Accurate runoff prediction is one of the important tasks in various fields such as agriculture, hydrology, and environmental studies. Recently, with massive improvements of computational system and hardware, the deep learning-based approach has recen...
Accurate urban water demand (UWD) forecasts are key to the effective management of water distribution systems. This research explores the potential of encoder–decoder models, specifically sequence-to-sequence (S2S) deep learning models, for UWD...
In integrated circuit (IC) design, floorplanning is an important stage in obtaining the floorplan of the circuit to be designed. Floorplanning determines the performance, size, yield, and reliability of very large-scale integration circuit (VLSI) ICs...
The solidifying steel follows highly nonlinear thermo-mechanical behavior depending on the loading history, temperature, and metallurgical phase fraction calculations (liquid, ferrite, and austenite). Numerical modeling with a computationally challen...
Recently proposed recommendation systems based on embedding vector technology allow us to utilize a wide range of information such as user side and item side information to predict user preferences. Since there is a lack of ability to use the sequent...
Commercial e-learning platforms have to overcome the challenge of resource overload and find the most suitable material for educators using a recommendation system (RS) when an exponential increase occurs in the amount of available online educational...
This study addresses the performance of deep learning models for predicting human DNA sequence classification through an exploration of ideal feature representation, model architecture, and hyperparameter tuning. It contrasts traditional machine lear...
This study addresses the challenge of DNA sequence similarity analysis by combining deep learning with DNABERT embeddings. Traditional alignment methods based on direct pairwise comparisons often fail to detect deeper biological relationships beyond...
At present, substation operation ticket system is developed based on an expert system, which has some problems such as knowledge base redundancy, intelligence deficiency and automatic learning ability. To solve this problem, this paper proposes an op...
Driving risk prediction is crucial for advanced driving technologies, with deep learning approaches leading the way in driving safety analysis. Current driving risk prediction methods typically establish a mapping between driving features and risk st...
Accurate forecasting is essential for effective energy management, particularly in evolving and data-driven electricity markets. To address the increasing complexity of national energy planning in The Netherlands, this study proposes a hybrid multi-s...
A flood of information has occurred, making it challenging for people to find and filter their favorite items. Recommendation systems (RSs) have emerged as a solution to this problem; however, traditional Appenrecommendation systems, including collab...
The collapse pressure equivalent density is a key parameter for determining the safe drilling fluid density window and evaluating wellbore stability. To address the limited number of drilled wells, the lack of continuous geomechanical labels, and the...
The interpretation of Myocardial Infarction (MI) via electrocardiogram (ECG) signal is a challenging task. ECG signals’ morphological view show significant variation in different patients under different physical conditions. Several learning al...
Predicting students’ performance is one of the most important issues in educational data mining. In this study, a method for representing students’ partial sequence of learning activities is proposed, and an early prediction model of stud...
Electricity load forecasting is a crucial undertaking within all the deregulated markets globally. Among the research challenges on a global scale, the investigation of deep transfer learning (DTL) in the field of electricity load forecasting represe...
Although deep learning (DL) algorithms have been proved to be effective in diverse research domains, their application in developing models for tabular data remains limited. Models trained on tabular data demonstrate higher efficacy using traditional...
DNA sequence classification is a fundamental problem in bioinformatics, playing an indispensable role in gene annotation and disease prediction. Whereas most deep learning models, such as CNNs, BiLSTM networks, and GRUs, have been found individually...
In the field of forensic document examination, accurately determining the chronological sequence of intersecting lines between seal ink and handwriting is a crucial technical step for verifying document authenticity, identifying contract tampering, a...
Alzheimer’s disease (AD) is an irreversible neurodegenerative disease. Providing trustworthy AD progression predictions for at-risk individuals contributes to early identification of AD patients and holds significant value in discovering effect...
We propose a method for minimizing global buffer access within a deep learning accelerator for convolution operations by maximizing the data reuse through a local register file, thereby substituting the local register file access for the power-hungry...
To address the issue of increasing navigation errors in low-cost autonomous underwater vehicles (AUVs) operating without assisted positioning underwater, this paper proposes a Virtual GPS Model (VGPSM) based on deep sequence learning. This model is i...
Early and accurate cancer detection from medical imaging remains challenging because clinically relevant evidence is distributed across local image appearance, structural relationships between suspicious regions, and ordered imaging context. This stu...
Evaluating the response to open questions is a complex process since it requires prior knowledge of a specific topic and language. The computational challenge is to analyze the text by learning from a set of correct examples to train a model and then...
Commercial buildings in hot and humid tropical climates rely significantly on cooling systems to maintain optimal occupant comfort. A well-accurate day-ahead load profile prediction plays a pivotal role in planning the energy requirements of cooling...
With the rapid increase in the number of Earth Observation Satellites (EOSs), research on autonomous mission scheduling has become increasingly critical for optimizing satellite sensor operations. While most existing studies focus on static environme...
Human Activity Recognition (HAR) has gained significant attention due to its broad range of applications, such as healthcare, industrial work safety, activity assistance, and driver monitoring. Most prior HAR systems are based on recorded sensor data...
The ongoing global energy transition has intensified the need for precise modeling of renewable energy systems, especially photovoltaic–thermal (PV/T) systems that have the ability to produce both electrical and thermal energy. Improving the ef...
Accurate field-scale surface soil moisture (SSM) measures are important for agricultural water management. Conventional satellite SSM products remain too coarse for within-field applications. Here, we developed sequence-aware deep learning models for...
The time-varying, dynamic, nonlinear, and other characteristics of lithium-ion batteries, as well as the capacity regeneration phenomenon, leads to the low accuracy of the traditional deep learning models in predicting the remaining useful life of li...
Knee osteoarthritis (KOA) is a highly prevalent muscoloskeletal joint disorder affecting a significant portion of the population worldwide. Accurate predictions of KOA progression can assist clinicians in drawing preventive strategies for patients. I...
With developments in artificial intelligence (AI), it is possible for novel applications to utilize deep learning to compose music by the format of musical instrument digital interface (MIDI) even without any knowledge of musical theory. The composed...
Background: COVID-19 genetic sequence research is crucial despite immunizations and pandemic control. COVID-19-causing SARS-CoV-2 must be understood genomically for several reasons. New viral strains may resist vaccines. Categorizing genetic sequence...
Reinforcement learning (RL)–based car-following (CF) control strategies have attracted significant attention in academia, emerging as a prominent research topic in recent years. Most of these control strategies focus solely on the motion status...
The sheaf cohomology techniques are newly used to include Morse simplicial complexes in a rectangular-matrix chain, whose singular values are compatible with those of a square matrix, which can be used for multiple sequencing. The equivalence with th...
Several studies have linked disruptions of protein stability and its normal functions to disease. Therefore, during the last few decades, many tools have been developed to predict the free energy changes upon protein residue variations. Most of these...
During a colposcopic examination of the uterine cervix for cervical cancer prevention, one or more digital images are typically acquired after the application of diluted acetic acid. An alternative approach is to acquire a sequence of images at fixed...
Background: Diffusion-weighted images (DWI) obtained by echo-planar imaging (EPI) are frequently degraded by susceptibility artifacts. It has been suggested that DWI obtained by fast advanced spin-echo (FASE) or reconstructed with deep learning recon...
This study explores reinforcement learning algorithms combined with graph embedding methods to optimize the assembly sequence of complex single-layer reticulate shells. To minimize the number of temporary support brackets during installation, the str...
Targeted sequencing has been widely utilized for genomic molecular diagnostics and the emerging DNA data storage paradigm. However, the probe sequences used to enrich regions of interest have different hybridization kinetic properties, resulting in p...
With the explosive growth of sequencing data, rapidly and accurately classifying and identifying species has become a critical challenge in amplicon analysis research. The internal transcribed spacer (ITS) region is widely used for fungal species cla...
Multi-parametric industrial time-series forecasting is important for predictive monitoring, soft sensing, and decision support in complex process industries. This study proposes a process-informed hybrid ARX-residual deep sequence architecture for sh...
The robot task sequencing problem and trajectory planning problem are two important issues in the robotic optimization domain and are solved sequentially in two separate levels in traditional studies. This paradigm disregards the potential synergisti...
Due to the rapid emergence of malware and its greater harm, the successful execution of malware often brings incalculable losses. Consequently, the detection of malware has become increasingly crucial. The sequence of API calls in software embodies s...
Cancer is one of the most deadly diseases, costing millions of lives and billions of USD every year. There are different ways to identify the biomarkers that can be used to detect cancer types and subtypes. RNA sequencing is steadily taking the lead...
2 January 2022
The highly rapid spread of the current pandemic has quickly overwhelmed hospitals all over the world and motivated extensive research to address a wide range of emerging problems. The unforeseen influx of COVID-19 patients to hospitals has made it in...
Task planning involving multiple unmanned aerial vehicles (UAVs) is one of the main research topics in the field of cooperative unmanned aerial vehicle control systems. This is a complex optimization problem where task allocation and path planning ar...
Time series analysis using long short term memory (LSTM) deep learning is a very attractive strategy to achieve accurate electric load forecasting. Although it outperforms most machine learning approaches, the LSTM forecasting model still reveals a l...
Long non-coding RNA (LncRNA) and microRNA (miRNA) are both non-coding RNAs that play significant regulatory roles in many life processes. There is cumulating evidence showing that the interaction patterns between lncRNAs and miRNAs are highly related...
Characterization of gene regulatory mechanisms in cancer is a key task in cancer genomics. CCCTC-binding factor (CTCF), a DNA binding protein, exhibits specific binding patterns in the genome of cancer cells and has a non-canonical function to facili...
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