Miracle-dz/UniFAR-FTUs huggingface dataset:
A study on specific learning algorithms pertaining to classify lung cancer disease

Lung cancer is a worldwide precarious disease and it is encouraged by the abnormal growth of cells in bronchi. Spotting the cancer cells is unknown until it leads to respiration issues and the muddling of organs working. Due to problems, limited or incorrect selection of hypothesis space, and dropping into local minima, single learners often give erratic output in an existing approach. The ensemble method accomplished a dataset that is free and composed of computed tomography (CT) images. The annotation process reveals observed lung lesions and provides a degree of malignancy for each lesion. Detection of benign and malignant nodules is recognized using deep convolutional frameworks AlexNet, SqueezeNet, GoogleNet, ResNet, and Inception ResNet, achieves higher accuracy (93%) than other convolutional neural networks (CNNs). Eight machine learning methods are involved for achieving better performance. The prediction probability obtained from CNN is applied as input to support vector machines (SVM), K‐nearest neighbours (KNN), naive Bayes (NB), multi‐layer perceptron (MLP), decision trees (DT), gradient boosted regression trees (GBRT), and adaptive boosting. The composition of GoogleNet model and AdaBoost classifier reached the most coherent classification accuracy as 99%. This is one of the best ways to analyse early detection and it increases the survival rate. Therefore, the result from the proposed deep CNN and ML technique achieves better precision than sputum cytology, X‐Ray process, and earlier detection of lung cancer.
Robust Yellow River Delta Flood Management under Uncertainty

A number of principles for evaluating water resources decisions under deep long-run uncertainty have been proposed in the literature. In this paper, we evaluate the usefulness of three widely recommended principles in the context of delta water and sedimentation management: scenario-based uncertainty definition, robustness rather than optimality as a performance measure, and modeling of adaptability, which is the flexibility to change system design or operations as conditions change in the future. This evaluation takes place in the context of an important real-world problem: flood control in the Yellow River Delta. The results give insight both on the physical function of the river system and on the effect of various approaches to modeling risk attitudes and adaptation on the long-term performance of the system. We find that the optimal decisions found under different scenarios differ significantly, while those resulting from using minimal expected cost and minmax regret metrics are similar. The results also show that adaptive multi-stage optimization has a lower expected cost than a static approach in which decisions over the entire time horizon are specified; more surprisingly, recognizing the ability to adapt means that larger, rather than smaller, first-stage investments become optimal. When faced with deep uncertainty in water resources planning, this case study demonstrates that considering scenarios, robustness, and adaptability can significantly improve decisions.
Iris Recognition Development Techniques: A Comprehensive Review

Recently, iris recognition techniques have achieved great performance in identification. Among authentication techniques, iris recognition systems have received attention very much due to their rich iris texture which gives robust standards for identifying individuals. Notwithstanding this, there are several challenges in unrestricted recognition environments. In this article, the researchers present the techniques used in different phases of the recognition system of the iris image. The researchers also reviewed the methods associated with each phase. The recognition system is divided into seven phases, namely, the acquisition phase in which the iris images are acquired, the preprocessing phase in which the quality of the iris image is improved, the segmentation phase in which the iris region is separated from the background of the image, the normalization phase in which the segmented iris region is shaped into a rectangle, the feature extraction phase in which the features of the iris region are extracted, the feature selection phase in which the unique features of the iris are selected using feature selection techniques, and finally the classification phase in which the iris images are classified. This article also explains the two approaches of iris recognition which are the traditional approach and the deep learning approach. In addition, the researchers discuss the advantages and disadvantages of previous techniques as well as the limitations and benefits of both the traditional and deep learning approaches of iris recognition. This study can be considered as an initial step towards a large-scale study about iris recognition.
Why and how do individual entrepreneurs use digital channels in an emerging market? Determinants of use and channel coordination

PurposeMany studies have examined the use of social media by either customers or firms, especially in developed markets. However, little is known about why young individual entrepreneurs use digital channels (DCs) as business platforms in emerging markets (EMs) and how they integrate them into their marketing activities. This paper aims to try filling this gap.Design/methodology/approachGiven the exploratory nature of this research, the authors employ a qualitative approach based on a focus group (12 participants) and semi-structured interviews (8 participants) with young entrepreneurs in Saudi Arabia. The authors adopted a deductive thematic analysis based on pre-determined theoretical frameworks to analyse and interpret the data.FindingsThe authors found that the young entrepreneurs mainly used Instagram, Snapchat and, to a moderate extent, YouTube as digital marketing channels for advertising purposes. However, they used WhatsApp for establishing direct contact and personalising communication with customers. The entrepreneurs used these channels because they are useful, easy to use, cost-effective, fun and widely used by local customers. The authors also found that the entrepreneurs used social media and offline channels conjointly in three main marketing activities (communication/promotion, transactions and customer service) to boost the customer purchasing process. The complementarity of social media and offline channels was found to improve brand visibility, enlarge markets and strengthen the customer relationship. However, the achievement of these benefits varied across small firms depending on the entrepreneurs' abilities in coordinating multiple channels and according to the distinctiveness of their businesses.Research limitations/implicationsThis study contributes to the entrepreneurial literature by extending the unified theory of acceptance and use of technology (UTAUT) to explain the motives behind the use of DCs amongst individual entrepreneurs. Furthermore, this paper proposes a novel theoretical framework for studying the interaction between online and offline marketing channels during the purchasing process. Through this framework, the study provides new insights into channel coordination and multi-channel customer behaviours from the entrepreneur's perspective.Practical implicationsThis study helps understand why small firms use different DCs. Likewise, it shows how young entrepreneurs coordinate online/offline channels in a complementary manner. The findings could also help in designing appropriate programmes encouraging young entrepreneurs to use online channels and social media to enhance their business activities.Originality/valueThis study offers a novel attempt at explaining the use of DCs and their interaction with traditional channels from the entrepreneur's perspective and brings new insights to why and how young entrepreneurs use DCs in an emerging market.
Modeling Reference Evapotranspiration in a Semiarid Area Using Data-driven Techniques: A Case Study in Lower Cheliff plain, Algeria

Evapotranspiration (ET) is an important part of the hydrologic cycle, especially when it comes to irrigated agriculture. For the estimation of reference evapotranspiration (ET0), direct methods either pose difficulties or call for many inputs that may not always be available from weather stations. This study compares Feed Forward Neural Network (FFNN), Radial Basis Function Neural Network (RBFNN). and Gene Expression Programming (GEP) approachs for the estimation of daily ET0 in a weather station in Lower Cheliff plain (northwest Algeria), over a 6-year period (2006–2011). Firstly, measured air temperature, relative humidity, wind speed, solar radiation and global radiation was used to calculate ET0 using FAO-56 Penman-Monteith equation as the reference. Then, the calculated ET0 using FAO-56 Penman-Monteith was considered as output for data driven models, while the measured meteorological data were considered as input of the models. The coefficient of determination (R2), root mean square error (RMSE) and Nash Sutcliffe efficiency coefficient (EF) were used to evaluate the developed models. The results of the developed models were compared with the Penman-Monteith evapotranspiration using these performance criteria. The FFNN model proved to yield the best performance compared to all the developed data-driven models, while the RBF-NN and GEP models also demonstrated potential for good performance.
Direct and External Hybrid Modulation Approaches for Access Networks

The demand for low-cost high-speed transmission is a major challenge for 5G future networks. To meet this optical communication demand, holistic and painstaking approaches are required in designing a simplified system model. Since the demands for high bandwidth are growing at unprecedented speed as we approach the Zettabyte era, it is crucial to minimize chromatic dispersion (CD) associated to high bit-rate signals. Mitigating CD electronically comes at high cost which may not be compatible with 5G. Photonic Integrated Circuit (PIC) as an enabler for fast speed optical transmission is still undergoing its growth stage and its major speed and efficiency have not yet been attained. However, proper and right combination of components and approaches can potentiate this technology in a more cost-efficient way. Hybrid modulation (HM)-PIC presents a simplified approach in terms of cost and efficiency for 5G networks. Hybridization of existing modulation components and approaches in PIC can enhance the generation of high bit-rate signals without the need for electrical CD compensation. A detailed study of hybrid multilevel signal modulation concept as a valuable solution for Data Centers (DC) high data-rate signals and next-generation Passive Optical Networks (PONs) is proposed.
Microstructural characterisation and wettability behaviour of nano-HA coating on Ti-6Al-4V alloy by electrophoretic deposition method (EPD)

ABSTRACT In the present study, nano-hydroxyapatite (HA) coating was developed on the Ti-6Al-4V alloy by electrophoretic deposition techniques (EPD). Followed by coating, a detailed microstructural characterisation was done. Wettability analysis was done by using Hank’s solution. Surface roughness of the coated sample is 4.19 μm as compared to 0.5 μm for as received Ti-6Al-4V alloy. Microstructural analysis shows the development of uniform, homogeneous and dense coating with the presence of porosities (35%). The size of porosities varies from 1 to 2.5 μm and the particles size varies from 0.3 to 1.5 μm. Phase analysis shows the presence of mainly HA (95%) and few rutile (5%) phase in nano-HA coated sample; on the other hand, presence of α (93%) and β (7%) phases in as received Ti-6Al-4V alloy. There is an improvement in wettability of the coated sample as compared to as received Ti-6Al-4V alloy in terms of reduction in contact angle from 60° (for Ti-6Al-4V alloy) to 45° (for nano-HA coated Ti-6Al-4V alloy).
Auto-Threshold Deep SVDD for Anomaly-based Web Application Firewall

Protecting websites and applications from cyber-threats is vital for any organization. A Web application firewall (WAF) prevents attacks to damaging applications. This provides a web security by filtering and monitoring traffic network to protect against attacks. A WAF solution based on the anomaly detection can identify zero-day attacks. Deep learning is the state-of-the-art method that is widely used to detect attacks in the anomaly-based WAF area. Although deep learning has demonstrated excellent results on anomaly detection tasks in web requests, there is trade-off between false-positive and missed-attack rates which is a key problem in WAF systems. On the other hand, anomaly detection methods suffer adjusting threshold-level to distinguish attack and normal traffic. In this paper, first we proposed a model based on Deep Support Vector Data Description (Deep SVDD), then we compare two feature extraction strategies, one-hot and bigram, on the raw requests. Second to overcome threshold challenges, we introduce a novel end-to-end algorithm Auto-Threshold Deep SVDD (ATDSVDD) to determine an appropriate threshold during the learning process. As a result we compare our model with other deep models on CSIC-2010 and ECML/PKDD-2007 datasets. Results show ATDSVDD on bigram feature data have better performance in terms of accuracy and generalization.
Convergence of Wireless and Optical Network in Future Communication Network

The requirement of data increases many-fold in recent years to support the newest technologies in B5G and 6G. Wireless is the last mile solution as access with an optical network as the backbone in future communication systems. Over the years in every new generation, the distance between the base station and the user is decreasing and the optical node is coming closer to the user. There are several technologies like AR/VR, AI, holographic communication, holographic telepresence, etc. are the main candidates in B5G and 6G, which are required high-speed connection with low latency. To support these services, it is almost mandatory that transmit data across the network should be smooth and seamless to provide successful communication. Providing a successful and appropriate wireless link among the users simultaneously to achieve the requirements is becoming more complex, hence challenging. The optical backbone of all wireless access networks requires supporting these user’s requirements, needs to evolve continuously with wireless network evolution. This chapter will study the evolution of both networks to understand their cooperation, alignment, and support.
On the Control Strategy to Improve the Salt Rejection of a Thin-Film Composite Reverse Osmosis Membrane

Since the specific energy consumption (SEC) required for reverse osmosis (RO) desalination has been steeply reduced over the past few decades, there is an increasing demand for high-selectivity membranes. However, it is still hard to find research papers empirically dealing with increasing the salt rejection of RO membranes and addressing the SEC change possibly occurring while increasing salt rejection. Herein, we examined the feasibility of the process and material approaches to increase the salt rejection of RO membranes from the perspective of the SEC and weighed up a better approach to increase salt rejection between the two approaches. A process approach was confirmed to have some inherent limitations in terms of the trade-off between water permeability and salt rejection. Furthermore, a process approach is inappropriate to alter the intrinsic salt permeability of RO membranes, such that it should be far from a fundamental improvement in the selectivity of RO membranes. Thus, we could conclude that a material approach is necessary to make a fundamental improvement in the selectivity of RO membranes. This paper also provides discussion on the specific demands for RO membranes featuring superior mechanical properties and excellent water/salt permselectivity to minimize membrane compaction while maximizing the selectivity.
Group structure detection for a high‐dimensional panel data model

This article investigates latent group structures in a high‐dimensional panel data model. It is assumed that subjects are classified into unobserved groups, where the fixed effects for each subject are the same within a group but heterogeneous across different groups. A penalized regression approach is proposed to identify latent group structures and significant covariates simultaneously. A new algorithm is proposed to optimize the objective function. When the sample size goes to infinity, it is shown that the proposed estimator recovers latent group structures and important covariates with probability approaching one. The finite sample performance of the proposed estimator is evaluated through Monte Carlo studies and illustrated using a real dataset.
Complex Scene Understanding and Recognition Based on the Explainable Machine Learning in AI Models

Information fusion is an important part of numerous neural network systems and other machine learning models. However, there exist some problems about fusion in scene understanding and recognition of complex environment, such as difficulty in feature extraction, small sample size and interpretability of the model. Deep reinforcement learning can combine the perception ability of deep learning with the decision-making ability of reinforcement learning to learn control strategies directly from high-dimensional original data. However, It faces these challenges, such as low optimization efficiency, poor generality of network model, small labeled samples, explainable decisions for users without a strong background on Artificial Intelligence (AI). Therefore, at the level of application and theoretical research, this paper aims to solve the above problems,the main contributions include: (1)optimize the feature representation methods based on spatial-temporal feature of the behavior characteristics in the scene, deep metric learning between adjacent layers and cross-layer learning theory, and then propose a lightweight reinforcement learning network model to solve these problems of the complexity of the model to be explained, the difficulty of extracting feature and the difficulty of tuning parameter; (2)construct the self-paced learning strategy of the deep reinforcement learning model, introduce transfer learning mechanism in the optimization process, and solve the problem of low optimization efficiency and small labeled samples; (3)design the behavior recognition framework of the multi-perspective deep knowledge transfer learning model, construct a explainable behavior descriptor, and solve the problems of poor network generality and weak 1explanation of network. Our research is of great theoretical and practical significance in the fields of artificial intelligence and public security.
NaOH-Catalyzed Fractionation of Rice Husk Followed by Concomitant Production of Bioethanol and Furfural for Improving Profitability in Biorefinery

The alkaline fractionation of rice husk (RH) with NaOH was optimized for the purpose of obtaining a high-yield recovery of glucan and increasing the removal rate for lignin and ash, resulting in a hemicellulose-rich hydrolysate. The determined optimal conditions were a temperature of 150 °C, reaction time of 45 min, and NaOH concentration of 6% (w/v). The glucan content in the fractionated RH (Fr. RH) was 80.1%, which was significantly increased compared to the raw RH (35.6%). High glucan content in the fractionated solid residue is the most essential factor for minimizing enzyme dosages in enzymatic saccharification. The final enzymatic digestibilities (at 96 h) of raw and NaOH-Fr. RH with cellulase loadings of 30 FPU/g cellulose were 10.5% and 81.3%, respectively. Approximately 71.6% of the xmg content (mainly xylose) was concomitantly degraded into the fractionated hydrolysate (Fr. Hydrolysate). When this hydrolysate was acidified with sulfuric acid and subjected to heat treatment, a furfural production yield of about 64.9% was obtained. The results show that two-stage fed-batch fermentation with glucan-rich Fr. RH has the potential to achieve high-ethanol titers of 28.7 g/L.
Actions define a character: Assessment centers as behavior‐focused personality measures

To expand our knowledge of personality assessment, this study connects research and theory related to two common selection methods: assessment centers (ACs) and personal-ityinventories.Weexaminethevalidityofpersonality-based AC ratings within a multi-method framework. Drawing from the self-other knowledge asymmetry model (Vazire, 2010), we propose that AC ratings are suited to capture personality traits that are observable in social interactions, whereas other methods (i.e., self-ratings) are useful to assess more internal traits. We obtained data from two personality-based ACs, self-and other-rated personality inventories, and supervisor ratings of job performance. Confirmatory factor analyses indicated that personality-based AC ratings reflected the Big Five traits. Consistent with the self-other knowledge asymmetry model, AC ratings of more observable personality traits (Extraversion, Agreeableness, and Intellect/Openness) were correlated with inventory-based measures of these traits. AC ratings demonstrated incremental validity in predicting job performance over inventory-based
The Role of Nurses in Diabetes Care: A Qualitative Study

Purpose: Nurses are key players in the care of diabetes patients. It is not clear how nurses experience the diabetes care role within a multidisciplinary care context. This article reports the perspectives of nurses working in one National Health Service trust in the United Kingdom, about their role in diabetes care. Methods: The study employed a descriptive qualitative approach. Participants were nurses who worked in two diabetes clinics in one National Health Service trust in the United Kingdom. The study included 10 female participants, who provided nursing care to patients with diabetes. Data were collected between May and September 2017. Results: Three themes were generated in relation to the role of nurse in diabetes care: 1) Nurses’ performed role: the role performed by nurses in diabetes care was regarded as complex and one that required appropriate preparation and support; 2) Implications of the nurses’ performance: nurses engaged in multiple activities that aimed at providing holistic and patient centred care, and 3) Challenges facing nurses in diabetes care: nurses reported facing significant challenges that interfered with their care role and which could negatively affect patient outcomes. Conclusions: Nurses play a critical role in the care of diabetes patients and require significant support to undertake their role.
Lightweight Connectivity In Graph Convolutional Networks For Skeleton-Based Recognition

Graph convolutional networks (GCNs) aim at extending deep learning to arbitrary irregular domains, namely graphs. Their success is highly dependent on how the topology of input graphs is defined and most of the existing GCN architectures rely on predefined or handcrafted graph structures. In this paper, we introduce a novel method that learns the topology (or connectivity) of input graphs as a part of GCN design. The main contribution of our method resides in building an orthogonal connectivity basis that optimally aggregates nodes, through their neighborhood, prior to achieve convolution. Our method also considers a stochasticity criterion which acts as a regularizer that makes the learned basis and the underlying GCNs lightweight while still being highly effective. Experiments conducted on the challenging task of skeleton-based hand-gesture recognition show the high effectiveness of the learned GCNs w.r. t. the related work.