Optimized speaker change detection approach for speaker segmentation towards speaker diarization based on deep learning

Speaker diarization is the partitioning of an audio source stream into homogeneous segments according to the speaker's identity. It can improve the readability of an automatic speech transcription by segmenting the audio stream into speaker turns and identifying the speaker's true identity when used in combination with speaker recognition systems. Generally, the automatic speaker diarization is done based on two phases, like the transformation of audio segments into feature representation and the clustering. In this paper, clustering along with a hybrid optimization technique is carried out for performing the speaker diarization. For that, the extracted features from the audio signal is processed under speech activity prediction in order to identify the speak segments. The diarization process is done by Deep Embedded Clustering (DEC) in which the constants are trained by the developed Fractional Anticorona Whale Optimization Algorithm (FrACWOA). The FrACWOA is a hybrid optimization technique, which is designed by adapting the concept of fractional theory, precaution behaviour of COVID-19 and hunting performance of whales. DEC performs the diarization, which concurrently learns the representation of features as well as cluster assignments with neural networks. Using a mapping from the information space to a lower-dimensional feature space, DEC repeatedly discovers the most effective solution for a clustering objective. On the basis of testing accuracy, diarization error, false discovery rate (FDR), false negative rate (FNR), and false positive rate (FPR) of 0.902, 0.627, 0.276, 0.117, and 0.118, respectively, the developed FrACWOA+DEC algorithm performed much better with six speakers using the EenaduPrathidwani dataset. Comparing the accuracy of the proposed method to existing approaches such as Active learning, DE+K-means, LSTM, MCGAN, ANN-ABC-LA, and ACWOA+DFC, the accuracy of the proposed method is 12.97%, 10.31%, 9.75%, 7.53%, 4.32%, and 2.106% higher when using 6 speakers. © 2022 Elsevier B.V.

UTILIZING BERT AND CNN-LSTM IN STOCK PRICE PREDICTION USING DATA SENTIMENT ANALYSIS AND TECHNICAL ANALYSIS OF STOCK AND COMMODITY

The COVID-19 pandemic undoubtedly has affected people's lifestyles and stock investment activities. The government's policies to deal with the pandemic have an impact on increasing the number of investors in the stock market. Apart from profits, there are also risks associated with investing in stocks. To reduce the risk required analysis for stock price predictions. The data often used are stock data, commodity prices, and social media. The application of deep learning and natural language processing can help investors to process data. This paper proposes Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) for technical analysis predicting stock prices using stock and commodity price data and urges BERT for sentiment analysis using social media data. The CNN-LSTM method has the best performance compared to the other four methods. The results showed that the performance of this method was the best, Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) were the smallest, and R Square (R2) was the largest. The BERT method has the best classification performance using 5-epochs, Weight Macro Avg, Weighted Avg, Accuracy, and the highest F1-Score. CNN-LSTM and BERT are more appropriate to predict stock prices and give investors suggestions to make stock investment decisions based on technical analysis and sentiment analysis. © 2023 ICIC International. All rights reserved.

Pressure Prediction System in Lung Circuit Using Deep Learning

A massive number of patients infected with SARS-CoV2 and Delta variant of COVID-19 have generated acute respiratory distress syndrome (ARDS) which needs intensive care, which includes mechanical ventilation. But due to the huge no of patients, the workload and stress on healthcare infrastructure and related personnel have grown exponentially. This has resulted in huge demand for innovation in the field of automated health care which can help reduce the stress on the current healthcare infrastructure. This work gives a solution for the issue of pressure prediction in mechanical ventilation. The algorithm suggested by the researchers tries to predict the pressure in the respiratory circuit for various lung conditions. Prediction of pressure in the lungs is a type of sequence prediction problem. Long short-term memory (LSTM) is the most efficient solution to the sequence prediction problem. Due to its ability to selectively remember patterns over the long term, LSTM has an edge over normal RNN. RNN is good for short-term patterns but for sequence prediction problems, LSTM is preferred. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A Hybrid Deep Fused Learning Approach to Segregate Infectious Diseases

Humankind is facing another deadliest pandemic of all times in history, caused by COVID-19. Apart from this challenging pandemic, World Health Organization (WHO) considers tuberculosis (TB) as a preeminent infectious disease due to its high infection rate. Generally, both TB and COVID-19 severely affect the lungs, thus hardening the job of medical practitioners who can often misidentify these diseases in the current situation. Therefore, the time of need calls for an immediate and meticulous automatic diagnostic tool that can accurately discriminate both diseases. As one of the preliminary smart health systems that examine three clinical states (COVID-19, TB, and normal cases), this study proposes an amalgam of image filtering, data-augmentation technique, transfer learning-based approach, and advanced deep-learning classifiers to effectively segregate these diseases. It first employed a generative adversarial network (GAN) and Crimmins speckle removal filter on X-ray images to overcome the issue of limited data and noise. Each pre-processed image is then converted into red, green, and blue (RGB) and Commission Internationale de l'Elcairage (CIE) color spaces from which deep fused features are formed by extracting relevant features using DenseNet121 and ResNet50. Each feature extractor extracts 1000 most useful features which are then fused and finally fed to two variants of recurrent neural network (RNN) classifiers for precise discrimination of three-clinical states. Comparative analysis showed that the proposed Bi-directional long-short-term-memory (Bi-LSTM) model dominated the long-short-term-memory (LSTM) network by attaining an overall accuracy of 98.22% for the three-class classification task, whereas LSTM hardly achieved 94.22% accuracy on the test dataset. © 2023 Tech Science Press. All rights reserved.

COVID-19 clinical medical relationship extraction based on MPNet

With the rapid development of biomedical research and information technology, the number of clinical medical literature has increased exponentially. At present, COVID-19 clinical text research has some problems, such as lack of corpus and poor annotation quality. In clinical medical literature, there are many medical related semantic relationships between entities. After the task of entity recognition, how to further extract the relationships between entities efficiently and accurately becomes very critical. In this study, a COVID-19 clinical trial data relationship extraction model based on deep learning method is proposed. The model adopts MPNet model, bidirectional-GRU (BiGRU) network, MAtt mechanism and Conditional Random Field inference layer integration architecture and improves the problem that static word vector cannot represent ambiguity through pre-trained language model. BiGRU network is used to replace the current Bi directional long short term memory structure and simplify the network structure of Long Short Term Memory to improve the training efficiency of the model. Through comparative experiments, the proposed method performs well in the COVID-19 clinical text entity relation extraction task. © 2023 The Authors. IET Cyber-Physical Systems: Theory & Applications published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

CSI-Based Joint Location and Activity Monitoring for COVID-19 Quarantine Environments

The recent SARS-COV-2 virus, also known as COVID-19, badly affected the world's healthcare system due to limited medical resources for a large number of infected human beings. Quarantine helps in breaking the spread of the virus for such communicable diseases. This work proposes a nonwearable/contactless system for human location and activity recognition using ubiquitous wireless signals. The proposed method utilizes the channel state information (CSI) of the wireless signals recorded through a low-cost device for estimating the location and activity of the person under quarantine. We propose to utilize a Siamese architecture with combined one-dimensional convolutional neural networks (1-D-CNNs) and bi-directional long short-term memory (Bi-LSTM) networks. The proposed method provides high accuracy for the joint task and is validated on two real-world testbeds, first, using the designed low-cost CSI recording hardware, and second, on a public dataset for joint activity and location estimation. The human activity recognition (HAR) results outperform state-of-the-art machine and deep learning methods, and localization results are comparable with the existing methods. © 2001-2012 IEEE.

Optimized face detector-based intelligent face mask detection model in IoT using deep learning approach

The growth of the "Internet of Medical Things (IoMT)” allows for the collection and processing of data in healthcare systems. At the same time, it is challenging to study the requirements of public health prevention. Here, mask-wearing is considered an efficient preventive measure for avoiding virus transfer. Hence, it is necessary to implement an automated mask identification model to prevent public epidemics. The main scope of the proposed method is to design a face mask detection model with IoT using a "Single Shot Multi-box Detector (SSD)” and a hybrid deep learning method. The novelty of the proposed model is that the enhancement made in the face detection and face classification with the developed ASMFO by optimizing the parameters like the threshold in SSD, steps per execution in ResNet, and learning rate in MobileNet, which makes it more efficient and to perform better the conventional models. Here, the parameter optimization is carried out using a hybrid optimization algorithm named Adaptive Sailfish Moth Flame Optimization (ASMFO). Then, the detected face images are given to the hybrid approach named Hybrid ResMobileNet (HResMobileNet)-based classification, where the parameters are tuned using the same ASMFO algorithm for achieving accurate mask detection results. However, the suggested mask identification model with IoT based on three standard datasets is compared with the conventional meta-heuristic algorithms and existing classifiers with various measures. Thus, the experimental analysis is conducted to analyze the effectiveness of the proposed framework over different meta-heuristic algorithms and existing classifiers. The implemented ASMFO-HResMobileNet provides 18.57%, 15.67%, 17.56%, 16.24%, and 19.2% elevated accuracy than SVM, CNN, VGG16-LSTM, ResNet 50, MobileNetv2, and ResNet 50-MobileNetv2. © 2022 Elsevier B.V.

Automated Stacking Ensemble Model for Forecasting COVID-19 Cases

COVID-19 is an infectious disease caused by the SARS-Cov2 virus. Multiple variants of COVID-19 such as beta and omicron have spread all over the world that has caused more than six million fatalities till now and it is still not halted. Multiple vaccinations have been already created but are not full-proof solutions for all the existing mutations and also its protection against any upcoming mutations is not known. Therefore, forecasting of COVID-19 cases becomes the most important weapon to avoid the spread of COVID-19 due to any forthcoming mutation. This research presents an automated stacking ensemble method to forecast COVID-19 cases. The proposed method employs multilayer perceptron, long short-term memory, and linear regression together with a genetic algorithm. The purpose of this research is create an automated model to foresee COVID-19 cases so that any impending COVID-19 wave can be discovered at an early stage to prevent it from spreading. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Soft computing techniques for forecasting of COVID-19 in Pakistan

Novel Pandemic COVID-19 led globally to severe health barriers and financial issues in different parts of the world. The forecast on COVID-19 infections is significant. Demeanor vital data will help in executing policies to reduce the number of cases efficiently. Filtering techniques are appropriate for dynamic model structures as it provide reasonable estimates over the recursive Bayesian updates. Kalman Filters, used for controlling epidemics, are valuable in knowing contagious infections. Artificial Neural Networks (ANN) have generally been used for classification and forecasting problems. ANN models show an essential role in several successful applications of neural networks and are commonly used in economic and business studies. Long short-term memory (LSTM) model is one of the most popular technique used in time series analysis. This paper aims to forecast COVID-19 on the basis of ANN, KF, LSTM and SVM methods. We applied ANN, KF, LSTM and SVM for the COVID-19 data in Pakistan to find the number of deaths, confirm cases, and cases of recovery. The three methods were used for prediction, and the results showed the performance of LSTM to be better than that of ANN and KF method. ANN, KF, LSTM and SVM endorsed the COVID-19 data in closely all three scenarios. LSTM, ANN and KF followed the fluctuations of the original data and made close COVID-19 predictions. The results of the three methods helped significantly in the decision-making direction for short term strategies and in the control of the COVID-19 outbreak. © 2022 Faculty of Engineering, Alexandria University

Quantile time-frequency connectedness between cryptocurrency volatility and renewable energy volatility during the COVID-19 pandemic and Ukraine-Russia conflicts

Our article employs a quantile vector autoregression (QVAR) to identify the connectedness of seven variables from April 1, 2019, to June 13, 2022, in order to examine the relationships between crypto volatility and energy volatility. Our findings reveal that the dynamic connectedness is approximately 25% in the short term and approximately 9% in the long term. The 50% quantile equates to the overall average connectedness of the entire period, according to dynamic net total directional connectedness over a quantile, which also indicates that connectedness is very intense for both highly positive changes (above the 80% quantile) and crypto and energy volatility (below the 20% quantile). With the exception of the early 2022 period when the Crypto Volatility Index transmits a net of shocks because of the Ukraine-Russia Conflict, dynamic net total directional connectedness implies that in the short term, the Crypto Volatility Index acts as a net shock receiver across time. While this indicator is a net shock receiver for long-term dynamics, wind energy is a net shock transmitter during the short term. Green bonds are a short-term net shock receiver. This role is valid in the long term. Clean energy and solar energy are the long-term net transmitters of shocks;nevertheless, the series is always and only momentarily a net receiver of shocks because of the short-term dynamics. Natural gas and crude oil play roles in both two quantiles. Dynamic net pairwise directional connectedness over a quantile suggests that uncertain events like the COVID-19 epidemic or Ukraine-Russia Conflict influence cryptocurrency volatility and renewable energy volatility. © 2022 Elsevier Ltd

Alpha Fusion Adversarial Attack Analysis Using Deep Learning

The deep learning model encompasses a powerful learning ability that integrates the feature extraction, and classification method to improve accuracy. Convolutional Neural Networks (CNN) perform well in machine learning and image processing tasks like segmentation, classification, detection, identification, etc. The CNN models are still sensitive to noise and attack. The smallest change in training images as in an adversarial attack can greatly decrease the accuracy of the CNN model. This paper presents an alpha fusion attack analysis and generates defense against adversarial attacks. The proposed work is divided into three phases: firstly, an MLSTM-based CNN classification model is developed for classifying COVID-CT images. Secondly, an alpha fusion attack is generated to fool the classification model. The alpha fusion attack is tested in the last phase on a modified LSTM-based CNN (CNN-MLSTM) model and other pre-trained models. The results of CNN models show that the accuracy of these models dropped greatly after the alpha-fusion attack. The highest F1 score before the attack was achieved is 97.45 And after the attack lowest F1 score recorded is 22%. Results elucidate the performance in terms of accuracy, precision, F1 score and Recall. © 2023 CRL Publishing. All rights reserved.

Hybrid optimization enabled deep learning and spark architecture using big data analytics for stock market forecasting

The precise forecasting of stock prices is not possible because of the complexity and uncertainty of stock. The effectual model is needed for the triumphant assessment of upcoming stock prices for several companies. Here, an optimized deep model is utilized to effectively predict the stock market using the spark framework. Here, the data partitioning is done using deep embedded clustering, wherein the tuning of parameters is done using the proposed Jaya Anti Coronavirus Optimization (JACO) algorithm in the master node. The proposed JACO is developed by combining Jaya Algorithm and Anti-Coronavirus Optimization algorithm. Then, important technical indicators are mined from divided data in slave nodes. Here, the technical indicators are considered features for enhanced processing. Then, data augmentation is done to make data suitable for processing in the master node. At last, the prediction was done in the master node using deep long short-term memory (Deep LSTM), and training is performed with the proposed JACO. The proposed JACO-based Deep LSTM attains the smallest mean absolute error of 0.113, mean squared error of 0.095, and root mean squared error of 0.309.

The Political Economy of Housing Investment in the Short-Term Rental Market: Insights from Urban Portugal

Short-term rentals (STRs) emerged as holiday accommodations, disrupting the hospitality industry in the decade before COVID-19. Mainstream explanations for their growth revolved around digital tourism platforms like Airbnb as market disruptors and the sharing economy rationale. At the same time, critical scholars explored the capitalisation of greater rent gaps in urban central locations. However, these explanations are insufficient to explain the growth of STRs. We supplement them by building bridges between the urban political economy and the geographies of financialisation through the cases of Lisbon and Porto before the pandemic. The paper focuses on tourism-induced housing investment, taking a closer look at the profile of investors in association with STR property managers in the context of the late-entrepreneurial urban regime. We conclude that tourism development has allowed opportunities for housing financialisation through STR professionalisation, enhancing the allocation of interest-bearing capital in tourism-oriented real estate. © 2022 The Authors. Antipode © 2022 Antipode Foundation Ltd.

Optimal gene prioritization and disease prediction using knowledge based ontology structure

Prioritizing candidate genes is essential for genome-based diagnostics of various hereditary disorders. Furthermore, it is a difficult task with particular and noisy information about genes, illnesses, and relationships. Although several computer methods for disease gene prioritization have been developed, their efficiency is limited by manually created traits, network architecture, or pre-established data fusion criteria. Hence, this research proposes a unique gene prioritization and disease prediction model. Initially, the gathered information is pre-processed by a data cleaning model. In the proposed gene prioritization phase, the pre-processed data are tokenized. Then a new knowledge-based ontology structure is constructed with the improved skewness-based semantic similarity function. The ensemble classifier is constructed along Recurrent Neural Network (RNN), optimized fuzzy logic, and also Deep Belief Network (DBN) to forecast the gene disorders in the prediction phase. The retrieved features from the feature extraction phase are used to train RNN;while the extracted knowledge bases are used to train the DBN, then the results are fed into the optimized fuzzy logic. The fuzzy logic is the primary indication;its fuzzification function is fine-tuned employing a methodology to improve illness prediction accuracy. A recommended new hybrid system, named as Cauchy's Mutated Corona Virus Optimization Algorithm (CMCOA), is the upgraded version of the CVOA, a typical coronavirus optimization technique. Finally, to evaluate the efficiency of the projected model, a comparison of the suggested and existent models is performed with respect to various measures. In particular, the proposed model has recorded the highest accuracy as 93 % at 60 % of training, which is 42.5 %, 36.1 %, 33.3 %, 41.1 %, 48.5 %, 48.5 %, 9 %, 8 %, 8 %, 8 %, 8 %, and 14.5 % improved over existing models like GCN, GCN [6], SVM, CNN, Bi-LSTM, LSTM, GRU, fuzzy, EC + GOA, EC + SSO, EC + CMBO, EC + SMA and EC + CCVOA, respectively. The precision of the suggested work with improved features &CMCOA is 15.5 %, and 14.42 % superior to the proposed work without existing features & CMCOA and proposed work with existing features & CMCOA approaches. © 2022 Elsevier Ltd

Forecasting domestic waste generation during successive COVID-19 lockdowns by Bidirectional LSTM super learner neural network

Accurate prediction of domestic waste generation is a challenging task for municipalities to implement sustainable waste management strategies. In the present study, domestic waste generation in the Kingdom of Bahrain, representing a Small Island Developing State (SIDS) case study, has been investigated during successive COVID-19 lockdowns due to the pandemic in 2020. Temporal trends of daily domestic waste generation between 2019 and 2020 and their statistical analyses exhibited remarkable variations highlighting the impact of consecutive COVID-19 lockdowns on domestic waste generation. Machine learning has great potential for predicting solid waste generation rates, but only a few studies utilized deep learning approaches. The state-of-the-art Bidirectional Long Short-Term Memory (BiLSTM) network model as a deep learning method is applied to forecast daily domestic waste data in 2020. Bayesian optimization algorithm (BOA) was hybridized with BiLSTM to generate a super learner approach. The performance of the BOA-BiLSTM super learner model was further compared with the statistical ARIMA model. Performance indicators of the developed models using ARIMA and BiLSTM showed that the latter yielded superior performance for short-term forecasts of domestic waste generation. The MAE, RMSE, MAPE, and R2 were 47.38, 60.73, 256.43, and 0.46, respectively, for the ARIMA model, compared to 3.67, 12.57, 0.24, and 0.96, respectively, for the BiLSTM model. Additionally, the relative errors for the BiLSTM model were lower than those of the ARIMA model. This study highlights that the BiLSTM can be a reliable forecasting tool for solid waste management policymakers during public health emergencies. © 2022 Elsevier B.V.

Individual Surveillance Around Parked Aircraft at Nighttime: Thermal Infrared Vision-Based Human Action Recognition

The COVID-19 crisis has led to an unusually large number of commercial aircraft being currently parked or stored. For airlines, airports, and civil aviation authorities around the world, monitoring, and protecting these parked aircraft to prevent them from causing human-made damage are becoming urgent problems that are receiving increasing attention. In this study, we use thermal infrared monitoring videos to establish a framework for individual surveillance around parked aircraft by proposing a human action recognition (HAR) algorithm. As the focus of this article, the proposed HAR algorithm seamlessly integrates a preprocessing module in which a novel data structure is constructed to introduce spatiotemporal information of the action;a convolutional neural network-based module for spatial feature extraction;a triple-layer convolutional long short-term memory network for temporal feature extraction;and two fully connected layers for classification. Moreover, because no infrared dataset is available for the HAR task on airport grounds at nighttime, we present a dataset called IIAR-30, which consists of eight action categories that frequently occur on airport grounds and 2000 video clips. The experimental results on the IIAR-30 dataset demonstrated that the recognition accuracy of the proposed method was higher than 96%. We also further evaluated the effectiveness of the proposed method by comparing it with five baselines and four other methods. © 2022 IEEE.

Synchronisation among short-term rental markets, co-movements and cycles in 39 European cities

This paper presents new evidence of the short-term rental market's prices and transactions from a daily time -series perspective in 39 European cities from 2015 to 2020. It uses Airbnb micro datasets to build time-series cycles by extracting the original observations containing total bookings (rent transactions), rental units sup-ply, and asking rent, with a daily periodicity. The cycles show the periods in which short-rental activity was more relevant for each city, and the level of rents across Europe. The paper provides empirical evidence of a long-term relationship among the city variables (tested via mean and variance). Causality supporting co-movements across cities was found by estimating a short-term naive market equilibrium model using the vector error correction model approach, supporting the hypothesis that the short-term rental market performs according to housing -market principles. Short-run elasticities among rents and contracts across the 39 cities show causal evidence of co-movements among rents and the supply and demand of properties. The market adjustment on the supply side estimates new units responding to changes in prices within 15 lags (days) and longer (350 lags) from the demand side, equivalent to eight to nine months. Evidence of the pandemic's limited effect on housing supply and prices' positive effect is also provided. A robust negative weekend impact on prices was found, suggesting stronger market relevance on weekdays.

Systematic review of short-term study abroad outcomes and an agenda for future research

Purpose: The purpose of this paper is to serve as a comprehensive review of short-term study abroad (STSA) outcomes to help guide future STSA and study abroad (SA) scholars and practitioners in the further development of the field. Design/methodology/approach: This paper is the first comprehensive and systematic review of all outcomes of STSA programs within the SA body of research based on 156 papers. Findings: The study provides the first comprehensive classification of all previously studied STSA outcomes (85) into six categories: cross-cultural outcomes, STSA pedagogy outcomes, personal and professional outcomes;language outcomes;teacher and faculty outcomes;and other outcomes. Distinct sub-categories are identified that provide insights on the current landscape of STSA and related research. Research limitations/implications: This study makes a significant contribution to the theory and practice of SA, and among the key contributions are a systematic understanding of the scale and scope of STSA outcomes;insights on the most efficient design of future STSA programs;and an expanded understanding of the role and importance of STSA programs in international education. Furthermore, a comprehensive STSA outcomes map develops an extensive research agenda. Social implications: While the COVID-19 pandemic currently limits the opportunities for STSA, given its previous popularity, the authors envisage a strong return in the coming years of this form of affordable and valuable global learning. STSA programs have become an important component of higher education and which require considerable resources from participants and educational institutions alike. Therefore, further research is needed to understand the impacts of STSA programs and to further improve program design. Such research will serve to better inform both academic understanding of the phenomenon and educational practice. Originality/value: The study provides the first comprehensive classification of all studied STSA outcomes. © 2022, Emerald Publishing Limited.

Smartwatch Sensors with Deep Learning to Predict the Purchase Intentions of Online Shoppers.

In the past decade, the scale of e-commerce has continued to grow. With the outbreak of the COVID-19 epidemic, brick-and-mortar businesses have been actively developing online channels where precision marketing has become the focus. This study proposed using the electrocardiography (ECG) recorded by wearable devices (e.g., smartwatches) to judge purchase intentions through deep learning. The method of this study included a long short-term memory (LSTM) model supplemented by collective decisions. The experiment was divided into two stages. The first stage aimed to find the regularity of the ECG and verify the research by repeated measurement of a small number of subjects. A total of 201 ECGs were collected for deep learning, and the results showed that the accuracy rate of predicting purchase intention was 75.5%. Then, incremental learning was adopted to carry out the second stage of the experiment. In addition to adding subjects, it also filtered five different frequency ranges. This study employed the data augmentation method and used 480 ECGs for training, and the final accuracy rate reached 82.1%. This study could encourage online marketers to cooperate with health management companies with cross-domain big data analysis to further improve the accuracy of precision marketing.

Forecast combination approach with meta-fuzzy functions for forecasting the number of immigrants within the maritime line security project in Turkey.

In this study, forecasting the number of immigrants on the Turkey's maritime line for use in a national security project carried out by Turkish Government within the scope of fight against uncontrolled immigration is discussed for the first time. Handling with the immigration problem is one of the biggest concerns of Turkey as unsupervised immigration can adversely affect the demographic and economic structure of the country. Precautions are needed as the short-, medium- and long-term impacts of undetected immigrants on the country's ecosystem are unpredictable, but due to the uncertainties inherent in immigration, the cost of using government resources such as patrol vehicles to capture undocumented immigrants can be extremely high. In order to both minimize the expenditure problem and keep immigration under control by providing a proper scan, forecasting the number of immigrants on the maritime line route is seen as an important problem and studied by probabilistic and non-probabilistic models. Since the data for 2020 and 2021 could not be attained yet due to COVID-19, in order to obtain forecasts and compare actual observations for 2019, which is the primarily focus of the research in this study, the dataset of interest on the number of daily immigrants between years 2016 and 2019 is obtained from Turkish Coast Guard Command within Ministry of Interior of Republic of Turkey. To obtain the most accurate forecasts, seven distinguished forecasting methods, from simple to complex, are implemented. Then, the forecast combination approach with meta-fuzzy functions which combines all methods is proposed. Consequently, the forecasting results are acquired and evaluated by using R. The evaluation of the results is made by using widely considered measurement accuracy metric root mean square error. According to the final assessments, the proposed approach gives more accurate forecasting results for the expected number of immigrants on the Turkey's maritime line and these results become an input to the national security project.