Machine learningalgorithms have become pervasive in diverse applications, revolutionizing various domains. However, the abundance of algorithms, each designed for specific purposes, poses a challenge for both novice ...
详细信息
this paper delves into the transformative potential of ML in addressing some of the world's most pressing challenges in optimizing renewable energy and mitigating climate change. How ML algorithms work to improve ...
详细信息
In urban environments, last-mile item delivery relies heavily on trucks, causing issues like noise pollution and traffic congestion. Unmanned Aerial Vehicles (UAVs) offer a promising solution to these challenges. this...
详细信息
ISBN:
(纸本)9783031774256;9783031774263
In urban environments, last-mile item delivery relies heavily on trucks, causing issues like noise pollution and traffic congestion. Unmanned Aerial Vehicles (UAVs) offer a promising solution to these challenges. this study compares the effectiveness of delivery using trucks versus drones. Two customer datasets, one clustered and one random, were used for testing. Route optimization involved four deterministic and four non-deterministic algorithms. the performance of these algorithms, considering the total distance traveled, was evaluated across different datasets and vehicle types. the top two algorithms were further assessed for environmental impact and cost efficiency. Battery consumption along the routes was also analyzed to gauge operational feasibility.
Massive MIMO (Multiple-Input Multiple-Output) technology hugely enhances spectral and energy efficiency in wireless communication systems, but signal detection remains a key challenge due to its high computational com...
详细信息
Object detection is a fundamental task in computer vision, playing a crucial role in various applications such as surveillance, autonomous driving, and agriculture. In agricultural contexts, object detection technique...
详细信息
ISBN:
(数字)9783031774263
ISBN:
(纸本)9783031774256;9783031774263
Object detection is a fundamental task in computer vision, playing a crucial role in various applications such as surveillance, autonomous driving, and agriculture. In agricultural contexts, object detection techniques are essential for assessing plant health and implementing targeted interventions. In this paper, we introduce a novel methodology for the detection of cochineal colonies of Dactylopius opuntiae in cactus pear, which aims to estimate the degree of infestation and facilitate precise treatment strategies. Leveraging recent advancements in deep learning, we present a new dataset specifically curated for colony cochineal detection in cactus pear. We evaluate the performance of three state-of-the-art deep learning models, namely YOLOV7, YOLOV8, and YOLO-NAS, using our dataset. through rigorous experimentation and comparative analysis, we identify YOLOV8 as the most effective model for colony cochineal detection in cactus pear. the proposed approach not only offers accurate colony detection but also provides valuable insights for implementing precise treatment measures, thereby contributing to the efficient management of plant infestations.
Nowadays, the industrial market is characterised by high levels of competition, with customers increasingly demanding in terms of quality, delivery times, costs, etc.. However, with increasing demand and the need to i...
详细信息
ISBN:
(数字)9783031774263
ISBN:
(纸本)9783031774256;9783031774263
Nowadays, the industrial market is characterised by high levels of competition, with customers increasingly demanding in terms of quality, delivery times, costs, etc.. However, with increasing demand and the need to increase productivity, many companies in recent years have dedicated themselves to decentralising their factories, thus moving to distributed production. Today's manufacturing systems are distributed in the sense that there are several jobs that have to be carry out on machines located in different factories. this paper proposes a multi-objective distributed job shop scheduling model with unrelated parallel machines and sequence-dependent setup times. the transport time of raw materials to carry out a given job to a factory is also taken into account. Small instances of the problem were solved using NSGA-III withthe aim of simultaneously minimising two objectives: the makespan and average completion time. Preliminary results show the validity of this approach.
Colorectal cancer is a major health concern, ranking as one of the most common and deadly forms of cancer. It typically begins as polyps, which are abnormal growths in the intestinal mucosa. Identifying and removing t...
详细信息
ISBN:
(数字)9783031774263
ISBN:
(纸本)9783031774256;9783031774263
Colorectal cancer is a major health concern, ranking as one of the most common and deadly forms of cancer. It typically begins as polyps, which are abnormal growths in the intestinal mucosa. Identifying and removing these polyps through colonoscopy is a crucial preventative measure. However, even experienced professionals can overlook some polyps during examinations. In this context, segmentation algorithms can assist medical professionals by identifying areas in an image that correspond to a polyp. these algorithms, which rely on deep learning, require extensive image datasets to effectively learn how to identify and segment polyps. this study aimed to identify public colonoscopy image datasets that contain polyps and to examine how combining these datasets might affect the performance of a deep learning-based segmentation algorithm. After selecting the datasets and defining their combinations, we trained a segmentation algorithm on each combination. the evaluation of the trained models showed that merging datasets can enhance model generalization, with increases of up to 0.242 in the dice coefficient and 0.256 in the Intersection over Union (IoU). these improvements could lead to higher diagnostic accuracy in clinical settings, enhancing efforts to prevent colorectal cancer.
As an optimization algorithm that imitates the natural selection and genetic mechanism, genetic algorithm applied in the combination withthe computer technology, gradually obtained rapid development along withthe co...
详细信息
Type IV secretion systems (T4SSs) are employed by pathogenic bacteria to inject proteins known as Type IV secreted effectors (T4SEs) into both prokaryotic and eukaryotic cells. these effectors play a crucial role in b...
详细信息
ISBN:
(纸本)9798350349764;9798350349771
Type IV secretion systems (T4SSs) are employed by pathogenic bacteria to inject proteins known as Type IV secreted effectors (T4SEs) into both prokaryotic and eukaryotic cells. these effectors play a crucial role in bacterial virulence by disrupting host cell functions and immune responses. While extensive research has focused on classifying T4SEs, the application of unsupervised learning techniques in this domain remains unexplored. In this study, we applied six unsupervised machine learningalgorithms to a dataset of T4SEs and non-effectors to identify distinct clusters. Our findings suggest that unsupervised learning holds potential for gaining a deeper understanding of T4SS mechanisms and the diverse properties of T4SEs. Among the clustering algorithmsthat utilized in this study, it has been observed that the Density-Based Spatial Clustering of applications with Noise (DBSCAN) algorithm generally achieved the highest performance values in terms of average silhouette score and Davies-Bouldin Index (DBI) evaluation metrics. the Laplacian score feature selection algorithm and Principal Component Analysis (PCA) were found to have a positive effect on performance when used in conjunction with amino acid composition (AAC) as a feature extraction method. Additionally, it can be concluded that glycine (G) and lysine (K) are informative amino acids in the clustering algorithmsthat lead to the formation of two clusters.
this article presents an in-depth study on the detection and location of agricultural zones in El Jadida, Morocco. Accurate identification of these zones is crucial for sustainable land management and effective region...
详细信息
ISBN:
(数字)9783031774324
ISBN:
(纸本)9783031774317;9783031774324
this article presents an in-depth study on the detection and location of agricultural zones in El Jadida, Morocco. Accurate identification of these zones is crucial for sustainable land management and effective regional planning. To achieve this objective, a methodology integrating remote sensing techniques, geographic information systems (GIS), and machine learningalgorithms was implemented. the results demonstrate the accuracy and reliability of this approach, providing essential information to decision-makers and agricultural stakeholders in the region. this study highlights the potential of remote sensing technologies to improve the mapping of agricultural areas, thus contributing to sustainable agricultural practices and optimal territorial planning. the methodological approach adopted involves the pre-processing of imagery data from Landsat 7 and 8 satellites, covering the city of El Jadida. We extracted the relevant features, divided them into training and test sets, and then applied three supervised learningalgorithms: random forest (RF), support vector machine (SVM), and gradient boost tree (GTB). through several experiments, we evaluated the performance of each machine learning method in terms of accuracy and Kappa coefficient for the years 2000 and 2020. We also analyzed changes in agricultural areas between these two periods. the results show that random forest is the best performing algorithm, with an accuracy of 98.14% in 2000 and 98% in 2020, and Kappa coefficients of 0.96 in 2000 and 0.95 in 2020.
暂无评论