Furkan Gözükara

Oyster mushroom cultivation requires accurate object detection for automated monitoring and precision agriculture applications. This study evaluates a structure-aware three-channel input representation for YOLOv8s in which raw RGB channels are replaced by grayscale intensity, Sobel gradient magnitude, and a complementary structural channel derived from Gaussian Blur, Laplacian of Gaussian (LoG), Canny edge detection, or Gabor filtering. The dataset contains 555 RGB images and 8282 maturity-labeled mushroom instances. Controlled experiments include an RGB baseline with HSV augmentation disabled, component-wise ablations (GGG and GGradG), repeated-seed training, a chronological holdout, and a cross-architecture RT-DETR evaluation. Under the fixed random split, differences among RGB, grayscale, and structure-aware inputs were modest, and the ablations indicate that grayscale conversion accounts for most of the measured effect. Performance decreased substantially under the chronological split, and RT-DETR did not reproduce the same ordering observed with YOLOv8s. These results show that input representation can influence detector behavior, but they do not support a general claim that handcrafted structural channels consistently improve robustness across evaluation protocols or architectures.

Accurate localization of bounding boxes is a prerequisite for enabling vision-driven precision agriculture pipelines, as downstream tasks such as morphological feature extraction, growth monitoring, and digital-twin synchronization depend directly on the geometric quality of the detected boxes. Distance-IoU (DIoU) and Complete-IoU (CIoU) improve upon simple overlap-based objectives by incorporating a normalized center-distance term into the regression loss, along with the overlap and, for CIoU, an aspect-ratio penalty, but that term remains embedded in a single composite formulation with an implicit, non-adjustable weight. We propose a Directional Alignment Penalty (DAP), an auxiliary localization regularizer that introduces an independently weighted normalized center-displacement term into the bounding-box regression objective without modifying the detector architecture. The proposed DAP-YOLOv8 increased mAP@0.5:0.95 from 51.69% to 53.76% and mAP@0.5 from 80.03% to 80.36% while preserving precision and recall; repeated-seed experiments further showed that the improvement in fine-grained localization was consistent across random initializations on a purpose-built oyster-mushroom (Pleurotus ostreatus) dataset collected from a real-world smart greenhouse. Results show that explicitly modeling the center-distance factor as an independent and tunable component can improve fine-grained localization without sacrificing the computational efficiency of the base detector, thereby providing a lightweight plug-in extension for agricultural detection and digital-twin applications.

The power flow problem is one of the most challenging tasks in power systems, affecting both generation cost and energy quality. Optimal power flow (OPF) further complicates this task by requiring the optimal adjustment of system variables and parameters. This paper adapts the Modified Effective Butterfly Optimizer (MEBO) to solve multi-objective optimal power flow (MOOPF) problems with the contribution of optimized weighting using multiple Pareto archives. MEBO is an advanced optimization algorithm that utilizes population reduction and parameter learning to guide subsequent searches for unconstrained problems. The proposed technique has been tested on IEEE 30 and 57 bus test systems, and the results have been compared with existing methods reported in the literature. In the paper, four single-objective functions, namely generator cost, active power loss, fuel emission, and voltage deviation, are used to construct four multi-objective (MO) problems: cost–loss, cost–voltage, cost-emission, and emission–loss. For the cost-emission case, the proposed MEBO achieved compromised solutions of 791.1951 $/h fuel cost with 0.10873 ton/h emission and 801.8172 $/h fuel cost with 0.10044 ton/h emission under different Pareto-based optimization metrics. In the emission–loss case, the algorithm obtained 0.20539 ton/h emission with 3.1403 MW/h power loss, demonstrating the effectiveness of the proposed approach in balancing conflicting objectives. The Pareto curves of MEBO in achieving MO problems are presented, along with the suggested compromised solutions acquired from the literature. In the literature, this is the first application of MEBO for solving MOOPF problems. The results demonstrate that MEBO performs better than most other alternatives; this shows potential for further improvements with respect to the MOOPF problem.

Air-writing is an emerging form of human-computer interaction that enables text entry through hand movements in the air. This paper explores air-writing-based person recognition alongside letter recognition to analyze the correlation between subjects and their writing styles. This analysis has potential applications in early childhood education for assessing cognitive and psychomotor skills, as well as in user authentication. We introduce a comprehensive framework for freeform air-writing analysis using a wearable glove, encompassing person and letter recognition, letter segmentation, feature extraction, and dataset generation. Unlike user authentication where the person recognition depends on a predefined content, such as passcode or signature, the person recognition here depends on the underlying writing style of the writer in free-form. The free-form air-writing indicates the user freedom to select any letter with any size (varying from a quarter to the full size of an A4 page) to write on the air. In the study, a dataset is also developed, containing the air-writing signals from the wearable glove, integrated with IMU sensors (gyroscope and accelerometer). Fourier and wavelet transforms are used to extract features and the performances of various machine learning algorithms, namely Decision Tree, Random-Forest, K-Nearest Neighbors, Support Vector Machine, Artificial Neural Networks, and SubSpace KNN, are comparatively studied. To the best of our knowledge, there is no study for person recognition from freeform air-writing letters through IMU sensors and also there is no study using the Fourier and Wavelet features in this context. Furthermore, the study is also original due to its publicly available air-writing dataset on Turkish alphabet and also applying various machine learning algorithms. The experimental results show that SubSpace KNN is superior to the others under the suggested parameter settings.

In this study, a novel record linkage system for E-commerce products is presented. Our system aims to cluster the same products that are crawled from different E-commerce websites into the same cluster. The proposed system achieves a very high success rate by combining both semi-supervised and unsupervised approaches. Unlike the previously proposed systems in the literature, neither a training set nor structured corpora are necessary. The core of the system is based on Hierarchical Agglomerative Clustering (HAC); however, the HAC algorithm is modified to be dynamic such that it can efficiently cluster a stream of incoming new data. Since the proposed system does not depend on any prior data, it can cluster new products. The system uses bag-of-words representation of the product titles, employs a single distance metric, exploits multiple domain-based attributes and does not depend on the characteristics of the natural language used in the product records. To our knowledge, there is no commonly used tool or technique to measure the quality of a clustering task. Therefore in this study, we use ELKI (Environment for Developing KDD-Applications Supported by Index-Structures), an open-source data mining software, for performance measurement of the clustering methods; and show how to use ELKI for this purpose. To evaluate our system, we collect our own dataset and make it publicly available to researchers who study E-commerce product clustering. Our proposed system achieves 96.25% F-Measure according to our experimental analysis. The other state-of-the-art clustering systems obtain the best 89.12% F-Measure.

The paper introduces a novel feature selection algorithm for labeling identical products collected from online web resources. Product labeling is important for clustering similar or same products. Products blindly crawled over the web sources, such as online sellers, have unstructured data due to having features expressed in different representations and formats. Such data result in feature vectors whose representation is unknown and non-uniform in length. Thus, product labeling, as a challenging problem, needs efficient selection of features that best describe the products. In this paper, an efficient feature selection algorithm is proposed for product labeling problem. Hierarchical clustering is used with the state of the art similarity metrics to assess the performance of the proposed algorithm. The results show that the proposed algorithm increases the performance of product labeling significantly. Furthermore, the method can be applied to any clustering algorithm that works on unstructured data.

Duygu analizi, verilen bir metin belgesinin genel yargısını, metin analizi ve doğal dil işleme teknikleri kullanarak belirleme işlemidir. Bu çalışmada, İngilizce ve Türkçe dillerinde yazılmış film ve ürün yorumlarının, Destek Vektör Makineleri (DVM) sınıflayıcısı kullanarak yapılan, duygu analizi deney sonuçları yer almaktadır. Bunun yanında, farklı doküman vektör hesaplama yöntemleri karşılaştırılmakta ve bu tekniklerin duygu analizi üzerindeki etkileri gösterilmektedir. DVM türleri, kernel çeşitleri, TF veya TF*IDF gibi ağırlıklandırma yöntemleri, TF türleri, IDF türleri, öznitelik oluşturma yöntemleri, öznitelik seçme sistemleri, metin önişleme teknikleri ve vektör normalizasyon teknikleri deneysel olarak analiz edilmektedir. Oluşturduğumuz Türkçe ürün yorumları veri kümesi üzerinde, doğrusal kernel ile kullanılan C-SVC DVM türü, log normalleştirme TF* olasılıklı IDF ağırlıklandırma yöntemi, L2 vektör normalizasyonu, Ki-kare öznitelik seçme ve tekli kelime öznitelikleri kullanılarak %91.33 doğruluk ile en iyi sonuç elde edilmiştir. Ayrıca doküman vektörü hesaplama yöntemlerinin Türkçe ve İngilizce veri kümeleri üzerindeki detaylı karşılaştırmaları da çalışmada yer almaktadır.

Bu çalışmada, “verilen bir kategori için en iyi ürünleri bul” tarzındaki sorguları destekleyen özgün bir ürün arama motoru sistemi önerilmektedir. Geliştirilen ürün arama motoru sistemi, bir odaklı tarayıcı, bir kayıt eşleştirme sistemi, bir duygu analizi sistemi ve bir sorgu motoru sisteminden oluşmaktadır. Odaklı tarayıcı sistemi çeşitli e-ticaret sitelerindeki ürün bilgilerini elde etmek için kullanılmaktadır; kayıt eşleştirme sistemi farklı e-ticaret sitelerinde satılan aynı ürünleri tespit etmektedir; duygu analizi sistemi ürünlere yapılan kullanıcı yorumlarını olumlu veya olumsuz olarak sınıflandırmakta ve bu sınıflandırma hangi ürünlerin aranan kategori için en iyi ürün olduğunu belirlemek için kullanılmaktadır ve sorgu motoru ise kullanıcıların sorgusunu alıp kullanıcılara sonuçları göstermektedir. Bütün sistem C# programlama dilinde .NET 4.5 alt yapısı kullanılarak geliştirilmiştir ve veri depolamak için MS-SQL 2014 veri tabanı yönetim sistemi kullanılmıştır. Önerilen sistemin temelini artımsal olacak şekilde değiştirilmiş Hiyerarşik Aglomeratif Kümeleme algoritmasına dayanan kayıt eşleştirme sistemi oluşturmaktadır. Kayıt eşleştirme işleminin başarısını artırmak için ürün kodu eşleştirme sistemi geliştirilmiştir. Bu sistem farklı e-ticaret sitelerinde satılan ismi farklı olarak yazılmış ancak aynı ürün koduna sahip ürünleri tespit edebilmektedir. Deneysel analizlerimiz sonucunda, e-ticaret ürünlerinin kayıt eşleştirmesinde %96,25 F-ölçeği ve en alakalı ürünler aramasında %100 kesinlik elde edilmiştir. Geliştirilen sistem verilen kategori içinde en iyi ürünleri başarılı bir şekilde kullanıcıya sunabilmektedir. Önerilen sistem mevcut sistemlere göre daha başarılı bir kullanıcı deneyimi sunabilmektedir.