Muhammet Fatih Aslan

Bio: Muhammet Fatih Aslan is an academic researcher from Karamanoğlu Mehmetbey University. The author has contributed to research in topics: Artificial intelligence & Computer science. The author has an hindex of 5, co-authored 26 publications receiving 151 citations.

A Comprehensive Survey of the Recent Studies with UAV for Precision Agriculture in Open Fields and Greenhouses

Abstract: The increasing world population makes it necessary to fight challenges such as climate change and to realize production efficiently and quickly. However, the minimum cost, maximum income, environmental pollution protection and the ability to save water and energy are all factors that should be taken into account in this process. The use of information and communication technologies (ICTs) in agriculture to meet all of these criteria serves the purpose of precision agriculture. As unmanned aerial vehicles (UAVs) can easily obtain real-time data, they have a great potential to address and optimize solutions to the problems faced by agriculture. Despite some limitations, such as the battery, load, weather conditions, etc., UAVs will be used frequently in agriculture in the future because of the valuable data that they obtain and their efficient applications. According to the known literature, UAVs have been carrying out tasks such as spraying, monitoring, yield estimation, weed detection, etc. In recent years, articles related to agricultural UAVs have been presented in journals with high impact factors. Most precision agriculture applications with UAVs occur in outdoor environments where GPS access is available, which provides more reliable control of the UAV in both manual and autonomous flights. On the other hand, there are almost no UAV-based applications in greenhouses where all-season crop production is available. This paper emphasizes this deficiency and provides a comprehensive review of the use of UAVs for agricultural tasks and highlights the importance of simultaneous localization and mapping (SLAM) for a UAV solution in the greenhouse.

Breast Cancer Diagnosis by Different Machine Learning Methods Using Blood Analysis Data

Abstract: Today, one of the most common types of cancer is breast cancer. It is crucial to prevent the propagation of malign cells to reduce the rate of cancer induced mortality. Cancer detection must be done as early as possible for this purpose. Machine Learning techniques are used to diagnose or predict the success of treatment in medicine. In this study, four different machine learning algorithms were used to early detection of breast cancer. The aim of this study is to process the results of routine blood analysis with different ML methods and to understand how effective this method is for detection. Methods used can be listed as Artificial Neural Network (ANN), standard Extreme Learning Machine (ELM), Support Vector Machine (SVM) and K-Nearest Neighbor (k-NN). Dataset used were taken from UCI library. In this dataset age, body mass index (BMI), glucose, insulin, homeostasis model assessment (HOMA), leptin, adiponectin, resistin and chemokine monocyte chemoattractant protein 1 (MCP1) attributes were used. Parameters that have the best accuracy values were found by using four different Machine Learning techniques. For this purpose, hyperparameter optimization method was used. In the end, the results were compared and discussed.

Human action recognition with bag of visual words using different machine learning methods and hyperparameter optimization

Abstract: Human activity recognition (HAR) has quite a wide range of applications. Due to its widespread use, new studies have been developed to improve the HAR performance. In this study, HAR is carried out using the commonly preferred KTH and Weizmann dataset, as well as a dataset which we created. Speeded up robust features (SURF) are used to extract features from these datasets. These features are reinforced with bag of visual words (BoVW). Different from the studies in the literature that use similar methods, SURF descriptors are extracted from binary images as well as grayscale images. Moreover, four different machine learning (ML) methods such as k-nearest neighbors, decision tree, support vector machine and naive Bayes are used for classification of BoVW features. Hyperparameter optimization is used to set the hyperparameters of these ML methods. As a result, ML methods are compared with each other through a comparison with the activity recognition performances of binary and grayscale image features. The results show that if the contrast of the environment decreases when a human enters the frame, the SURF of the binary image are more effective than the SURF of the gray image for HAR.

Determination of the bioactive compounds, antioxidant activity and chemical composition of Brazilian blackberry, red raspberry, strawberry, blueberry and sweet cherry fruits

Abstract: This study aimed to evaluate the chemical composition, identify the bioactive compounds and measure the antioxidant activity present in blackberry, red raspberry, strawberry, sweet cherry and blueberry fruits produced in the subtropical areas of Brazil and to verify that the chemical properties of these fruit are similar when compared to the temperate production zones. Compared with berries and cherries grown in temperate climates, the centesimal composition and physical chemical characteristics found in the Brazilian berries and cherries are in agreement with data from the literature. For the mineral composition, the analyzed fruits presented lower concentrations of P, K, Ca, Mg and Zn and higher levels of Fe. The values found for the bioactive compounds generally fit the ranges reported in the literature with minor differences. The greatest difference was found in relation to ascorbic acid, as all fruits analyzed showed levels well above those found in the literature.

Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions.

Abstract: Deep learning (DL), a branch of machine learning (ML) and artificial intelligence (AI) is nowadays considered as a core technology of today’s Fourth Industrial Revolution (4IR or Industry 4.0). Due to its learning capabilities from data, DL technology originated from artificial neural network (ANN), has become a hot topic in the context of computing, and is widely applied in various application areas like healthcare, visual recognition, text analytics, cybersecurity, and many more. However, building an appropriate DL model is a challenging task, due to the dynamic nature and variations in real-world problems and data. Moreover, the lack of core understanding turns DL methods into black-box machines that hamper development at the standard level. This article presents a structured and comprehensive view on DL techniques including a taxonomy considering various types of real-world tasks like supervised or unsupervised. In our taxonomy, we take into account deep networks for supervised or discriminative learning, unsupervised or generative learning as well as hybrid learning and relevant others. We also summarize real-world application areas where deep learning techniques can be used. Finally, we point out ten potential aspects for future generation DL modeling with research directions. Overall, this article aims to draw a big picture on DL modeling that can be used as a reference guide for both academia and industry professionals.

Artificial Intelligence in the Battle against Coronavirus (COVID-19): A Survey and Future Research Directions

Abstract: Artificial intelligence (AI) has been applied widely in our daily lives in a variety of ways with numerous successful stories. AI has also contributed to dealing with the coronavirus disease (COVID-19) pandemic, which has been happening around the globe. This paper presents a survey of AI methods being used in various applications in the fight against the COVID-19 outbreak and outlines the crucial roles of AI research in this unprecedented battle. We touch on a number of areas where AI plays as an essential component, from medical image processing, data analytics, text mining and natural language processing, the Internet of Things, to computational biology and medicine. A summary of COVID-19 related data sources that are available for research purposes is also presented. Research directions on exploring the potentials of AI and enhancing its capabilities and power in the battle are thoroughly discussed. We highlight 13 groups of problems related to the COVID-19 pandemic and point out promising AI methods and tools that can be used to solve those problems. It is envisaged that this study will provide AI researchers and the wider community an overview of the current status of AI applications and motivate researchers in harnessing AI potentials in the fight against COVID-19.

A Survey of Machine Learning for Indoor Positioning

Abstract: Widespread proliferation of wireless coverage has enabled culmination of number of advanced location-based services (LBS). Continuous tracking of accurate physical location is the foundation of these services, which is a challenging task especially indoors. Multitude of techniques and algorithms have been proposed for indoor positioning systems (IPS’s). However, accuracy, reliability, scalability and, adaptability to the environment still remain as challenges for widespread deployment. Especially, unpredictable radio propagation characteristics in vastly varying indoor environments plus access technology limitations contribute to these challenges. Machine learning (ML) approaches have been widely attempted recently to overcome these challenges with reasonable success. In this paper, we aim to provide a comprehensive survey of ML enabled localization techniques using most common wireless technologies. First, we provide a brief background on indoor localization techniques. Afterwards, we discuss various ML techniques (supervised and unsupervised) that could alleviate different challenges in indoor localization including Non-line-of-sight (NLOS) issue, device heterogeneity and environmental variations with reasonable complexity. The trade-offs among multitude of issues are discussed using numerous published results. We also discuss how the ML algorithms can be effectively used for fusing different technologies and algorithms to achieve a comprehensive IPS. In essence, this survey will serve as a reference material to acquire a detailed knowledge on recent development of machine learning for accurate indoor positioning.