neural network and transfer learning approach
Authors: F. El Abady Naglaa , H. Zayed Hala , Taha Mohamed
Published: December 2023

In recent years, Source printer identification has become increasingly important for detecting forged documents. A printer's distinguishing feature is its fingerprints. Each printer has a unique collection of fingerprints on every printed page. A model for identifying the source printer and classifying the questioned document into one of the printer classes is provided by source printer identification. A paper proposes a new approach that trains three different approaches on the dataset to choose the more accurate model for determining the printer's source. In the first, some pre-trained models are used as feature extractors, and support vector machine (SVM) is used to classify the generated features. In the second, we construct a two-dimensional convolutional neural network (2D-CNN) to address the source printer identification (SPI) problem. Instead of SoftMax, 2D-CNN is employed for feature extractors and SVM as a classifier. This approach obtains 93.75% 98.5% accuracy for 2D-CNN-SVM in the experiments. The SVM classifier enhanced the 2D-CNN accuracy by roughly 5% over the initial configuration. Finally, we adjusted 13 already-pre-trained CNN architectures using the dataset. Among the 13 pre-trained CNN models, DarkNet-19 has the greatest accuracy of 99.2 %. On the same dataset, the suggested approaches achieve well in terms of classification accuracy than the other recently released algorithms. 
Signature verification based on proposed fast hyper deep neural network
Authors: Hashim Zainab , Mohsin Hanaa , Alkhayyat Ahmed
Published: December 2023

Many industries have made widespread use of the handwittern signature verification system, including banking, education, legal proceedings, and criminal investigation, in which verification and identification are absolutely necessary. In this research, we have developed an accurate offline signature verification model that can be used in a writer-independent scenario. First, the handwitten signature images went through four preprocessing stages in order to be suitable for finding the unique features. Then, three different types of features namely principal component analysis (PCA) as appearance-based features, gray-level co-occurrence matrix (GLCM) as texture-features, and fast Fourier transform (FFT) as frequency-features are extracted from signature images in order to build a hybrid feature vector for each image. Finally, to classify signature features, we have designed a proposed fast hyper deep neural network (FHDNN) architecture. Two different datasets are used to evaluate our model these are SigComp2011, and CEDAR datasets. The results collected demonstrate that the suggested model can operate with accuracy equal to 100%, outperforming several of its predecessors. In the terms of (precision, recall, and F-score) it gives a very good results for both datasets and exceeds (1.00, 0.487, and 0.655 respectively) on Sigcomp2011 dataset and (1.00, 0.507, and 0.672 respectively) on CEDAR dataset.
Control theory for skewed distribution under operation side of the telecommunication industry and hard-bake process in the semiconductor manufacturing process
Authors: Zaka Azam , Jabeen Riffat , Ahmad Mashhood , Aljohani Hassan M , Helmi Maha M
Published: December 2023

Statistical process control basically involves inspecting a random sample of the output from a process and deciding whether the process is producing products with characteristics that fall within a predetermined range. It is used extensively in the field of reliability engineering. The reliability of the production process is thoroughly monitored for any internal variation using the SPC. The aim is always to settle such variations through a proper control monitoring. If the underlying distribution of the process is known to the researcher than the use of parametric control charts are useful but in many cases when there is doubt about the distribution of the process then it is preferred to use non parametric control charts. In this paper we propose the modified Exponentially weighted moving average (EWMA), Double Exponentially weighted moving average (DEWMA), Hybrid Exponentially weighted moving average (HEWMA), Extended Exponentially weighted moving average (EEWMA), Modified Exponentially weighted moving average (MEWMA) and mix- type control charts by mixing these control charts with Tukey control chart EWMA-TCC, DEWMA-TCC, HEWMA-TCC, EEWMA-TCC, MEWMA-TCC for the shape parameter of the Kumaraswamy Lehmann-2 Power function distribution (KL2PFD).
Racial Differences in Retention in a Community-based Addiction Reduction Program Implemented by Peer Recovery Coaches for Women of Reproductive Age
Authors: Yakovlyeva Anastasiya , Griffin Wilma , Worden Allison , Roth Sarah , DeChant Paige , Butler Dawn , Tang Qing , Litzelman Debra K.
Published: December 2023

To identify program characteristics that influence the retention of women of reproductive age in the Community-based Addiction Reduction program (CARE), mixed-method analyses of CARE survey data and CARE Peer Recovery Coach (PRC) narrative entries of participant encounters were performed. About 251 women were enrolled in this prospective community-based implementation science intervention. We compared survey responses by race for treatment status, treatment motivation scales, and retention in the program at intake, 2-, 6-, 9-, and 12-month follow ups using Chi-square/ T-tests. Qualitative analysis of PRC narrative entries was conducted following thematic analysis and crystallization immersion analytic methods. White compared with Black women in CARE were significantly more likely to be in treatment at intake ( P < .001) and more motivated to engage in recovery treatment ( P < .001). However, Black women were retained longer in CARE at 2- ( P < .006), 6- ( P < .011), and 9- ( P < .004) months. PRC narrative entries were coded, and emergent themes mapped well to the 4 types of supports provided by PRC as outlined by the Substance Abuse and Mental Health Services Administration: emotional, instrumental, informational, and affiliational. Analysis of narrative entries by race revealed that Black women were given more detailed information, communications with PRC were more encouraging and proactive in identifying and meeting needs, and PRC took a more hands-on approach when assisting and linking to resources. The inclusion of PRC as integral members of SUD recovery programs may preferentially provide Black women with SUD the opportunity to build more trusting relationships with these peer coaches, thereby increasing their participation and retention.
(Un)stated assumptions: values, ethics, and the System of Radiological Protection
Authors: Velshi R.
Published: December 2023

The issues of what is ‘safe’ and what is an ‘unreasonable risk’ pre-occupy regulators each and every day. Western science alone is inadequate as a guide. Good advice and good decision-making require consideration of Western science in tandem with traditional knowledge, our personal and organisational ethics and values, and our shared experiences. As the global information environment grows increasingly complex, it is more crucial than ever that international organisations, such as ICRP, and national regulators, such as the Canadian Nuclear Safety Commission, are clear about their ethical guideposts and their core values. Clarity in these anchors allows policy makers and regulators to communicate the rationale for decisions more clearly, and to combat disinformation and misinformation more effectively – a challenge that demands action from us all. As we look towards revising the System of Radiological Protection and implementing it in international and national regulatory systems around the world, codifying and communicating our underpinning assumptions will clearly enhance our credibility and trust, and will enable more effective combination of the guidance offered by the System of Radiological Protection with relevant national considerations, including factors such as societal risk tolerance and indigenous knowledge.
A first report on the distribution of Ostracod taxa in the Ashtamudi Lake, southwest coast of Kerala, India
Authors: Radhakrishnan K. , Hussain S.M. , Prakasheswar P. , Augustine Desna Mary , Vijayaraman
Published: December 2023

The present study is the first taxonomical account of Ostracoda occurring in the sediments of the Ashtamudi Lake in Kerala, intending to generate a baseline for future palaeo-ecological or palaeoenvironmental applications. A total of 22 Ostracod species belonging to 16 genera, 11 families, 5 superfamilies and 2 suborders of the order Podocopida have been identified from 66 surface sediment samples collected across the lake. Sedimentological parameters, such as CaCO3, organic matter, sand silt and clay, were estimated and their distribution is discussed. The ratio between the carapaces and open valves has been taken into consideration for determining the rate of sedimentation in the study area.
Spatial distribution of pteropods from North Andaman Sea: Insights into aragonite supply and deposition in low saline, delta shelf regimes
Authors: Panchang Rajani , Nigam Rajiv , Ambokar Mugdha
Published: December 2023

Pteropods are important components of the marine zooplankton. Not only are they crucial parts of the pelagic ecosystem but are also key contributors to the global carbon budget. Their shells sink to the ocean floor contributing to the inorganic carbon supply to the ocean floor. Being aragonitic, they easily dissolve in oceans receiving fresh water. They also dissolve as they sink beyond the Aragonite Saturation Zone, that is, within the top 500 m of the water column, releasing the trapped CO2 into the surrounding waters. Their taxonomic and quantitative distribution on the modern ocean floor can help strengthen estimates of inorganic carbon supply in shallow oceans, which are also susceptible to climate-induced dissolution. The present study, for the first time, reports 18 pteropod taxa, their abundances and spatial distribution from the surface sediments of the Ayeyarwady Delta Shelf in the North Andaman Sea, characterised by a very high influx of riverine water and sediments.
The “Dark Side” of General Health and Fitness-Related Self-Service Technologies: A Systematic Review of the Literature and Directions for Future Research
Authors: Chen Haiyan , Schoefer Klaus , Manika Danae , Tzemou Effy
Published: December 2023

While general health and fitness-related self-service technologies, such as health and fitness apps and wearable activity trackers, are steadily increasing in popularity, there are increasing reports of the potential harm they can cause to consumer well-being. An overview and analysis of the “dark side” of general health and fitness-related self-service technologies is therefore timely and appropriate. In the present work, the authors systematically identify and review the existing literature on this topic across various disciplinary backgrounds. They summarize available knowledge concerning the potential adverse consequences for consumer well-being resulting from the use of health and fitness apps and wearable devices and propose a conceptual framework to explain the relationship between using such technologies and these negative outcomes. Based on these insights, the authors identify current research gaps concerning the dark side of general health and fitness-related self-service technologies and propose corresponding directions for future research. They also discuss the implications of these findings for marketers and public policy makers.
An integrated Design Methodology for Swarm Robotics using Model-Based Systems Engineering and Robot Operating System
Authors: Aloui Khalil , Guizani Amir , Hammadi Moncef , Soriano Thierry , Haddar Mohamed
Published: December 2023

In swarm robotics, robots solve problems using collective behaviors similar to those observed in natural systems, such as birds, bees or fish. They determine their collective behavior through several simple interactions. However, the behavior of swarms emerging from local interactions remains difficult to predict. When trying to design swarm robotic systems for real applications, researchers are confronted with a wide range of software and hardware challenges in the different phases of the design process; problems related to the modeling of swarm systems, related to simulation, related to implementation on software and even on real systems, etc. Despite the increasing popularity of swarm robotics, designing effective and scalable swarm systems remains a challenging task due to the complex interactions between the individual robots and the environment. Therefore, there is a need for a systematic and comprehensive methodology that can guide designers in developing swarm robotics systems that are efficient, reliable, and adaptable to different scenarios. Our main contribution is to propose a new integrated methodology for the development of swarm robot systems. This methodology is based on modeling with Model-Based Systems Engineering method (MBSE) to specify the requirements and the collective behaviors of the swarms, then on the verification of the developed models and finally on the validation of the swarm system by physical prototyping with real robots. Our contribution also focuses on the development of a new SysML profile using the Domain Specific Language (DSL) that we call SwarmML to customize the functional and structural modeling of a swarm system (properties and attributes of the swarm). Two case studies are applied to validate our methodology; a case study of an aggregation of a swarm of robots and a case study of a collaborative simultaneous localization and mapping application (C-SLAM) performed by a swarm of Turtlebots. The novelty of this proposed methodology is the combination of SysML and Robot Operating System (ROS) to address the management of traceability between the different levels of swarm system design, in order to achieve functional, physical and software integration.
Pregnancy and severe mental illness: Birth choices, best interests and the untapped potential of advance decisions
Authors: Halliday Samantha
Published: December 2023

Choice is a central tenet of maternity care; its importance is emphasised in policy documents, clinical guidelines, and the law. However, the lived experience is often rather different and that is particularly the case in the context of pregnant women with a severe mental illness (SMI). The biomedical discourse is powerful and has successfully constructed pregnancy and birth as risky, as a procedure to be managed by experts using technology to ensure that nothing goes wrong. Within that already risky process, women with SMI are constructed as risky, rather than at risk, posing a risk to themselves and the foetus they carry, as well as to the healthcare professionals who care for them. It is not merely their treatment decisions that are questioned, but their very ability to make those decisions. The consequence of this is reduced choice for women with SMI who are cautioned to act responsibly, but where they fail to acquiesce and comply with medical advice, making the ‘wrong’ choice, their capacity is called into question and, if found to lack capacity, decisions about mode and place of delivery will be determined by someone else on the basis of that third party’s evaluation of her best interests, giving scant regard to her wishes. This article reviews the recent obstetric intervention case law, interrogating the discrepancy between the minimal weight attributed to the birth choices of pregnant women with SMI within the best interests assessment in comparison to the significance accorded to the wishes of non-pregnant individuals in other treatment contexts. It challenges the pregnancy exceptionalism evident in the case law and proposes the use of advance decisions as a tool for women to make birth decisions at a time when their capacity is undiminished, ensuring that their own choices, albeit expressed as refusals of treatment, determine maternity care.
Are Bankruptcy Prediction Models Effective for Predicting Ratings? An Empirical Investigation of Altman’s Model in Indian Corporate Sector
Authors: Singh Gurmeet , Singla Ravi
Published: December 2023

The alternative method for predicting credit ratings by employing bankruptcy prediction models is the main subject of the present article. Rating agencies utilize an extremely intricate rating methodology, which differs from agency to agency, and the ratings given by agencies are less reliable in anticipating short-term defaults and react slowly to credit events, as demonstrated by the failure of agencies during the crisis of 2008. Both credit rating assignment and bankruptcy prediction focused on the probability of default, but the methodology used by the two varies significantly: corporate bankruptcy prediction models predict the future default probability of a company as a whole, whereas credit rating only access the default risk associated with a specific instrument. Therefore, it is preferable to employ bankruptcy prediction algorithms to access a company’s future ratings. The present study used Altman’s framework in order to assess whether bankruptcy prediction models can be applied to access a company’s future ratings in advance or not and found that it can be used as an alternative method to predict the future credit ratings, which can be used by investors and banks to make better investment and lending decisions.