New chaos-integrated improved grey wolf optimization based models for automatic detection of depression in online social media and networks.

Depression is a psychological effect of the modern lifestyle on people's thoughts. It is a serious individual and social health problem due to the risk of suicide and loss of workforce, high chronicity, recurrence rates, and prevalence. Therefore, identification, prevention, treatment of depression, and determination of relapse risk factors are of great importance. Depression has traditionally been diagnosed using standardized scales that require clinical diagnoses or patients' subjective responses. However, these classical techniques have some limitations such as cost, uncomfortability, subjectivity, and ineffectiveness. Social media data can be simply and efficiently used for depression detection because it allows instantaneous emotional expression and quick access to various information. Some machine learning-based methods are used for detecting the depression in online social media and networks. Nevertheless, these algorithms suffer from several drawbacks, including data sparsity, dimension explosion, restricted capacity for generalization, and low performance on imbalanced data sets. Furthermore, many machine learning methods work as black-box models, and the constructed depression detection models are not interpretable and explainable. Intelligent metaheuristic optimization algorithms are widely used for different types of complex real-world problems due to their simplicity and high performance. It is aimed to remove the limitations of studies on this problem by increasing the success rate and automatically selecting the relevant features and integrating the explainability. In this study, new chaos-integrated multi-objective optimization algorithms are proposed to increase efficiency. New improved Grey Wolf Optimization algorithms have been proposed by integrating Circle, Logistic, and Iterative chaotic maps into the improved Grey Wolf Optimization algorithm. It is aimed to increase the success rate by proposing a multi-objective fitness function that can optimize the accuracy and the number of features simultaneously. The proposed algorithms are compared with different types of popular supervised machine learning algorithms and current metaheuristic algorithms that are widely and successfully used in depression detection problems. Experimental results show that the proposed models outperform machine learning methods, as evidenced by examining results with accuracy, F-measure, MCC, sensitivity, and precision measures. An accuracy value of 100% was obtained from proposed algorithms. In addition, when the confusion matrices are examined, it is seen that they exhibit a successful distribution. Although it is a new research and application area for optimization theory, promising results have been obtained from the proposed models.

Modeling the Energy Consumption of R600a Gas in a Refrigeration System with New Explainable Artificial Intelligence Methods Based on Hybrid Optimization.

Refrigerant gases, an essential cooling system component, are used in different processes according to their thermophysical properties and energy consumption values. The low global warming potential and energy consumption values of refrigerant gases are primarily preferred in terms of use. Recently, studies on modeling properties such as compressor energy consumption, efficiency coefficient, exergy, and thermophysical properties of refrigerants in refrigeration systems with artificial intelligence methods has become increasingly common. In this study, a hybrid-optimization-based artificial intelligence classification method is applied for the first time to produce explainable, interpretable, and transparent models of compressor energy consumption in a vapor compression refrigeration system operating with R600a refrigerant gas. This methodological innovation obtains models that determine the energy consumption values of R600a gas according to the operating parameters. From these models, the operating conditions with the lowest energy consumption are automatically revealed. The innovative artificial intelligence method applied for the energy consumption value determines the system's energy consumption according to the operating temperatures and pressures of the evaporator and condenser unit. When the obtained energy consumption model results were compared with the experimental results, it was seen that it had an accuracy of 84.4%. From this explainable artificial intelligence method, which is applied for the first time in the field of refrigerant gas, the most suitable operating conditions that can be achieved based on the minimum, medium, and maximum energy consumption ranges of different refrigerant gases can be determined.

Assessment of PET/CT: National Electrical Manufacturers Association NU2-2018 Standards versus American Association of Physicists in Medicine Task Group 126.

OBJECTIVE: The aim of this study was to perform and evaluate PET/computed tomography acceptance tests separately using American Association of Physicists in Medicine (AAPM) Task Group 126 and National Electrical Manufacturers Association (NEMA) methods. MATERIALS AND METHODS: Measurements of sensitivity, spatial resolution, count rate performance and scatter fraction, the accuracy of corrections for count losses and randoms, and image quality were obtained according to NEMA NU-2018. Likewise, the performance tests were made using the AAPM Task Group 126 method, and the results were compared with NEMA NU-2018. RESULTS: The sensitivity at the isocenter was 8.87 cps/kBq according to NEMA and 7.60 cps/kBq by using the AAPM Task Group 126. For the spatial resolution, the full width at half maximum (FWHM) and FWTM values were 4.34 mm and 6.78 mm at 1 cm radial offset by NEMA, while AAPM Task Group 126 yielded FWHM and FWTM values of 4.42 mm and 8.14 mm, respectively. In the image quality, NEMA exhibited hot lesions contrast of 40.8, 56.7, 69.9, and 77.3 for 10, 13, 17, and 22 mm spheres, respectively. As a ratio to 25 mm, the "Hot" max standard uptake values by AAPM Task Group 126 were found to be 1, 1.1, 1.37, and 1.68 for 8, 12, 16, and 25 mm lesions, respectively. CONCLUSION: Acceptance tests using NEMA are of high relevance and convenience for the reliability of the results. Alternatively, AAPM Task Group 126 seems convenient and more economical to apply with reliable outcomes for the equivalent tests.

Multi-feature fusion and improved BO and IGWO metaheuristics based models for automatically diagnosing the sleep disorders from sleep sounds.

A night of regular and quality sleep is vital in human life. Sleep quality has a great impact on the daily life of people and those around them. Sounds such as snoring reduce not only the sleep quality of the person but also reduce the sleep quality of the partner. Sleep disorders can be eliminated by examining the sounds that people make at night. It is a very difficult process to follow and treat this process by experts. Therefore, this study, it is aimed to diagnose sleep disorders using computer-aided systems. In the study, the used data set contains seven hundred sound data which has seven different sound class such as cough, farting, laugh, scream, sneeze, sniffle, and snore. In the model proposed in the study, firstly, the feature maps of the sound signals in the data set were extracted. Three different methods were used in the feature extraction process. These methods are MFCC, Mel-spectrogram, and Chroma. The features extracted in these three methods are combined. Thanks to this method, the features of the same sound signal extracted in three different methods are used. This increases the performance of the proposed model. Later, the combined feature maps were analyzed using the proposed New Improved Gray Wolf Optimization (NI-GWO), which is the improved version of the Improved Gray Wolf Optimization (I-GWO) algorithm, and the proposed Improved Bonobo Optimizer (IBO) algorithm, which is the improved version of the Bonobo Optimizer (BO). In this way, it is aimed to run the models faster, reduce the number of features, and obtain the most optimum result. Finally, Support Vector Machine (SVM) and k-nearest neighbors (KNN) supervised shallow machine learning methods were used to calculate the metaheuristic algorithms' fitness values. Different types of metrics such as accuracy, sensitivity, F1 etc., were used for the performance comparison. Using the feature maps optimized by the proposed NI-GWO and IBO algorithms, the highest accuracy value was obtained from the SVM classifier with 99.28% for both metaheuristic algorithms.

A new hybrid method based on Aquila optimizer and tangent search algorithm for global optimization.

Since no single algorithm can provide the optimal solutions for all problems, new metaheuristic methods are always being proposed or developed by combining current algorithms or creating adaptable versions. Metaheuristic methods should have a balanced exploitation and exploration stages. One of these two talents may be sufficient in some metaheuristic methods, while the other may be insufficient. By integrating the strengths of the two algorithms and hybridizing them, a more efficient algorithm can be formed. In this paper, the Aquila optimizer-tangent search algorithm (AO-TSA) is proposed as a new hybrid approach that uses the intensification stage of the tangent search algorithm (TSA) instead of the limited exploration stage to improve the Aquila optimizer's exploitation capabilities (AO). In addition, the local minimum escape stage of TSA is applied in AO-TSA to avoid the local minimum stagnation problem. The performance of AO-TSA is compared with other current metaheuristic algorithms using a total of twenty-one benchmark functions consisting of six unimodal, six multimodal, six fixed-dimension multimodal, and three modern CEC 2019 benchmark functions according to different metrics. Furthermore, two real engineering design problems are also used for performance comparison. Sensitivity analysis and statistical test analysis are also performed. Experimental results show that hybrid AO-TSA gives promising results and seems an effective method for global solution search and optimization problems.