Hyperband-Optimized CNN-BiLSTM with Attention Mechanism for Corporate Financial Distress Prediction by Yingying Song, Monchaya Chiangpradit, Piyapatr Busababodhin

Get full text

A Machine Learning Approach to Predict Site Selection from the Perspective of Vitality Improvement by Bin Zhao, Hao Zheng, Xuesong Cheng

“…To enhance site selection and planning efficiency, we developed a predictive model integrating Artificial Neural Networks (ANNs) and Genetic Algorithms (GAs). …”
Get full text

Suggesting a Novel Hybrid Approach for Predicting Solar Irradiance in the Qinghai Province of China by Baran Yılmaz, Rachel Samra

“…In this work, long short-term memory has been optimized using Particle swarm optimization, Grasshopper optimization algorithm, and Slime mold algorithm. SMA-LSTM, which has the best performance result compared to other developed models, is presented as the main method for this work. …”
Get full text

Enhancing Intrusion Detection Systems with Dimensionality Reduction and Multi-Stacking Ensemble Techniques by Ali Mohammed Alsaffar, Mostafa Nouri-Baygi, Hamed Zolbanin

“…To overcome these limitations, this paper presents an innovative approach that integrates dimensionality reduction and stacking ensemble techniques. We employ the LogitBoost algorithm with XGBRegressor for feature selection, complemented by a Residual Network (ResNet) deep learning model for feature extraction. …”
Get full text

Enhancing Cloud Security: A Multi-Factor Authentication and Adaptive Cryptography Approach Using Machine Learning Techniques by K. Sasikumar, Sivakumar Nagarajan

Subjects: Get full text

Half-hourly electricity price prediction model with explainable-decomposition hybrid deep learning approach by Sujan Ghimire, Ravinesh C. Deo, Konstantin Hopf, Hangyue Liu, David Casillas-Pérez, Andreas Helwig, Salvin S. Prasad, Jorge Pérez-Aracil, Prabal Datta Barua, Sancho Salcedo-Sanz

“…Input features are identified using the Partial Autocorrelation Function , and models are optimized using the Optuna algorithm. The final prediction combines the trend, seasonal, and residual components’ predictions. …”
Get full text

Automatic Bone Fracture Reduction Technique with Section Registration by Qinhui YUAN, Mengxing LIU, Chu GUO, Yukun AN, Ping ZHOU

“…In order to enhance treatment efficiency and accuracy, an automatic fracture reduction algorithm is proposed. This algorithm utilizes the similarity of fracture cross-sections for registration, thereby reducing the workload of physicians and eliminating the need for a healthy-side bone template. …”
Get full text

Big Data Reduction and Optimization in Sensor Monitoring Network by Bin He, Yonggang Li

Get full text

A Parallel Attribute Reduction Method Based on Classification by Deguang Li, Zhanyou Cui

“…Finally, the proposed algorithm and the traditional algorithm are analyzed and compared by experiments, and the results show that the proposed method in this paper has more advantages in time efficiency, which proves that the method could improve the processing efficiency of attribute reduction and makes it more suitable for massive data sets.…”
Get full text

The application of adaptive symmetry reduction for LTL model checking by I. V. Konnov, V. A. Zakharov

“…Adaptive symmetry reduction is a technique which exploits the similarity of com- ponents in systems of regular structure. …”
Get full text

Modern algorithms for statin therapy by V. A. Susekov

Get full text

Retracted: Prediction of stock market movement via technical analysis of stock data stored on blockchain using novel History Bits based machine learning algorithm by Nitin Nandkumar Sakhare, Imambi S. Shaik, Suman Saha

“…Shaik, Suman Saha, Prediction of stock market movement via technical analysis of stock data stored on blockchain using novel History Bits based machine learning algorithm, IET Software 2023 (https://doi.org/10.1049/sfw2.12092)]. …”
Get full text

A python approach for prediction of physicochemical properties of anti-arrhythmia drugs using topological descriptors by Huiling Qin, Mudassar Rehman, Muhammad Farhan Hanif, Muhammad Yousaf Bhatti, Muhammad Kamran Siddiqui, Mohamed Abubakar Fiidow

Get full text

Classification of imbalanced travel mode choice dataset with SMOTE and prediction using interpretable machine learning by Mujahid Ali

“…Therefore, the current study used the Synthetic Minority Oversampling Technique (SMOTE) to balance the TMC dataset and used several ML algorithms such as random forest (RF), decision tree (DT), Extreme Gradient Boosting (XGBoost), K-nearest Neighbors (KNN), and logistic regression for the prediction of TMC. …”
Get full text

Predictive analytics of complex healthcare systems using deep learning based disease diagnosis model by Muhammad Kashif Saeed, Alanoud Al Mazroa, Bandar M. Alghamdi, Fouad Shoie Alallah, Abdulrhman Alshareef, Ahmed Mahmud

“…This manuscript proposes the Predictive Analytics of Complex Healthcare Systems Using the DL-based Disease Diagnosis Model (PACHS-DLBDDM) method. …”
Get full text

Development and validation of a predictive nomogram for bilateral posterior condylar displacement using cone-beam computed tomography and machine-learning algorithms: a retrospective observational study by Huachao Sui, Mo Xiao, Xueqing Jiang, Jiaye Li, Feng Qiao, Bin Yin, Yuanyuan Wang, Ligeng Wu

“…Therefore, in this study, we aimed to develop and evaluate a predictive model for bilateral posterior condylar displacement. …”
Get full text

Prediction of Lithium-Ion Battery State of Health Using a Deep Hybrid Kernel Extreme Learning Machine Optimized by the Improved Black-Winged Kite Algorithm by Juncheng Fu, Zhengxiang Song, Jinhao Meng, Chunling Wu

“…Addressing the non-linear and non-stationary characteristics of battery capacity sequences, a novel method for predicting lithium battery SOH is proposed using a deep hybrid kernel extreme learning machine (DHKELM) optimized by the improved black-winged kite algorithm (IBKA). …”
Get full text

Harnessing AI for sustainable higher education: ethical considerations, operational efficiency, and future directions by Sunawar Khan, Tehseen Mazhar, Tariq Shahzad, Muhammad Amir Khan, Ateeq Ur Rehman, Mamoon M. Saeed, Habib Hamam

Subjects: Get full text

i-DENV: development of QSAR based regression models for predicting inhibitors targeting non-structural (NS) proteins of dengue virus by Sakshi Gautam, Anamika Thakur, Manoj Kumar

Get full text

A novel problem-solving method by multi-computational optimisation of artificial neural network for modelling and prediction of the flow erosion processes by Hossein Moayedi, Atefeh Ahmadi Dehrashid, Binh Nguyen Le

Get full text