bluuebunny/crossref_metadata_2025 huggingface dataset:
Therapeutische Behandlung der minimalen Resterkrankung zur Verhinderung von Spätrezidiven bei Hormonrezeptor-positivem Brustkrebs: Herausforderungen und neue Ansätze
Authors: Cescon David W. , Kalinsicy Kevin , Parsons Heather A. , Smith Karen Lisa , Spears Patricia A. , Thomas Alexandra , Zhao Fengmin , DeMichele Angela
Published: May 2022

Obwohl die meisten Brustkrebserkrankungen in einem heilbaren Stadium diagnostiziert werden, kommt es bei etwa 20% der Frauen im Laufe ihres Lebens zu einem erneuten Auftreten an einer entfernten Körperstelle. Diese metastatischen Rezidive sind mit den derzeitigen Therapieansätzen nicht heilbar. In den letzten 10 Jahren wurden die biologischen Mechanismen, die diesen Rezidiven zugrunde liegen, aufgeklärt. Dabei wurde die Existenz einer minimalen Resterkrankung in Form von zirkulierenden Mikrometastasen und einer ruhenden Erkrankung, vor allem im Knochenmark, festgestellt. Heute stehen zahlreiche Technologien zur Verfügung, um minimale Resterkrankungen (minimal residual disease, MRD) nach einer Brustkrebsbehandlung zu erkennen. Nicht bekannt ist jedoch, wie diese Zellen am besten anvisiert und ausgerottet werden können und ob die Beseitigung nachweisbarer Erkrankungen vor der Bildung von offenen Metastasen das endgültige Fortschreiten und ein Versterben verhindern kann. Klinische Studien zur Überprüfung dieser Hypothese sind aufgrund der Seltenheit von MRD im Blut und Knochenmark schwierig, weshalb eine große Anzahl von Überlebenden untersucht werden muss, um potenzielle Teilnehmer an einer Studie zu identifizieren. Durch Screening-Untersuchungen mit prognostischen molekularen Instrumenten könnten Patientinnen identifiziert werden, bei denen eine MRD am wahrscheinlichsten ist, wobei die Beziehung zwischen diesen Prädiktoren und dem Nachweis von MRD jedoch noch nicht definiert ist. Weitere Herausforderungen sind das Fehlen eines definitiven Tests für MRD mit nachgewiesenem klinischem Nutzen, Schwierigkeiten bei der Auswahl potenzieller Interventionen aufgrund des begrenzten Verständnisses der Biologie der MRD und die emotionale Belastung durch den Nachweis von MRD bei Patientinnen, die eine definitive Behandlung abgeschlossen haben und keine Anzeichen einer offenen metastatischen Erkrankung aufweisen. Diese Übersichtsarbeit bietet einen Strategieplan für die Bewältigung dieser Herausforderungen bei der Konzeption und Durchführung von klinischen Interventionsstudien, die darauf abzielen, die MRD zu eliminieren und letztlich eine metastatische Erkrankung zu verhindern, um die Überlebensrate bei dieser Erkrankung zu verbessern, wobei ein besonderer Schwerpunkt auf Spätrezidiven bei Östrogenrezeptor-positivem Brustkrebs liegt.
Today’s Mistakes and Tomorrow’s Wisdom in the Surgical Treatment of Barrett’s Adenocarcinoma
Authors: Garbarino Giovanni Maria , van Berge Henegouwen Mark Ivo , Gisbertz Suzanne Sarah , Eshuis Wietse Jelle
Published: May 2022

Background: Barrett’s esophagus is a premalignant condition caused by longstanding gastroesophageal reflux disease and may progress to low-grade dysplasia, high-grade dysplasia (HGD), and finally esophageal adenocarcinoma. Summary: Barrett’s adenocarcinoma can be treated either by endoscopic or surgical resection, depending on the clinical staging. Endoscopic resection is a safe and adequate treatment option for HGD, mucosal tumors, and low-risk submucosal tumors. Its role in the treatment of high-risk submucosal tumors and the role of organ-preserving sentinel node navigated surgery are still under investigation. Esophagectomy with neoadjuvant chemoradiation or perioperative chemotherapy is considered the standard of care for locally advanced Barrett’s adenocarcinoma. Regarding operative technique, there is no proven superiority of one technique over another, although a minimally invasive transthoracic technique seems most commonly applied nowadays. In this review, state-of-the-art evidence and future expectations are presented regarding indications for resection, neoadjuvant or perioperative therapy, type of surgery, and postoperative follow-up for Barrett’s adenocarcinoma. Key Messages: In Barrett’s adenocarcinoma, endoscopic resection is the standard treatment option for low-risk mucosal and submucosal tumors. For high-risk submucosal tumors, endoscopic submucosal dissection with close surveillance and sentinel node navigated surgery are currently being studied. For locally advanced cancer, a multimodal therapy including esophagectomy is the standard of care. Nowadays, in high-volume centers, a minimally invasive transthoracic esophagectomy with an intrathoracic anastomosis is the most common procedure for Barrett’s adenocarcinoma.
Treatment Response and Clinical Outcomes of Well-Differentiated High-Grade Neuroendocrine Tumors to Lutetium-177-DOTATATE
Authors: Raj Nitya , Coffman Kelley , Le Tiffany , Do Richard Kinh Gian , Rafailov Johnathan , Choi Ye , Chou Joanne F. , Capanu Marinela , Dunphy Mark , Fox Josef J. , Grewal Ravinder K. , Reddy Ryan P. , Riedl Christopher , Schoder Heiko , Bodei Lisa , Reidy-Lagunes Diane
Published: May 2022

Introduction: Lutetium-177 (177Lu)-DOTATATE received FDA approval in 2018 to treat somatostatin receptor-positive gastroenteropancreatic neuroendocrine tumors (NETs). Little data are available on response and outcomes for well-differentiated (WD) high-grade (HG) NETs treated with 177Lu-DOTATATE. Materials and Methods: Patients with WD HG NETs treated with 177Lu-DOTATATE at MSK from 2018 to 2020 were identified. Demographics, response (RECIST 1.1), and progression-free survival (PFS) were determined. Next-generation sequencing (NGS) was performed in the archival tumor. Results: Nineteen patients, all with progressive, heavily treated disease, were identified. Sites of tumor origin were: pancreas (74%), small bowel (11%), rectum (11%), and lung (5%); median Ki-67 was 32% (range 22–56). Thirteen patients (68%) completed all four 177Lu-DOTATATE cycles. Best response (N = 18 evaluable) was: 5/18 (28%) partial response, 8/18 (44%) stable disease, and 5/18 (28%) disease progression. Median PFS was 13.1 months (95% CI: 8.7–20.9). Most common treatment-related toxicities were thrombocytopenia (9 patients, 47%; G3/4, 1 patient, 5%), anemia (7 patients, 37%; G3/4, 2 patients, 11%), leukopenia (6 patients, 32%; G3/4, 0 patients), and liver function test elevation (4 patients, 21%; G3/4, 0 patients). NGS results were available from 13/19 tumors (68%). The most observed alterations were in MEN1 (6/13, 46%) and DAXX (4/13, 31%). No RB1 alterations identified. Conclusion: We observed a meaningful disease control rate of 72% during treatment of WD HG NETs with 177Lu-DOTATATE. In this heavily pre-treated population, more than half of patients received all four treatment cycles with toxicities largely bone marrow-related. As would be expected in WD NETs, the vast majority had alterations in chromatin remodeling genes and no RB1 alterations.
A Model-Based Method of Extension and Intension
Authors:   University of Talca, López-Astorga Miguel
Published: May 2022

The semantic method of extension and intension presented by Rudolf Carnap may have some problems from the current perspective in cognitive science. For example, that method is strongly linked to logic, and it is hard to determine where its state-descriptions come from, how many they are, and what the relation of accessibility between them is. This paper tries to solve difficulties such as those by updating the method resorting to the theses and results of a contemporary reasoning theory: the theory of mental models. Basically, the update consists of replacing the state-descriptions of the original method with the possibilities of the theory of mental models. That allows reformulating several relevant concepts in Carnap’s method in order to better adjust them to the real characteristics of human language.
Science in Eighteenth-Century French Literary Fiction: A Step to Modern Science Fiction and a New Definition of the Human Being?
Authors:   Mykolas Romeris University, Parent Arnaud
Published: May 2022

In eighteenth-century France, scientific progress and its spreading met a growing interest among public, an enthusiasm that was to be reflected in literature. Fictional works including scientific knowledge in their narrative made their appearance, paving the ground for a genre promised to a growing success in the following centuries—science fiction. The article presents three eighteenth-century French literary works, each one centered on a different domain of science: Voltaire’s Micromégas (1752), Charles-François Tiphaigne’s Amilec, or the Seeds of Mankind (Amilec, ou la graine d’hommes, 1753) and François-Félix Nogaret’s The Mirror of Current Events, or Beauty to the Highest Bidder (Le miroir des événements actuels, ou la belle au plus offrant, 1790). The first one, an iconic Enlightenment work that promotes critical thinking, relies on discoveries made in astronomy and optics. Tiphaigne de la Roche is far from sharing the fame of Voltaire, but his odd Amilec is noteworthy as it is possibly the very first science-fiction work in which biology is central. Written in the unique atmosphere of the French revolution, Nogaret’s work The Mirror of Current Events depicts androids-like interacting with humans. Our purpose is to show that these works were a precursor (proto science fiction) of the science fiction genre in literature, to describe how and what science or technology was depicted in them, and how they influenced the view of Man (humans) in eighteenth-century France.
A novel efficient adaptive-neuro fuzzy inference system control based smart grid to enhance power quality
Authors: Sekhar Dharamalla Chandra , Rama Rao Pokanati Veera Venkata , Kiranmayi Rachamadugu
Published: May 2022

A novel adaptive-neuro fuzzy inference system (ANFIS) control algorithm-based smart grid to solve power quality issues is investigated in this paper. To improve the steady-state and transient response of the solar-wind and grid integrated system proposed ANFIS controller works very well. Fuzzy maximum power point tracking (MPPT) algorithm-based DC-DC converters are utilized to extract maximum power from solar. A permanent magnet synchronous generator (PMSG) is employed to get maximum power from wind. To maximize both power generations, back-to-back voltage source converters (VSC) are operated with an intelligent ANFIS controller. Optimal power converters are adopted this proposed methodology and improved the overall performance of the system to an acceptable limit. The simulation results are obtained for a different mode of smart grid and non-linear fault conditions and the proven proposed control algorithm works well.
Modeling and analysis of energy losses under transient conditions in induction motors
Authors: Al-Rawashdeh Ayman Y. , Dalabeeh Ali , Samarah Ashraf , Alzyoud Abdallah Ershoud , Alzyoud Khalaf Y.
Published: May 2022

The present study is mainly concerned with solving the problems associated with energy losses resulting from starting transient conditions. In the present study, the possibilities of decreasing starting transient condition energy losses are investigated. Additionally, a comparison of the energy losses using different starting methods is also conducted. In this work, a complete description for deriving the equations used to calculate energy losses, mathematical analysis of the energy losses, the models of the different methods in this work were simulated using MATLAB/Simulink; and the results of energy losses and the dynamic characteristics are provided. Simulation results showed that the minimum starting energy losses were accomplished by soft starting method, in which the starting current could be reduced to the minimum value.
Advancements in energy storage technologies for smart grid development
Authors: Sharma Pankaj , Reddy Salkuti Surender , Kim Seong-Cheol
Published: May 2022

In the modern world, the consumption of oil, coal natural gas, and nuclear energy has been causing by a serious environmental problem and an ongoing energy crisis. The generation and consumption of renewable energy sources (RESs) such as solar and wind tidal, can resolve the problem but the nature of the RESs is fluctuating and intermitted. This evolution brings a lot of challenges in the management of electrical grids. The paper reviewed the advancements in energy storage technologies for the development of a smart grid (SG). More attention was paid to the classification of energy storage technologies based on the form of energy storage and based on the form of discharge duration. The evaluation criteria for the energy storage technologies have been carried out based on technological dimensions such as storage capacity, efficiency, response time, energy density, and power density, the economic dimension such as input cost and economic benefit; and the environmental dimension such as emission and stress on ecosystem, social demission such as job creation and social acceptance were also presented in this paper.
Design, modeling and simulation of perturb and observe maximum power point tracking for a photovoltaic water pumping system
Authors: Hilali Abdelilah , Mardoude Yahya , Ben Akka Youssef , El Alami Hassan , Rahali Abderrafii
Published: May 2022

Maximum power point tracking (MPPT) is considered one of the important factors in minimizing the installation costs and improving the efficiency of any photovoltaic water pumping system. The MPPT controller is specifically used to extract the maximum available power from the photovoltaic (PV) array. The maximum power can be achieved by using a specific algorithm. This work aims to raise awareness among farmers about the energy benefits available in the region of Meknes in Morocco, the economic gain and the environmental impact applied to the solar pumping system so that it can be generalized. To obtain the maximum power at each moment, a direct current (DC) water pump (SQF 0-6-2) powered by the solar panels (REC_330NP) through a buck converter was adapted. In addition, this study illustrates the theory of operation of the perturb and observe (P&O) algorithm and simulates the evaluation of this algorithm under different operating conditions (temperature and solar irradiation), and showed the advantages of this system that can operate at the optimal power regardless of disturbances.
Determining the Pareto front of distributed generator and static VAR compensator units placement in distribution networks
Authors: Ahmadi Bahman , Çağlar Ramazan
Published: May 2022

The integration of distributed generators (DGs), which are based on renewable energy sources, energy storage systems, and static VAR compensators (SVCs), requires considering more challenging operational cases due to the variability of DG production contributed by different characteristics for different time sequences. The size, quantity, technology, and location of DG units have major effects on the system to benefit from the integration. All these aspects create a multi-objective scope; therefore, it is considered a multi-objective mixed-integer optimization problem. This paper presents an improved multi-objective salp swarm optimization algorithm (MOSSA) to obtain multiple Pareto efficient solutions for the optimal number, location, and capacity of DGs and the controlling strategy of SVC a radial distribution system. MOSSA is a bio-inspired optimizer based on swarm intelligence techniques and it is used in finding the optimal solution for a global optimization problem. Two sets of objective functions have been formulated minimizing DGs and SVC cost, voltage violation, energy losses, and system emission cost. The usefulness of the proposed MOSSA has been tested with the 33-bus and 141-bus radial distribution systems and the qualitative comparisons against two well-known algorithms, multiple objective evolutionary algorithms based on decomposition (MOEA/D), and multiple objective particle swarm optimization (MOPSO) algorithm.
Analysis and simulation of even-level quasi-Z-source inverter
Authors: Akshath Niltala Sai shanmukha , Naresh Avugaddi , Nikesh kumar Matcha , Barman Mayur , Nandan Durgesh , Abhilash Tirupathi
Published: May 2022

This research proposes a seven-level inverter with quasi-Z-source boost converters. The proposed topology employs a packed U-cell asymmetrical type multilevel inverter along with front-end quasi-Z-source networks. The quasi networks provide high gain compared to a conventional boost converter. This topology is the most suitable for photovoltaic multi-string applications. The proposed topology has the potential to supply both the alternating current (AC) and direct current (DC) type load. The inverter structure has a lower number of active switches which helps in the reduction of losses and improvement in efficiency. In this paper, the operation principle of a quasi-network and inverter circuit are explained in detail. In addition, the simulation results for various modulation indices are presented. In the MATLAB/Simulink environment, the architecture is proposed by using gated sinusoidal “Pulse width modulation”.
A new method for self-organized dynamic delay loop associated pipeline with reconfigurable computing system
Authors: Kumar Nandigam Suresh , Lakshmi Dasari Vijaya , Sree Prasanna Bejjanki Pooja , Koti Reddy Dodda Venkata Rama
Published: May 2022

The minimization of propagation delay between pipeline stages is very important in wave propagation through pipeline-stages. The propagation delay can be minimized by minimizing the number of stages in a pipeline. In the proposed design a dynamic stage control is imparted in the pipeline. The propagation delay can be optimized in any type of pipeline by controlling number of stages dynamically. The pipeline interpretation helps a lot to overcome the flaws due to not ready sequence (NRS) and synchronization problems. It is observed that, in the pipeline design the basic and actively involved pipeline techniques are concerned with different challenges like clock, throughput, cell area, and sizes. As the data throughput increases the number of stages in pipeline also needs to be increased to meet the desired goal. In the case of unpredictable data speed, the definite number of pipeline stages creates severe problems. In this work a dynamic pipeline is integrated where the number of stages is dynamically changing depending up on data speed. In dynamic pipeline technique the circuit cell area of reconfigurable computing system (RCS) will be reduced dynamically at low-speed data transmission. In the high-speed data communication, the data speed is managed and controlled by dynamic delay loops.
A classification model based on depthwise separable convolutional neural network to identify rice plant diseases
Authors: Prottasha Md. Sazzadul Islam , Reza Sayed Mohsin Salim
Published: May 2022

Every year a number of rice diseases cause major damage to crop around the world. Early and accurate prediction of various rice plant diseases has been a major challenge for farmers and researchers. Recent developments in the convolutional neural networks (CNNs) have made image processing techniques more convenient and precise. Motivated from that in this research, a depthwise separable convolutional neural network based classification model has been proposed for identifying 12 types of rice plant diseases. Also, 8 different state-of-the-art convolution neural network model has been fine-tuned specifically for identifying the rice plant diseases and their performance has been evaluated. The proposed model performs considerably well in contrast to existing state-of-the-art CNN architectures. The experimental analysis indicates that the proposed model can correctly diagnose rice plant diseases with a validation and testing accuracy of 96.5% and 95.3% respectively while having a substantially smaller model size.
Internet of things based electrocardiogram monitoring system using machine learning algorithm
Authors: Rahaman Md. Obaidur , Mehedi Shamrat F. M. Javed , Abul Kashem Mohammod , Fahmida Akter Most. , Chakraborty Sovon , Ahmed Marzia , Mustary Shobnom
Published: May 2022

In Bangladesh’s rural regions, almost 30% of the population lives in poverty. Rural residents also have restricted access to nursing and diagnostic services due to obsolete healthcare infrastructure. Consequently, as cardiac failure occurs, they usually fail to call the services and adopt the facilities. The internet of things (IoT) offers a massive advantage in addressing cardiac problems. This study proposed a smart IoT-based electrocardiogram (ECG) monitoring system for heart patients. The system is divided into several parts: ECG sensing network (data acquisition), IoT cloud (data transmission), result analysis (data prediction) and monetization. P, Q, R, S, and T are ECG signal properties fetched, pre-processed, analyzed and predicted to age level for future health management. ECG data are saved in the cloud and accessible via message queuing telemetry transport (MQTT) and hypertext transfer protocol (HTTP) servers. The linear regression method is utilized to determine the impact of electrocardiogram signal characteristics and error rate. The prediction was made to see how much variation there was in PQRST regularity and its sufficiency to be utilized in an ECG monitoring device. Recognizing the quality parameter values, acceptable outcomes are achieved. The proposed system will diminish future medical costs and difficulties for heart patients.
A secure trust-based protocol for hierarchical routing in wireless sensor network
Authors: Al-Sadoon Maha , Jedidi Ahmed
Published: May 2022

Wireless sensor networks (WSNs) became the backbone of the internet of things (IoT). IoT applications are vital and demand specific quality of service (QoS) requirements. In addition, security has become a primary concern to provide secure communication between wireless nodes, with additional challenges related to the node’s computational resources. Particular, the design of secure and resource efficient routing protocol is a critical issue in the current deployment of WSNs. Therefore, this paper proposes a novel secure-trust aware routing protocol (ST2A) that provides secure and reliable routing. The proposed protocol establishes communication routes based on calculated trust value in joint with a novel cluster head selection algorithm in the hierarchical routing process. The proposed trust-aware routing algorithm improves the routing security in WSN and optimizes many performance metrics related to WSNs unique characteristics. The results of simulation validate the feasibility of the proposed algorithm for enhancing the network lifetime up to 18% and data delivery by 17% as compared with some state-of-the-art routing algorithms.
Rectangular microstrip antenna design with multi-slotted patch and partial grounding for performance enhancement
Authors: Firoz Ahmed Md , Muhammad Touhidul Islam Abu Zafor , Hasnat Kabir Md
Published: May 2022

This paper presents design of a rectangular microstrip patch antenna by using multi-slotted patch and partial grounding plane techniques for both the gain and bandwidth enhancement at the same time. The antenna is designed and simulated for ultra-wideband (UWB) applications using a high frequency structure simulator (HFSS) on FR4_epoxy substrate having a size of 30×20 mm with a dielectric permittivity of 4.4, a tangent loss of 0.02, and a thickness of 0.8 mm and excited by a simple 50 Ω microstrip feed line. The simulation results show that the antenna attains an improved gain of 8.06 dB with a wider impedance bandwidth of 19.7 GHz ranges from 3.15 to 22.85 GHz. The antenna also achieves an efficiency of 96.83% with a return loss of -28.35 dB, and a directivity of 9.39 dB within the entire frequency range. These results imply that the deployment of multi-slotted patch and partial grounding techniques in designing a rectangular microstrip patch antenna is effective in improving its performance.
Resource placement strategy optimization for smart grid application using 5G wireless networks
Authors: Chafi Saad-Eddine , Balboul Younes , Mazer Said , Fattah Mohammed , El Bekkali Moulhime
Published: May 2022

With the evolution of 5G-network, wireless mobile networks are growing to take a strong stand in attempts to achieve ubiquitous large-scale acquisition, connectivity and processing. Smart-grids are among the critical areas that can benefit from the capabilities of the 5G-network, especially internet of things (IoT) applications such as massive machine-type-communications or ultra-reliable low-latency communications. A distributed cloud-services use the cloud, fog and edge computing infrastructures and applications to take advantage of every available resource including network equipment and connected objects to optimize cost, energy, and latency depending on the planned optimization criteria. In this article, we present smart-grid solution based on cloud-services and 5G-network, then we study the integration of smart-grid services in the cloud based on: placement in the cloud and in the end-device, and finally we introduce our proposed solution based on Intelligent placement strategy. The scenarios are evaluated by the iFogSim simulator, and the analyzed results compare the standard cloud placement, edge placement and our intelligent placement with regard to the optimization of the energy consumption, latency, and network usage. The findings show that cloud energy consumption can be substantially reduced using Intelligent Placement while respecting the potential central processing unit (CPU) processing power-limit for each IoT-device used and network constraints in smart-grid.
Fake news detection for Arabic headlines-articles news data using deep learning
Authors: Najadat Hassan , Tawalbeh Mais , Awawdeh Rasha
Published: May 2022

Fake news has become increasingly prevalent in recent years. The evolution of social websites has spurred the expansion of fake news causing it to a mixture with truthful information. English fake news detection had the largest share of studies, unlike Arabic fake news detection, which is still very limited. Fake news phenomenon has changed people and social perspectives through revolts in several Arab countries. False news results in the distortion of reality ignite chaos and stir public judgments. This paper provides an Arabic fake news detection approach using different deep learning models including long short-term memory and convolutional neural network based on article-headline pairs to differentiate if a news headline is in fact related or unrelated to the parallel news article. In this paper, a dataset created about the war in Syria and related to the Middle East political issues is utilized. The whole data comprises 422 claims and 3,042 articles. The models yield promising results.
Stream-keys generation based on graph labeling for strengthening Vigenere encryption
Authors: Prihandoko Antonius Cahya , Dafik Dafik , Agustin Ika Hesti
Published: May 2022

This paper address the cryptographic keys management problem: how to generate the cryptographic keys and apply them to secure encryption. The purpose of this research was to study on utilizing graph labeling for generating stream-keys and implementing the keys for strengthening Vigenere encryption. To achieve this objective, the research was carried out in four stages: developing an algorithm for generating stream-keys, testing the randomness of the constructed keys, implementing the eligible keys in a modified Vigenere encryption and, finally, analyzing the security of the encryption. As the result, most of stream-keys produced by the algorithm are random, and the implementation of the stream keys to the modified Vigenere cipher achieve good security. The contributions of this research are utilizing graph labeling to generate stream-keys and providing different encryption keys for different blocks in a block based cipher with low storage capacity. The novel technical results yielded from this research are the algorithm of developing the source of the stream-keys based on graph labeling, the algorithm of constructing the initial block keys, and the protocol of a modified Vigenere encryption using stream-keys and operated in cipher block chaining mode.
Health monitoring catalogue based on human activity classification using machine learning
Authors: Abdulhussien Ansam A. , Hassen Oday A. , Gupta Charu , Virmani Deepali , Nair Akshara , Rani Prachi
Published: May 2022

In recent times, fitness trackers and smartphones equipped with different sensors like gyroscopes, accelerometers, global positioning system sensors and programs are used for recognizing human activities. In this paper, the results collected from these devices are used to design a system that can assist an application in monitoring a person’s health. The proposed system takes the raw sensor signals as input, preprocesses it and using machine learning techniques outputs the state of the user with minimum error. The objective of this paper is to compare the performance of different algorithms logistic regression (LR), support vector machine (SVM), k-nearest neighbor (k-NN) and random forest (RF). The algorithms are trained and tested with an original number of features as well as with transformed number of features (using linear discriminant analysis). The data with a smaller number of features is then used to visualize the high dimensional data. In this paper, each data point is mapped in the high dimensional data to two-dimensional data using t-distributed stochastic neighbor embedding technique. Overall, the first high dimensional data is visualized and compared with model’s performance with different algorithms and different number of coordinates.
Manta ray optimized deep contextualized bi-directional long short-term memory based adaptive galactic swarm optimization for complex question answering
Authors: Obula Reddy Ankireddypalli Chandra , Madhavi Kasa
Published: May 2022

Complex question answering (CQA) is used for human knowledge answering and community questions answering. CQA system is essential to overcome the complexities present in the question answering system. The existing techniques ignores the queries structure and resulting a significant number of noisy queries. The complex queries, distributed knowledge, composite approaches, templates, and ambiguity are the common challenges faced by the CQA. To solve these issues, this paper presents a new manta ray foraging optimized deep contextualized bidirectional long-short term memory based adaptive galactic swarm optimization (MDCBiLSTMAGSO) for CQA. At first, the given input question is preprocessed and the similarity assessment is performed to eliminate the misclassification. Afterwards, the attained keywords are mapped into applicant results to improve the answer selection. Next, a new similarity approach named InfoSelectivity is introduced for semantic similarity evaluation based on the closeness among elements. Then, the relevant answers are classified through the MDCBiLSTM and optimized by a new manta ray foraging optimization (MRFO). Finally, adaptive galactic swarm optimization (AGSO) resultant is the best output. The proposed scheme is implemented on the JAVA platform and the outputs of designed approach achieved the better results when compared with the existing approaches in average accuracy (98.2%).
Breast cancer histological images nuclei segmentation and optimized classification with deep learning
Authors: Khan Fawad Salam , Abbasi Muhammad Inam , Khurram Muhammad , Haji Mohd Mohd Norzali , Khan M. Danial
Published: May 2022

Breast cancer incidences have grown worldwide during the previous few years. The histological images obtained from a biopsy of breast tissues are regarded as being the highest accurate approach to determine whether any cells exhibit symptoms of cancer. The visible position of nuclei inside the image is achieved through the use of instance segmentation, nevertheless, this work involves nucleus segmentation and features classification of the predicted nucleus for the achievement of best accuracy. The extracted features map using the feature pyramid network has been modified using segmenting objects by locations (SOLO) convolution with grasshopper optimization for multiclass classification. A breast cancer multiclassification technique based on a suggested deep learning algorithm was examined to achieve the accuracy of 99.2% using a huge database of ICIAR 2018, demonstrating the method’s efficacy in offering an important weapon for breast cancer multi-classification in a medical setting. The segmentation accuracy achieved is 88.46%.
An agent-based model to assess coronavirus disease 19 spread and health systems burden
Authors: Narassima Madhavarao Seshadri , Anbuudayasankar Singallur Palanisamy , Rajesh Jammy Guru , Sankarshana AnanthaPadmanabhan , Pant Rashmi , Choudhury Lincoln , Yeldandi Vijay , Singh Shubham , John Denny
Published: May 2022

The present pandemic has tremendously raised the health systems’ burden around the globe. It is important to understand the transmission dynamics of the infection and impose localized strategies across different geographies to curtail the spread of the infection. The present study was designed to assess the transmission dynamics and the health systems’ burden of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using an agent-based modeling (ABM) approach. The study used a synthetic population with 31,738,240 agents representing 90.67 percent of the overall population of Telangana, India. The effects of imposing and lifting lockdowns, nonpharmaceutical interventions, and the role of immunity were analyzed. The distribution of people in different health states was measured separately for each district of Telangana. The spread dramatically increased and reached a peak soon after the lockdowns were relaxed. It was evident that is the protection offered is higher when a higher proportion of the population is exposed to the interventions. ABMs help to analyze grassroots details compared to compartmental models. Risk estimates provide insights on the proportion of the population protected by the adoption of one or more of the control measures, which is of practical significance for policymaking.
Development and validation of a tool for measuring digital library engagement
Authors: Rahimi Mohamad Rosman Mohamad , Nasir Ismail Mohd , Noorman Masrek Mohamad
Published: May 2022

Digital library engagement can be defined as the extensive usage of the services, functions, and tools provided by the digital library (DL). Although many instruments have been developed to measure digital library usage, however, the instruments were only measuring a limited aspect of usage, such as behavioral perspective. Therefore, the aim of this study is to establish and confirm a research instrument to measure digital library engagement. The study is conducted on several empirical phases. First, a listof variables was selected. Second, an instrument was developed based on the variables selected. Third, the instrument measuring digital library engagement was validated through expert review process. Fourth, face validity was conducted, before confirming the reliability of the instrument through a pilot test. Lastly, the instrument was validated through quantitative data collection by 492 respondents. As a result, a valid instrument consisting of 14 variables underneath 5 dimensions (technological, individual, contextual, digital library engagement, perceived benefits) and 61 items were produced. The instrument could be employed to identify and assess the state of digital library engagement among practitioners, universities, government, and local communities. This study however is limited in few ways in relation to context coverage, generalization of theory, and variables selection.
Tifinagh handwritten character recognition using optimized convolutional neural network
Authors: Niharmine Lahcen , Outtaj Benaceur , Azouaoui Ahmed
Published: May 2022

Tifinagh handwritten character recognition has been a challenging problem due to the similarity and variability of its alphabets. This paper proposes an optimized convolutional neural network (CNN) architecture for handwritten character recognition. The suggested model of CNN has a multi-layer feed-forward neural network that gets features and properties directly from the input data images. It is based on the newest deep learning open-source Keras Python library. The novelty of the model is to optimize the optical character recognition (OCR) system in order to obtain best performance results in terms of accuracy and execution time. The new optical character recognition system is tested on a customized dataset generated from the amazigh handwritten character database. Experimental results show a good accuracy of the system (99.27%) with an optimal execution time of the classification compared to the previous works.
Effective classification of birds’ species based on transfer learning
Authors: Alswaitti Mohammed , Zihao Liao , Alomoush Waleed , Alrosan Ayat , Alissa Khalid
Published: May 2022

In recent years, with the deterioration of the earth’s ecological environment, the survival of birds has been more threatened. To protect birds and the diversity of species on earth, it is urgent to build an automatic bird image recognition system. Therefore, this paper assesses the performance of traditional machine learning and deep learning models on image recognition. Also, the help-ability of transfer learning in the field of image recognition is tested to evaluate the best model for bird recognition systems. Three groups of classifiers for bird recognition were constructed, namely, classifiers based on the traditional machine learning algorithms, convolutional neural networks, and transfer learning-based convolutional neural networks. After experiments, these three classifiers showed significant differences in the classification effect on the Kaggle-180-birds dataset. The experimental results finally prove that deep learning is more effective than traditional machine learning algorithms in image recognition as the number of bird species increases. Besides, the obtained results show that when the sample data is small, transfer learning can help the deep neural network classifier to improve classification accuracy.
Dialectal Arabic sentiment analysis based on tree-based pipeline optimization tool
Authors: Mihi Soukaina , Ben Ali Brahim Ait , El Bazi Ismail , Arezki Sara , Laachfoubi Nabil
Published: May 2022

The heavy involvement of the Arabic internet users resulted in spreading data written in the Arabic language and creating a vast research area regarding natural language processing (NLP). Sentiment analysis is a growing field of research that is of great importance to everyone considering the high added potential for decision-making and predicting upcoming actions using the texts produced in social networks. Arabic used in microblogging websites, especially Twitter, is highly informal. It is not compliant with neither standards nor spelling regulations making it quite challenging for automatic machine-learning techniques. In this paper’s scope, we propose a new approach based on AutoML methods to improve the efficiency of the sentiment classification process for dialectal Arabic. This approach was validated through benchmarks testing on three different datasets that represent three vernacular forms of Arabic. The obtained results show that the presented framework has significantly increased accuracy than similar works in the literature.
Coronavirus disease situation analysis and prediction using machine learning: a study on Bangladeshi population
Authors: Nayan Al-Akhir , Kijsirikul Boonserm , Iwahori Yuji
Published: May 2022

During a pandemic, early prognostication of patient infected rates can reduce the death by ensuring treatment facility and proper resource allocation. In recent months, the number of death and infected rates has increased more distinguished than before in Bangladesh. The country is struggling to provide moderate medical treatment to many patients. This study distinguishes machine learning models and creates a prediction system to anticipate the infected and death rate for the coming days. Equipping a dataset with data from March 1, 2020, to August 10, 2021, a multi-layer perceptron (MLP) model was trained. The data was managed from a trusted government website and concocted manually for training purposes. Several test cases determine the model's accuracy and prediction capability. The comparison between specific models assumes that the MLP model has more reliable prediction capability than the support vector regression (SVR) and linear regression model. The model presents a report about the risky situation and impending coronavirus disease (COVID-19) attack. According to the prediction produced by the model, Bangladesh may suffer another COVID-19 attack, where the number of infected cases can be between 929 to 2443 and death cases between 19 to 57.
Forest quality assessment based on bird sound recognition using convolutional neural networks
Authors: Effendy Nazrul , Ruhyadi Didi , Pratama Rizky , Rabba Dana Fatadilla , Aulia Ananda Fathunnisa , Atmadja Anugrah Yuwan
Published: May 2022

Deforestation in Indonesia is in a status that is quite alarming. From year to year, deforestation is still happening. The decline in fauna and the diminishing biodiversity are greatly affected by deforestation. This paper proposes a bioacoustics-based forest quality assessment tool using Nvidia Jetson Nano and convolutional neural networks (CNN). The device, named GamaDet, is a portable physical product based on the microprocessor and equipped with a microphone to record the sounds of birds in the forest and display the results of their analysis. In addition, a Google Collaboratorybased GamaNet digital product is also proposed. GamaNet requires forest recording audio files to be further analyzed into a forest quality index. Testing the forest recording for 60 seconds at an arboretum forest showed that both products could work well. The GamaDet takes 370 seconds, while the GamaNet takes 70 seconds to process the audio data into a forest quality index and a list of detected birds.
Employing deep learning for lung sounds classification
Authors: Dhari Satea Huda , Saleem Elameer Amer , Hussein Salman Ahmed , Dhari Sateaa Shahad
Published: May 2022

Respiratory diseases indicate severe medical problems. They cause death for more than three million people annually according to the world health organization (WHO). Recently, with corona virus disease 19 (COVID-19) spreading the situation has become extremely serious. Thus, early detection of infected people is very vital in limiting the spread of respiratory diseases and COVID-19. In this paper, we have examined two different models using convolution neural networks. Firstly, we proposed and build a convolution neural network (CNN) model from scratch for classification the lung breath sounds. Secondly, we employed transfer learning using the pre-trained network AlexNet applying on the similar dataset. Our proposed model achieved an accuracy of 0.91 whereas the transfer learning model performing much better with an accuracy of 0.94.
Neural network modeling of agglomeration firing process for polymetallic ores
Authors: Abitova Gulnara , Nikulin Vladimir , Rzayeva Leila , Zadenova Tansuly , Myrzatay Ali
Published: May 2022

While processing polymetallic ores at the non-ferrous metallurgy problems arises connecting with the excellence of production and the efficient applying the technological devices-firing furnace and crusher machine. In earlier time, similar questions were solved due to the big practice experiences and using a mathematical modeling method. The mathematical model for optimizing those operating mode is a very complex and hard to calculation. Performing computations is needed too much time and many resources. Because the control of the agglomeration furnaces and other machines are including complex multiparameter processes. The method of the math modeling for optimization the operating mode to the firing furnace is replaced with modeling based on the neural network that is here a new method. The results obtained have shown that proposed methods based on the neural network modeling of metallurgical processes allow determining more accurate and adequate results of calculations than mathematical modeling by the traditional program. The use of new approaches for modeling the technological processes at the non-ferrous metallurgy gives opportunity to enhance an effectiveness of production excellence and to enhance an efficient applying the heat-energy equipment while a firing the sulfide polymetallic ores of non-ferrous metallurgy
Protein secondary structure prediction by a neural network architecture with simple positioning algorithm techniques
Authors: Ema Romana Rahman , Sultana Sharmin , Shaj Shakil Ahmed , Galib Syed Md.
Published: May 2022

Protein secondary structure is an immense achievement of bioinformatics. It's an amino acid residue in a polypeptide backbone. In this paper, an innovative method has been proposed for predicting protein secondary structures based on 3-state protein secondary structure by neural network architecture with simple positioning algorithm (SIMPA) technique. Q3 (3-state prediction of protein secondary structure) is a fundamental methodology for our approach. Initially, the prediction of the secondary structure of the protein using the Q3 prediction method has been done. For this, a model has been built from its primary structure. Then it will retrieve the percentage of amino acid sequences from the original sequence to the accuracy of the predicted sequence. Utilizing the SIMPA technique from the 3-state secondary structure predicted sequence, the percentage of dissimilar residues of the three types (α-helix, β-sheet and coil) of Q3 has been extracted. Then the verification of the Q3 predicted accuracy through the SIMPA technique was done. Finally using a new method of neural network, it is verified that the Q3 prediction method gives good results from the neural network approach.
A modified residual network for detection and classification of Alzheimer’s disease
Authors: Hanoon Faten Salim , Hassin Alasadi Abbas Hanon
Published: May 2022

Alzheimer's disease (AD) is a brain disease that significantly declines a person's ability to remember and behave normally. By applying several approaches to distinguish between various stages of AD, neuroimaging data has been used to extract different patterns associated with various phases of AD. However, because the brain patterns of older adults and those in different phases are similar, researchers have had difficulty classifying them. In this paper, the 50-layer ResNet is modified by adding extra convolution layers to make the extracted features more diverse. Besides, the activation function (ReLU) was replaced with (Leaky ReLU) because ReLU takes the negative parts of its input, drops them to zero, and retains the positive parts. These negative inputs may contain useful feature information that could aid in the development of high-level discriminative features. Thus, Leaky ReLU was used instead of ReLU to prevent any potential loss of input information. In order to train the network from scratch without encountering the issue of overfitting, we added a dropout layer before the fully connected layer. The proposed method successfully classified the four stages of AD with an accuracy of 97.49 % and 98 % for precision, recall, and f1-score.