To produce important investment decisions, investors require financial records and economic information. However, most companies manipulate investors and financial institutions by inflating their financial statements. Fraudulent Financial Activities exist in any monetary or financial transaction scenario, whether physical or electronic. A challenging problem that arises in this domain is the issue that affects and troubles individuals and institutions. This problem has attracted more attention in the field in part owing to the prevalence of financial fraud and the paucity of previous research. For this purpose, in this study, the main approach to solve this problem, an anomaly detection-based approach based on a combination of feature selection based on squirrel optimization pattern and classification methods have been used. The aim is to develop this method to provide a model for detecting anomalies in financial statements using a combination of selected features with the nearest neighbor classifications, neural networks, support vector machine, and Bayesian. Anomaly samples are then analyzed and compared to recommended techniques using assessment criteria. Squirrel optimization's meta-exploratory capability, along with the approach's ability to identify abnormalities in financial data, has been shown to be effective in implementing the suggested strategy. They discovered fake financial statements because of their expertise.

The pricing of housing properties is determined by a variety of factors. However, post-pandemic markets have experienced volatility in the Chicago suburb area, which have affected house prices greatly. In this study, analysis was done on the Naperville/Bolingbrook real estate market to predict property prices based on these housing attributes through machine learning models, and to evaluate the effectiveness of such models in a volatile market space. Gathering data from Redfin, a real estate website, sales data from 2018 up until the summer season of 2022 were collected for research. By analyzing these sales in this range of time, we can also look at the state of the housing market and identify trends in price. For modeling the data, the models used were linear regression, support vector regression, decision tree regression, random forest regression, and XGBoost regression. To analyze results, comparison was made on the MAE, RMSE, and R-squared values for each model. It was found that the XGBoost model performs the best in predicting house prices despite the additional volatility sponsored by post-pandemic conditions. After modeling, Shapley Values (SHAP) were used to evaluate the weights of the variables in constructing models.

In this paper, we study the classification problem by estimating the conditional probability function of the given data. Different from the traditional expected risk estimation theory on empirical data, we calculate the probability via Fredholm equation, this leads to estimate the distribution of the data. Based on the Fredholm equation, a new expected risk estimation theory by estimating the cumulative distribution function is presented. The main characteristics of the new expected risk estimation is to measure the risk on the distribution of the input space. The corresponding empirical risk estimation is also presented, and an $\varepsilon$-insensitive $L_{1}$ cumulative support vector machines ($\varepsilon$-$L_{1}VSVM$) is proposed by introducing an insensitive loss. It is worth mentioning that the classification models and the classification evaluation indicators based on the new mechanism are different from the traditional one. Experimental results show the effectiveness of the proposed $\varepsilon$-$L_{1}VSVM$ and the corresponding cumulative distribution function indicator on validity and interpretability of small data classification.

Our project probes the relationship between temperatures and the blossom date of cherry trees. Through modeling, future flowering will become predictive, helping the public plan travels and avoid pollen season. To predict the date when the cherry trees will blossom exactly could be viewed as a multiclass classification problem, so we applied the multi-class Support Vector Classifier (SVC) and Recurrent Neural Network (RNN), particularly Long Short-term Memory (LSTM), to formulate the problem. In the end, we evaluate and compare the performance of these approaches to find out which one might be more applicable in reality.

Support vector machines are supervised learning models that analyse data to find patterns useful in classification and regression. They are versatile: they can identify non-linear relationships, work with discrete and continuous data, and are used for two-class classification, multi-class classification as well as regression. They are remarkable for unifying geometric theory, elegant mathematics, and theoretical guarantees with practical solid use cases. They provide several specific benefits. With the use of Kernel functions, they are highly effective in higher dimensional spaces.

To address practical challenges in establishing and maintaining robust wireless connectivity such as multi-path effects, low latency, size reduction, and high data rate, the digital beamformer is performed by the hybrid antenna array at the frequency of operation of 10 GHz. The proposed digital beamformer, as a spatial filter, is capable of performing Direction of Arrival (DOA) estimation and beamforming. The most well-established machine learning technique of support vector machine (SVM) for the DoA estimation is limited to problems with linearly-separable datasets. To overcome the aforementioned constraint, in the proposed beamformer, the QS-SVM classifier with a small regularizer has been used for the DoA estimation in addition to the two beamforming techniques of LCMV and MVDR. The QS-SVM-based beamformer has been deployed in an FPGA board, as demonstrated in detail in this work. The implementation results have verified the strong performance of the QS-SVM-based beamformer in suppressing undesired signals, deep nulls with powers less than -10 dB in undesired signals, and transferring desired signals. Furthermore, we have demonstrated that the performance of the QS-SVM-based beamformer consists of other advantages of average latency time in the order of milliseconds, performance efficiency of more than 90%, and throughput of about 100%. Index Terms Digital beamforming, Support vector machine, Minimum variance distortionless response, Linearly constrained minimum variance, Direction of arrival estimation, and FPGA, Spatial filter, Massive wireless communications.

GBSVM (Granular-ball Support Vector Machine) is an important attempt to use the coarse granularity of a granular-ball as the input to construct a classifier instead of a data point. It is the first classifier whose input contains no points, i.e., $x_i$, in the history of machine learning. However, on the one hand, its dual model is not derived, and the algorithm has not been implemented and can not be applied. On the other hand, there are some errors in its existing model. To address these problems, this paper has fixed the errors of the original model of GBSVM, and derived its dual model. Furthermore, an algorithm is designed using particle swarm optimization algorithm to solve the dual model. The experimental results on the UCI benchmark datasets demonstrate that GBSVM has good robustness and efficiency.

Epilepsy is a neurological brain disorder which life threatening and gives rise to recurrent seizures that are unprovoked. It occurs due to the abnormal chemical changes in our brain. Over the course of many years, studies have been conducted to support automatic diagnosis of epileptic seizures for the ease of clinicians. For that, several studies entail the use of machine learning methods for the early prediction of epileptic seizures. Mainly, feature extraction methods have been used to extract the right features from the EEG data generated by the EEG machine and then various machine learning classifiers are used for the classification process. This study provides a systematic literature review of feature selection process as well as the classification performance. This study was limited to the finding of most used feature extraction methods and the classifiers used for accurate classification of normal to epileptic seizures. The existing literature was examined from well-known repositories such as MPDI, IEEEXplore, Wiley, Elsevier, ACM, Springerlink and others. Furthermore, a taxonomy was created that recapitulates the state-of-the-art used solutions for this problem. We also studied the nature of different benchmark and unbiased datasets and gave a rigorous analysis of the working of classifiers. Finally, we concluded the research by presenting the gaps, challenges and opportunities which can further help researchers in prediction of epileptic seizure

Astrobot swarms are used to capture astronomical signals to generate the map of the observable universe for the purpose of dark energy studies. The convergence of each swarm in the course of its coordination has to surpass a particular threshold to yield a satisfactory map. The current coordination methods do not always reach desired convergence rates. Moreover, these methods are so complicated that one cannot formally verify their results without resource-demanding simulations. Thus, we use support vector machines to train a model which can predict the convergence of a swarm based on the data of previous coordination of that swarm. Given a fixed parity, i.e., the rotation direction of the outer arm of an astrobot, corresponding to a swarm, our algorithm reaches a better predictive performance compared to the state of the art. Additionally, we revise our algorithm to solve a more generalized convergence prediction problem according to which the parities of astrobots may differ. We present the prediction results of a generalized scenario, associated with a 487-astrobot swarm, which are interestingly efficient and collision-free given the excessive complexity of this scenario compared to the constrained one.

Renewable Energies (RE) have gained more attention in recent years since they offer clean and sustainable energy. One of the major sustainable development goals (SDG-7) set by the United Nations (UN) is to achieve affordable and clean energy for everyone. Among the world's all renewable resources, solar energy is considered as the most abundant and can certainly fulfill the target of SDGs. Solar energy is converted into electrical energy through Photovoltaic (PV) panels with no greenhouse gas emissions. However, power generated by PV panels is highly dependent on solar radiation received at a particular location over a given time period. Therefore, it is challenging to forecast the amount of PV output power. Predicting the output power of PV systems is essential since several public or private institutes generate such green energy, and need to maintain the balance between demand and supply. This research aims to forecast PV system output power based on weather and derived features using different machine learning models. The objective is to obtain the best-fitting model to precisely predict output power by inspecting the data. Moreover, different performance metrics are used to compare and evaluate the accuracy under different machine learning models such as random forest, XGBoost, KNN, etc.