Hasil untuk "Computer software"

M. Kohl, S. Wiese, B. Warscheid

A. Rosset, L. Spadola, O. Ratib

C. Bale, Eve Bélisle, P. Chartrand et al.

T. Werner

Muhammad Afaq Hussain, Zhanlong Chen, Biswajeet Pradhan et al.

Study region: The National Highways 85 and 50, key routes of the China–Pakistan Economic Corridor (CPEC) in Balochistan, Pakistan. Study focus: Flooding is a natural disaster that is becoming increasingly frequent and severe. The National Highways 85 and 50 are vulnerable, necessitating accurate flood susceptibility mapping (FSM). Current machine learning (ML) models for FSM often suffer from low efficiency and overfitting. This study introduces an innovative hybrid FSM approach using four heterogeneous ensemble learning (HEL) techniques combined with three ML models: Random Forest (RF), Support Vector Machine (SVM), and Light Gradient Boosting Machine (LGBM). The proposed method was tested using satellite data from Sentinel-1, Sentinel-2, and Landsat-8, analyzing 1371 flood locations and 12 contributing variables. RF, variable importance factors (VIF), and information gain ratio (IGR) were applied to assess multicollinearity. The dataset was split (70:30) for model training and testing, with HEL-based models achieving superior performance over single ML models. New hydrological insights for the region: The stacking model yielded the highest AUROC (0.98), Kappa (0.82), accuracy (0.927), precision (0.963), Matthew’s correlation coefficient (0.820), and F1-score (0.950). HEL-based models proved more stable and resistant to overfitting. IGR analysis identified slope and distance from streams as key factors in FSM. The resulting flood-prone maps provide insights for disaster management adaptation strategies, demonstrating the broader applicability of the developed approach to enhance FSM accuracy and reliability.

Maali Alabdulhafith, Abduljabbar S. Ba Mahel, Nagwan Abdel Samee et al.

Quality of life is greatly affected by chronic wounds. It requires more intensive care than acute wounds. Schedule follow-up appointments with their doctor to track healing. Good wound treatment promotes healing and fewer problems. Wound care requires precise and reliable wound measurement to optimize patient treatment and outcomes according to evidence-based best practices. Images are used to objectively assess wound state by quantifying key healing parameters. Nevertheless, the robust segmentation of wound images is complex because of the high diversity of wound types and imaging conditions. This study proposes and evaluates a novel hybrid model developed for wound segmentation in medical images. The model combines advanced deep learning techniques with traditional image processing methods to improve the accuracy and reliability of wound segmentation. The main objective is to overcome the limitations of existing segmentation methods (UNet) by leveraging the combined advantages of both paradigms. In our investigation, we introduced a hybrid model architecture, wherein a ResNet34 is utilized as the encoder, and a UNet is employed as the decoder. The combination of ResNet34’s deep representation learning and UNet’s efficient feature extraction yields notable benefits. The architectural design successfully integrated high-level and low-level features, enabling the generation of segmentation maps with high precision and accuracy. Following the implementation of our model to the actual data, we were able to determine the following values for the Intersection over Union (IOU), Dice score, and accuracy: 0.973, 0.986, and 0.9736, respectively. According to the achieved results, the proposed method is more precise and accurate than the current state-of-the-art.

Jiajun Guo, Qingge Ji, Guangwei Shan

Abstract Current skeleton‐based action recognition methods usually assume the input skeleton is complete and noise‐free. However, it is inevitable that the captured skeletons are incomplete due to occlusions or noisy due to changes in the environment. When dealing with these data, even State Of The Art (SOTA) recognition backbones experience significant degradation in recognition accuracy. Though a few methods have been proposed to address this issue, they still lack flexibility, efficiency and interpretability. In this work, an overcomplete Graph Convolutional Denoising Autoencoder (GCDAE) is proposed which can act as a flexible preprocessing module for pretrained recognition backbones and improve their robustness. Taking advantages of the overcomplete and fully graph convolutional structure, GCDAE is able to rectify noisy joints while keeping information of unspoiled details efficiently. On two large scale skeleton datasets NTU RGB+D 60 and 120, the introducing of GCDAE brings significant robustness improvements to SOTA backbones towards different types of noises.

B. Valarmathi, N. Srinivasa Gupta, G. Prakash et al.

Deep learning and computer vision algorithms will be applied to find the breed of the dog from an image. The goal is to have the user submit an image of a dog, and the model will choose one of the 120 breeds stated in the dataset to determine the dog’s breed. The proposed work uses various deep learning algorithms like Xception, VGG19, NASNetMobile, EfficientNetV2M, ResNet152V2, Hybrid of Inception &Xception, and Hybrid of EfficientNetV2M, NASNetMobile, Inception &Xception to predict dog breeds. ResNet101, ResNet50, InceptionResNetV2, and Inception-v3 on the Stanford Dogs Standard Datasetswere used in the existing system. The proposed models are considered a hybrid of Inception-v3 &Xception and a hybrid of EfficientNetV2M, NASNetMobile, Inception & Xception. This hybrid model outperforms single models like Xception, VGG19, InceptionV3, ResNet50, and ResNet101.The authors used a transfer learning algorithm with data augmentation to increase their accuracy and achieved a validation accuracy score of 71.63% for ResNet101, 63.78% for ResNet50, 40.72% for InceptionResNetV2, and 34.84% for InceptionV3. This paper compares the proposed algorithms with existing ones like ResNet101, ResNet50, InceptionResNetV2, and InceptionV3. In the existing system, ResNet101 gave the highest accuracy of 71.63%. The proposed algorithms give a validation accuracy score of 91.9% for Xception, 55% for VGG19, 83.47% for NASNetMobile, 89.05% for EfficientNetV2M, 87.38% for ResNet152V2, 92.4% for Hybrid of Inception-v3 &Xception, and 89.00% for Hybrid of EfficientNetV2M, NASNetMobile, Inception &Xception. Among these algorithms, the Hybrid of Inception-v3 &Xception gives the highest accuracy of 92.4%.

Weidun Xie, Xingjian Chen, Zetian Zheng et al.

Summary: By soaking microRNAs (miRNAs), long non-coding RNAs (lncRNAs) have the potential to regulate gene expression. Few methods have been created based on this mechanism to anticipate the lncRNA-gene relationship prediction. Hence, we present lncRNA-Top to forecast potential lncRNA-gene regulation relationships. Specifically, we constructed controlled deep-learning methods using 12417 lncRNAs and 16127 genes. We have provided retrospective and innovative views among negative sampling, random seeds, cross-validation, metrics, and independent datasets. The AUC, AUPR, and our defined precision@k were leveraged to evaluate performance. In-depth case studies demonstrate that 47 out of 100 projected top unknown pairings were recorded in publications, supporting the predictive power. Our additional software can annotate the scores with target candidates. The lncRNA-Top will be a helpful tool to uncover prospective lncRNA targets and better comprehend the regulatory processes of lncRNAs.

Ephrem Abathun Ayalew, Ditaba David Mphuthi, Kholofelo Lorraine Matlhaba

Abstract Background One of the major challenges for healthcare professionals relates to awareness of patients’ preferences relative to how and when to break bad news and how much information should be disclosed in the eventuality of a serious medical diagnosis or prognosis. On occasions, a serious medical diagnosis or prognosis is withheld from the patient. There is a scarcity of evidence about cultural preferences regarding breaking bad news in the palliative care setting in Ethiopia. Therefore, it is necessary to understand the surrounding cultural issues to properly convey bad news. The purpose of the study was to explore Ethiopian patients’ cultural preferences for receiving bad news in a palliative care setting. Methods A qualitative research approach and nonprobability, purposive sampling method were applied. In-depth interviews were employed to collect data from eight patients who were diagnosed with cancer and cancer with HIV/AIDS during the time of data collection. Thematic analysis was applied to identify themes and subthemes. The data were transcribed verbatim and analysed using ATLAS.ti 22 computer software. Results The following three themes emerged and are reported in this study: (1) Perceptions about life-threatening illness: religious values and rituals are essential for establishing perspectives on life-threatening illnesses and preferences in receiving bad news. (2) Experiences with life-threatening illness: study participants’ experience with the method of breaking bad news was sad, and they were not provided with sufficient details about their medical condition. Making appropriate decisions, fulfilling the ordinance of religious faith, and avoiding unnecessary costs were outlined as benefits of receiving bad news. (3) Preferred ways of breaking bad news; the findings revealed that incremental, amiable and empathic methods for delivering bad news were preferred. It was suggested that the presence of family members is crucial when receiving bad news. Conclusion Patients choose to be told about their medical conditions in the presence of their family. However, the patient’s needs for receiving bad news were unmet. Patients should be involved in the treatment decision process. Delivery of bad news needs to tailor the preferred methods, cultural values, and religious beliefs. Delivering bad news according to the patients’ preferences helps to fulfil their wishes in palliative care.

Yuu Ichikawa, Keiichi Mizutani, Hiroshi Harada

The simplified universal time-domain windowed-orthogonal frequency-division multiplexing (Simplified UTW-OFDM) has been proposed to improve spectral efficiency. This study proposed a novel receiving method using an optimal inter-carrier interference (ICI) cancellation technique to improve communication quality during the application of the simplified UTW-OFDM to 5G. The simplified UTW-OFDM suppresses out-of-band emission (OOBE) through the application of very long time-domain windowing to the conventional OFDM with a cyclic prefix (CP-OFDM) symbol. However, in exchange for the OOBE suppression performance, a large ICI is generated due to the symbol distortion caused by the application of the time-domain window, resulting in the degradation of the reception quality. Therefore, in this study, we proposed a new method for calculating the log-likelihood ratio that takes into account the effect of the time-domain window. Further, an ICI canceller that included a process for suppressing the noise enhancement effect of the time-domain window was proposed. The effectiveness of the proposed method was evaluated through computer simulations and experiments using software-defined radio. The experimental evaluation showed that the proposed ICI canceller could suppress the OOBE by 23.5 dB compared to CP-OFDM under the condition that BLER of 0.1 is achieved even when 64QAM is applied.

Fengming Sun, Junjie Cui, Xia Yuan et al.

Abstract Fully convolutional neural networks‐based salient object detection has recently achieved great success with its performance benefits from the effective use of multi‐layer features. Based on this, most of the existing saliency detectors designed complex network structures to fuse the multi‐level features generated by the backbone network. However, the variable scale and complex shape of the target are always a great challenge for saliency detection tasks. In this paper, the authors propose a Rich‐scale Feature Fusion Network (RFFNet) for salient object detection. The authors design a rich‐scale feature interactive fusion module to obtain more efficient features from the multi‐scale features. Moreover, the global feature enhance module is used to extract features with better characterization for the final saliency prediction. Extensive experiments performed on five benchmark datasets demonstrate that the proposed method can achieve satisfactory results on different evaluation metrics compared to other state‐of‐the‐art salient object detection approaches.

Meri Azmi, Deni Satria, Farhan Rinsky Mulya et al.

The industrial sector's growth has led to an increase in the number of industrial products available in the market. However, this has made it more challenging for retail merchants to choose which items to sell due to the overwhelming number of options. The seller must carefully consider various factors such as the type, quality, and probability of selling the goods to turn a profit. This research proposes a group decision support system to assist retail sellers in selecting the products to sell. The system is designed to process various information on comparing retail products against specific criteria, enabling sellers to make quick and accurate decisions. To achieve optimal results, this study combines three methods in the decision-making calculation process: Fuzzy Logic, EDAS, and Borda methods. The Fuzzy Logic method is used to assign a value to an unclear criterion, followed by the EDAS method ranking process, and ending with the combination of the decision-making results using the Borda method. The group decision support system is web-based and has been proven to provide effective solutions for retail business actors to increase sales and reduce losses. By using this system, retail sellers can make informed decisions about their products, enabling them to optimize their profits and reduce their risks. In conclusion, the increase in the number of industrial products has created challenges for retail merchants, but this research proposes a solution in the form of a group decision support system. Combining Fuzzy Logic, EDAS, and Borda methods results in an effective decision-making process that allows retail sellers to make informed decisions and achieve their business goals.

Md. Arif Hossain, Ashik Ahmed, Shafiqur Rahman Tito et al.

An optimal energy mix of various renewable energy sources and storage devices is critical for a profitable and reliable hybrid microgrid system. This work proposes a hybrid optimization method to assess the optimal energy mix of wind, photovoltaic, and battery for a hybrid system development. This study considers the hybridization of a Non-dominant Sorting Genetic Algorithm II (NSGA II) and the Grey Wolf Optimizer (GWO). The objective function was formulated to simultaneously minimize the total energy cost and loss of power supply probability. A comparative study among the proposed hybrid optimization method, Non-dominant Sorting Genetic Algorithm II, and multi-objective Particle Swarm Optimization (PSO) was performed to examine the efficiency of the proposed optimization method. The analysis shows that the applied hybrid optimization method performs better than other multi-objective optimization algorithms alone in terms of convergence speed, reaching global minima, lower mean (for minimization objective), and a higher standard deviation. The analysis also reveals that by relaxing the loss of power supply probability from 0% to 4.7%, an additional cost reduction of approximately 12.12% can be achieved. The proposed method can provide improved flexibility to the stakeholders to select the optimum combination of generation mix from the offered solutions.

Weiwen Wu, Dianlin Hu, Wenxiang Cong et al.

Summary: Due to lack of the kernel awareness, some popular deep image reconstruction networks are unstable. To address this problem, here we introduce the bounded relative error norm (BREN) property, which is a special case of the Lipschitz continuity. Then, we perform a convergence study consisting of two parts: (1) a heuristic analysis on the convergence of the analytic compressed iterative deep (ACID) scheme (with the simplification that the CS module achieves a perfect sparsification), and (2) a mathematically denser analysis (with the two approximations: [1] AT is viewed as an inverse A-1 in the perspective of an iterative reconstruction procedure and [2] a pseudo-inverse is used for a total variation operator H). Also, we present adversarial attack algorithms to perturb the selected reconstruction networks respectively and, more importantly, to attack the ACID workflow as a whole. Finally, we show the numerical convergence of the ACID iteration in terms of the Lipschitz constant and the local stability against noise. The bigger picture: For deep tomographic reconstruction to realize its full potential in practice, it is critically important to address the instabilities of deep reconstruction networks, which were identified in a recent PNAS paper. Our analytic compressed iterative deep (ACID) framework has provided an effective solution to address this challenge by synergizing deep learning and compressed sensing through iterative refinement. Here, we provide an initial convergence analysis, describe an algorithm to attack the entire ACID workflow, and establish not only its capability of stabilizing an unstable deep reconstruction network but also its stability against adversarial attacks dedicated to ACID as a whole. Although our theoretical results are under approximations, they shed light on the converging mechanism of ACID, serving as a basis for further investigation.

Yaping Wang, Xinguang Xu, Xian Zhao et al.

Abstract Electrically controlled half-metallicity in antiferromagnets is of great significance for both fundamental research and practical application. Here, by constructing van der Waals heterostructures composed of two-dimensional (2D) A-type antiferromagnetic NiI2 bilayer (bi-NiI2) and ferroelectric In2Se3 with different thickness, we propose that the half-metallicity is realizable and switchable in the bi-NiI2 proximate to In2Se3 bilayer (bi-In2Se3). The polarization flipping of the bi-In2Se3 successfully drives transition between half-metal and semiconductor for the bi-NiI2. This intriguing phenomenon is attributed to the joint effect of polarization field-induced energy band shift and interfacial charge transfer. Besides, the easy magnetization axis of the bi-NiI2 is also dependent on the polarization direction of the bi-In2Se3. The half-metallicity and magnetic anisotropy energy of the bi-NiI2 in heterostructure can be effectively manipulated by strain. These findings provide not only a feasible strategy to achieve and control half-metallicity in 2D antiferromagnets, but also a promising candidate to design advanced nanodevices.

Firdews A.Alsalman, Shler Khorshid, Amira Sallow

Machine learning and deep learning algorithms have become increasingly important in the medical field, especially for diagnosing disease using medical databases. Techniques developed within these two fields are now used to classify different diseases. Although the number of Machine Learning algorithms is vast and increasing, the number of frameworks and libraries that implement them is also vast and growing.  TensorFlow is a well-known machine learning library that has been used by several researchers in the field of disease classification. With the help of TensorFlow (Google's framework), a complex calculation can be addressed effectively by modeling it as a graph and properly mapping the graph segments to the machine in the form of a cluster. In this review paper, the role of the TensorFlow-Python framework- for disease classification is discussed.

W. R. Adrion, M. Branstad, J. Cherniavsky

Ihor Butko

У статті запропоновано модель та метод прийняття управлінських рішень на основі аналізу геопросторової інформації. Метою статті є удосконалення моделі та методу прийняття управлінських рішень на основі аналізу геопросторової інформації. Результати: запропоновано алгоритм процесу прийняття управлінського рішення, який складається з ситуаційної та концептуальної частини; запропоновано алгоритм дій керівника організації на основі розробленої моделі прийняття управлінського рішення; розглянута ситуація, коли якість рішення залежить від зовнішніх факторів, на які орган прийняття рішення не впливає; наведена загальна схема методу прийняття управлінських рішень на основі аналізу геопросторової інформації. Використовуваними методами є: методи системного аналізу, теорії прийняття рішень, обробки інформації, оптимальних рішень, теорії ймовірності. Висновки. Удосконалено модель прийняття управлінських рішень, яка, на відміну від відомих, є динамічною і базується на відборі рішень, що є оптимальними за комбінованим критерієм, при цьому використовується прогнозні значення імовірностей станів середовища, що забезпечує обґрунтованість управлінських рішень. Отримав подальший розвиток метод прийняття управлінських рішень на основі аналізу геопросторової інформації, який базується на моделях прогнозування даних та прийняття управлінських рішень і використовує метод семантичної сегментації видових зображень для оцінки апріорних імовірностей станів середовища, що забезпечує можливість прийняття рішення в умовах ризику та невизначенності. Напрямком подальших досліджень є розробка інформаційної технології прийняття управлінських рішень на основі аналізу геопросторової інформації.

Mohammed A. Alanezi, Houssem R. E. H. Bouchekara, Muhammad S. Javaid

Internet of Things (IoT) is characterized by a system of interconnected devices capable of communicating with each other to carry out specific useful tasks. The connection between these devices is ensured by routers distributed in a network. Optimizing the placement of these routers in a distributed wireless sensor network (WSN) in a smart building is a tedious task. Computer-Aided Design (CAD) programs and software can simplify this task since they provide a robust and efficient tool. At the same time, experienced engineers from different backgrounds must play a prominent role in the abovementioned task. Therefore, specialized companies rely on both; a useful CAD tool along with the experience and the flair of a sound expert/engineer to optimally place routers in a WSN. This paper aims to develop a new approach based on the interaction between an efficient CAD tool and an experienced engineer for the optimal placement of routers in smart buildings for IoT applications. The approach follows a step-by-step procedure to weave an optimal network infrastructure, having both automatic and designer-intervention modes. Several case studies have been investigated, and the obtained results show that the developed approach produces a synthesized network with full coverage and a reduced number of routers.