Seed Selection Method Research Articles

A seed-epidermis-feature-recognition-based lightweight peanut seed selection method for embedded systems

High-quality seeds improve the germination rate. Therefore, seed selection before sowing peanuts is crucial. Current peanut seed selection methods include color sorters and sieving machines, which are efficient but lack accuracy due to their reliance on a single indicator. Manual selection is inefficient and subject to subjective influences. Although machine vision and deep learning have performed well in crops such as corn and pepper, most existing research is based on PC or MATLAB platforms, which are not portable and are prone to interference, making them unsuitable for field applications. This study developed a lightweight model for recognizing peanut seed epidermal features. The model was based on deep learning and model quantization techniques. The transfer learning method was used to use four pre-trained models, EfficientNet_b0, EfficientNetv2-b0, MobileNet_v2_35_224, and NasNet_Mobile, as feature extraction layers, the input layer was added before the feature extraction layer, and the dropout and dense layers were added after the feature extraction layer to construct a classifier. The peanut seed selection network(PSSNet) models were constructed and named PSSNet-E, PSSNet-E2, PSSNet-M, and PSSNet-N, respectively, and trained on the Huayu 22 peanut seed dataset constructed in this study. The constructed models were compressed using model quantization technology, and four quantized models were obtained, namely PSSNet-Ef, PSSNet-E2f, PSSNet-Mf, and PSSNet-Nf. Finally, PSSNet-Mf, which had the best model evaluation, was selected as the peanut selection model for this study and deployed on a prototype for testing. Compared with the unquantized model, the size of the quantized model was reduced by two-thirds and the running speed was increased by 37%. A total of 400 Huayu 22 peanut seeds were selected as test samples, and 5 selection tests were conducted.The results showed that the average accuracy was 95.3%, which met the requirements of peanut selection at the production site.

A bridge between influence models and control methods

Understanding how influence is seeded and spreads through social networks is an increasingly important study area. While there are many methods to identify seed nodes that are used to initialize a spread of influence, the idea of using methods for selecting driver nodes from the control field in the context of seed selection has not been explored yet. In this work, we present the first study of using control approaches as seed selection methods. We employ a Minimum Dominating Set to develop a candidate set of driver nodes. We propose methods based upon driver nodes (i.e. Driver-Random, Driver-Degree, Driver-Closeness, Driver-Betweenness, Driver-Degree-Closeness-Betweenness, Driver-Kempe, Driver-Ranked) for selecting seeds from this set. These methods make use of centrality measures to rank the driver nodes in terms of their potential as seed nodes. We compare proposed methods to existing approaches using the Linear Threshold model on both real and synthetic networks. Our experiment results show that the proposed methods consistently outperform the benchmarks. We conclude that using driver nodes as seeds in the influence spread results in faster and thus more effective spread than when applying traditional methods.

An Accurate Outlier Rejection Network With Higher Generalization Ability for Point Cloud Registration

Feature-based point cloud registration algorithms have gained more attention recently for their high robustness. Outlier rejection is a key step of such algorithms. With the development of deep learning, some of the learning-based outlier rejection methods have been proposed and implemented in various scenes. However, generalization ability and accuracy of the existing methods in complex scenes still need to be improved. In this paper, we construct a neural network for removing outlier correspondences. Particularly, we propose a novel seed selection method based on feature consistency (FC) and a new loss function based on second order feature consistency (FC <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> ). Experimental results on various datasets show the proposed network achieves better accuracy and stronger generalization ability than the state-of-the-art learning-based algorithms.

Detecting local communities in complex network via the optimization of interaction relationship between node and community.

The goal of local community detection algorithms is to explore the optimal community with a reference to a given node. Such algorithms typically include two primary processes: seed selection and community expansion. This study develops and tests a novel local community detection algorithm called OIRLCD that is based on the optimization of interaction relationships between nodes and the community. First, we introduce an improved seed selection method to solve the seed deviation problem. Second, this study uses a series of similarity indices to measure the interaction relationship between nodes and community. Third, this study uses a series of algorithms based on different similarity indices, and designs experiments to reveal the role of the similarity index in algorithms based on relationship optimization. The proposed algorithm was compared with five existing local community algorithms in both real-world networks and artificial networks. Experimental results show that the optimization of interaction relationship algorithms based on node similarity can detect communities accurately and efficiently. In addition, a good similarity index can highlight the advantages of the proposed algorithm based on interaction optimization.

Optimal Influencer Marketing Campaign Under Budget Constraints Using Frank-Wolfe

Influencer marketing has become a thriving industry with a global market value expected to reach 15 billion dollars by 2022. The advertising problem that such agencies face is the following: given a monetary budget find a set of appropriate influencers that can create and publish posts of various types (e.g. text, image, video) for the promotion of a target product. The campaign's objective is to maximize across one or multiple online social platforms some impact metric of interest, e.g. number of impressions, sales (ROI), or audience reach. In this work, we present an original continuous formulation of the budgeted influencer marketing problem as a convex program. We further propose an efficient iterative algorithm based on the Frank-Wolfe method, that converges to the global optimum and has low computational complexity. We also suggest a simpler near-optimal rule of thumb, which can perform well in many practical scenarios. We test our algorithm and the heuristic against several alternatives from the optimization literature as well as standard seed selection methods and validate the superior performance of Frank-Wolfe in execution time and memory, as well as its capability to scale well for problems with very large number (millions) of social users.

Indigenous agricultural knowledge: A neglected human based resource for sustainable crop protection and production

Indigenous knowledge, developed over generations and owned by communities or individuals within a community, offers alternative strategies and perspectives on resource management and use. However, as emphasized in the contemporary agricultural history of Ethiopia, the most effective indigenous agricultural knowledge has not been well documented and some of them are replaced by modern techniques. This study was therefore conducted to assess and document community-based techniques to control pests and diseases and the practical implications of indigenous farming techniques. A focus group discussion, key informant interviews and semi-structured questionnaires were conducted with 150 farmers. The result showed that a substantial number (92%) of the farming community uses indigenous based plant protection measures. Indigenous farmers (92%) splash liquids made of cow urine to control the adverse effect of fungi. Farmers are also using different seed selection methods for next season planting. About 29% of the farmers do single head-based seed selection prior to mass harvesting, 34% are collected as “Qerm” and 45% select their seeds during threshing. Indigenous farming knowledge varies with the natural feature of the growing location and cropping system, including the rainfall pattern, soil fertility status, crop, and weed type. The observed positive effect of indigenous agricultural practices on crop production substantiates the need to include these essential approaches in the cultivation system along with the modern agronomic techniques. This might reduce the dependency on expensive and pollutant agricultural inputs. However, sociodemographic factors such as educational level, marital status and farming experience have been found as a determinant factor that influences utilization of indigenous farming knowledge. It can be therefore inferred that documenting indigenous knowledge and proving its applicability scientifically could contribute to organically oriented agricultural production and consequently reduce agriculture's contribution to environmental pollution.

Dynamic node influence tracking based influence maximization on dynamic social networks

Influence maximization is one of the popular problems in social network analysis. Its application includes viral marketing, epidemic control, and recommender systems. Most of the existing methods are applicable to static networks. However, many real-world networks are dynamic and they evolve with time. This paper studies influence maximization on dynamic social networks and proposes Dynamic Influence based Seed Selection (DYISSE) method. To find ‘k’ most influential nodes, the proposed method first estimates the influence of each node by introducing a Two-hop Triangular Influence (TTI) that measures the influence strength of each node by utilizing the property of triangles. Based on the TTI, a method named the Dynamic Tractable Set (D-TeSt) is proposed to track the changes in the influence of individual nodes when the topology of the network changes with time. The performance of the DYISSE method is analyzed under the LAIC model on two real-world and twelve synthetic dynamic networks. The results show that the proposed method performs better than the temporal versions of DegreeDiscount, MaxDegree, K-shell, Random, and Closeness centrality measures in extracting initial influential nodes.

Theoretical and experimental analyses of high-speed seed filling in limited gear-shaped side space of cotton precision dibbler

A novel state-of-motion adjustment method of seeds for high-speed seed filling in a cotton precision dibbler is proposed to solve the problems of arch lifting (multiple particles squeezing each other and causing congestion) and leakage (caused by exceedingly high relative linear velocity between the seed and seed tray during the seed-filling process). This method is based on the stress of the seed group and the limited gear-shaped side space (seed holding space). Kinematic and dynamic analyses of the main states of motion of the seed in the holding space were conducted, and the effects of the stress and movement states on seed-filling performance and automatic seed-cleaning performance were investigated. Through simulation, the velocity fluctuation timings of transverse and longitudinal rotation fillings in the holding space were compared. It was found that sufficient arrangement and a full state-of-motion adjustment on the seed-arranging surface was the key condition to ensuring that seeds fill the type hole by transverse rotation of the seed-filling surface. When seeds were not fully arranged and their state of motion not fully adjusted on the seed-arranging surface, the structural design of the type hole, seed-cleaning surface, and seed-filling surface affected the filling result of longitudinal rotation. The Box–Behnken centre combination method was implemented by setting the friction coefficient μ1 (X1), seed height z (X2), and rotation speed n (X3) as the factors with the qualified index A, leakage index M, and velocity fluctuation ΔH as the evaluation indexes. Multiple regression models and response surface optimisation analyses were performed to obtain the optimal combination of parameters affecting the dibbler seed-filling performance. The results indicated that the most significant factors affecting the A, M, and ΔH, were: z >n > μ1, z >n > μ1, and n >z > μ1. The optimal combination of these parameters was μ1 = 0.463, z = 0.4 kg, and n = 1.88 rps (approximately 9.78 km/h), providing a maximum A = 98.837 %, minimum M = 0.199 %, and ΔH = 0.111 m/s. When ΔH was in the interval of 0.09–0.18 m/s, A was high and stable, whereas M was small, indicating that the seed-filling performance was effective. When μ1 = 0.48, n = 1.5 rps (approximately 7.8 km/h), and z = 0.3 kg, the average values of the bench test were 91.07 % and 6.07 %, consistent with the simulation results. This study contributes significantly toward the research and development of the type hole of precise dibblers, high-speed seed selection method, orderly arrangement, migration, and quantitative separation method of irregular rotary material groups.

Product Adoption Maximization Leveraging Social Influence and User Interest Mining

A Social Networking Service (SNS) platform provides digital footprints to discover users’ interests and track the social diffusion of product adoptions. How to identify a small set of seed users in a SNS who is potential to adopt a new promoting product with high probability, is a key question in social networks. Existing works approached this as a social influence maximization problem. However, these approaches relied heavily on text information for topic modeling and neglected the impact of seed users’ relation in the model. To this end, in this paper, we first develop a general product adoption function integrating both users’ interest and social influence, where the user interest model relies on historical user behavior and the seed users’ evaluations without any text information. Accordingly, we formulate a product adoption maximization problem and prove NP-hardness of this problem. We then design an efficient algorithm to solve this problem. We further devise a method to automatically learn the parameter in the proposed adoption function from users’ past behaviors. Finally, experimental results show the soundness of our proposed adoption decision function and the effectiveness of the proposed seed selection method for product adoption maximization.

Salient object detection by robust foreground and background seed selection

This paper presents a novel background and foreground seed selection method for graph-based salient object detection. First, according to the boundary prior which considers that the image boundary is mainly the background, we select the initial background seed set and optimize it through our proposed two-stage background seed correction processes by combing multiple internal image features. Second, different from most existing center-based prior methods, this paper uses the convex hull of the point of interest to estimate the location of salient objects and thus obtains the initial foreground seed set. Moreover, foreground seeds are refined by the proposed foreground seed correction process, which depends on the color and spatial differences between seeds. Third, we adopt the extended random walk to propagate the background and foreground labels. Finally, a fusion model is proposed to integrate the background- and foreground-based saliency maps, generating final salient object detection results. Experiments on publicly available data sets show that the proposed algorithm achieves better results in contrast to other state-of-the-art methods.

Financial Investment Recommendation in Coastal Areas Based on Improved Clustering Algorithm

Huang, Y.L., 2020. Financial investment recommendation in coastal areas based on improved clustering algorithm. In: Al-Tarawneh, O. and Megahed, A. (eds.), Recent Developments of Port, Marine, and Ocean Engineering. Journal of Coastal Research, Special Issue No. 110, pp. 215–218. Coconut Creek (Florida), ISSN 0749-0208.Based on the full analysis of the advantages and disadvantages of the traditional K - means and BIRCH clustering algorithms, an improved incremental clustering algorithm based on the core tree is proposed. The optimal global parameters Eps and MinPts are adaptively calculated according to the KNN distribution and mathematical statistics to avoid manual intervention in the clustering process so as to realize the full automation of the clustering process. By improving the seed selection method for regional query, no missing operation is needed to effectively improve the efficiency of clustering. The algorithm can helps financial users to make reasonable financial investment strategies in coastal areas, to a certain extent, reduce the financial investment risk, with strong practical significance.

Automatic Seeded Region Growing Image Segmentation for Medical Image Segmentation: A Brief Review

In the domain of computer technology, image processing strategies have become a part of various applications. A few broadly used image segmentation methods have been characterized as seeded region growing (SRG), edge-based image segmentation, fuzzy [Formula: see text]-means image segmentation, etc. SRG is a quick, strongly formed and impressive image segmentation algorithm. In this paper, we delve into different applications of SRG and their analysis. SRG delivers better results in analysis of magnetic resonance images, brain image, breast images, etc. On the other hand, it has some limitations as well. For example, the seed points have to be selected manually and this manual selection of seed points at the time of segmentation brings about wrong selection of regions. So, a review of some automatic seed selection methods with their advantages, disadvantages and applications in different fields has been presented.

Freshness-Aware Seed Selection for Offloading Cellular Traffic Through Opportunistic Mobile Networks

Offloading cellular traffic through Opportunistic Mobile Networks, also known as opportunistic offloading has been proposed as a promising way to relieve the overload of cellular networks. The efficiency of such opportunistic offloading is highly determined by the selection of initial seeds. With considering both the freshness of the content and the cost of transmission from the cellular network to the initial seeds, this paper defines a novel freshness-aware seed selection optimization problem to find both the optimal number of initial seeds and the maximum overall content utility. To solve the optimization problem, the optimal strategy is first analyzed, and then two seed selection methods: the greedy seed selection method and the decay-based seed selection method are proposed to find the optimal number of initial seeds to maximize the overall content utility. The greedy seed selection method iteratively selects nodes with the maximum Freshness Centrality value as initial seeds. To further improve the performance, the decay-based seed selection method selects initial seeds who are far apart and important in theirs local structure. Extensive real trace-driven simulations are conducted to evaluate the performance of our proposed seed selection methods. The results show that as expected the proposed decay-based seed selection method is superior to the proposed greedy seed selection method and the random seed selection method, not only in the Infocom 06 trace, but also in the MIT Reality trace.

Adaptive Region Growing Image Segmentation Algorithms for Breast MRI

Early detection and characterization of breast lesion are important for a better and effective treatment of breast cancer. In this paper, four different adaptive region growing image segmentation algorithms are compared. In fact, seed selection was a vital step in the success of region growing methods, so, better schemes for seed selection methods are proposed, namely, joint probabilistic seed selection (JPSS) and Generalised simulated annealing (GSA) based seed selection. The proposed region growing methods namely Fuzzy Region Growing (FRG) and Neutrosophic Region Growing (NRG) are integrated as JPSS-FRG and GSA-NRG frameworks. Another two methods are Scale Invariant Region growing (SiRG) and Fuzzy Neutrosophic Confidence Region growing (FNCRG). The results showed that FNCRG algorithm increases breast cancer detection rate on MRI breast images with the maximum of 93% is achieved. SiRG algorithm improves the true positive rate by 13% compared to existing methods. Further, GSA-NRG makes better segmentation accuracy by 9% and true positive rate by 12%. Also, JPSS-FRG algorithm enhances segmentation accuracy by 24% and improving the true positive rate by 27% compared to Region Growing-Cellular Neural Network (RG-CNN) and Seeded Region Growing-Particle swarm optimization (SRG-PSO) methods respectively.

A Farm Typology for Adoption of Innovations in Potato Production in Southwestern Uganda

In Uganda, low productivity of potato might be associated with poor and diverse adoption of innovative crop management practices. This paper aims to identify the potato farm typologies in southwestern Uganda, i.e. collections of farms that are homogeneous in uptake of innovations (use of fertilizer, organic input, fungicides, pesticides, seed selection methods, seed refreshment by using quality declared seed, and sole cropping), and to analyse these typologies based on socio-economic characteristics, access to agricultural extension services, memberships of farmers’ groups, yield levels of potato and return rates. A farm household survey (n=270) was carried out and principal component analysis and cluster analysis were used to identify types of farms differing in adoption of innovations. Four farm types were identified that demonstrated significant differences in uptake of innovation practices; despite the small differences in yield among farm types, differences in uptake were associated with significant differences in the yield and further in land ownership, availability of labourers and cash, economical return, and access to knowledge. The farm type with high frequencies of using organic input, fungicide input, pesticide input, seed selection method, quality declared seed and sole cropping achieved highest potato productivity; the farm type with frequent use of fungicide input and use of seed plot technology or positive selection was associated with the lowest potato yield. The findings emphasise associations between innovation uptake and farm characteristics. Opportunities for improvement through extension services and shared knowledge can achieve wider adoption, enhance potato productivity and increase income for smallholder farmers.

Combining external prior and internal features: toward a robust foreground seed selection method

Graph-based salient object detection has been widely applied in many applications, because of its excellent performance and strong theoretical basis. Basically, the performance of this type of methods depends on the correctness in foreground seed selection. In research aiming to exactly identify the seeds on foreground objects, an external prior has been defined in recent work as having an image boundary that is mostly background (called boundary prior), so the foreground seeds must locate around the image center. However, this is not the case when salient objects are spatially close to the image boundary. This problem will cause a severe error in salient object detection, because background noises are likely mixed in foreground seeds. To solve this problem, we propose a robust foreground seed selection method for salient object detection. In our method, the external prior and multiple internal image features are combined for foreground seed selection. Our method can relax the limitation of the external prior and make the foreground seed selection more adaptive and robust to diverse samples. As a result, the proposed method can generate satisfying results, no matter where the salient object is located. This advantage is demonstrated by experimental comparisons with several state-of-art methods.

Seed selection algorithm through K-means on optimal number of clusters

Clustering is one of the important unsupervised learning in data mining to group the similar features. The growing point of the cluster is known as a seed. To select the appropriate seed of a cluster is an important criterion of any seed based clustering technique. The performance of seed based algorithms are dependent on initial cluster center selection and the optimal number of clusters in an unknown data set. Cluster quality and an optimal number of clusters are the important issues in cluster analysis. In this paper, the proposed seed point selection algorithm has been applied to 3 band image data and 2D discrete data. This algorithm selects the seed point using the concept of maximization of the joint probability of pixel intensities with the distance restriction criteria. The optimal number of clusters has been decided on the basis of the combination of seven different cluster validity indices. We have also compared the results of our proposed seed selection algorithm on an optimal number of clusters using K-Means clustering with other classical seed selection algorithms applied through K-Means Clustering in terms of seed generation time (SGT), cluster building Time (CBT), segmentation entropy and the number of iterations (NOTK−means). We have also made the analysis of CPU time and no. of iterations of our proposed seed selection method with other clustering algorithms.

Cooperative Hybrid Semi-Supervised Learning for Text Sentiment Classification

A large-scale and high-quality training dataset is an important guarantee to learn an ideal classifier for text sentiment classification. However, manually constructing such a training dataset with sentiment labels is a labor-intensive and time-consuming task. Therefore, based on the idea of effectively utilizing unlabeled samples, a synthetical framework that covers the whole process of semi-supervised learning from seed selection, iterative modification of the training text set, to the co-training strategy of the classifier is proposed in this paper for text sentiment classification. To provide an important basis for selecting the seed texts and modifying the training text set, three kinds of measures—the cluster similarity degree of an unlabeled text, the cluster uncertainty degree of a pseudo-label text to a learner, and the reliability degree of a pseudo-label text to a learner—are defined. With these measures, a seed selection method based on Random Swap clustering, a hybrid modification method of the training text set based on active learning and self-learning, and an alternately co-training strategy of the ensemble classifier of the Maximum Entropy and Support Vector Machine are proposed and combined into our framework. The experimental results on three Chinese datasets (COAE2014, COAE2015, and a Hotel review, respectively) and five English datasets (Books, DVD, Electronics, Kitchen, and MR, respectively) in the real world verify the effectiveness of the proposed framework.

Parallel seed selection method for overlapping community detection in social network

Social network analysis is one of the key areas of research during modern times. The social network is growing with more users and the ties between them day by day. This reason brings out many research queries and new conclusions from this area. Overlapping community detection in the social network is one such research problem which has acquired interest among researchers nowadays. Earlier, the investigation was in finding out algorithms to detect communities in the network sequentially. There are many distinguished findings toward overlapping community detection. Due to the velocity of data in the current era, the available algorithms will be a bit sluggish in processing the data. The proposed algorithm uses parallel processing engine to resolve this delay problem in the current scenario. The algorithm in parallel finds out the superior seed set in the network and expands it in parallel to find out the community. The work shows amazing improvement in the runtime and also detects quality groups in the network.

Discover deeper bugs with dynamic symbolic execution and coverage‐based fuzz testing

Coverage-based fuzz testing and dynamic symbolic execution are both popular program testing techniques. However, on their own, both techniques suffer from scalability problems when considering the complexity of modern software. Hybrid testing methods attempt to mitigate these problems by leveraging dynamic symbolic execution to assist fuzz testing. Unfortunately, the efficiency of such methods is still limited by specific program structures and the schedule of seed files. In this study, the authors introduce a novel lazy symbolic pointer concretisation method and a symbolic loop bucket optimisation to mitigate path explosion caused by dynamic symbolic execution in hybrid testing. They also propose a distance-based seed selection method to rearrange the seed queue of the fuzzer engine in order to achieve higher coverage. They implemented a prototype and evaluate its ability to find vulnerabilities in software and cover new execution paths. They show on different benchmarks that it can find more crashes than other off-the-shelf vulnerability detection tools. They also show that the proposed method can discover 43% more unique paths than vanilla fuzz testing.