Visual searching is a perception task involved with visual attention, attention shift and active scan of the visual environment for a particular object or feature. The key idea of our paper is to ...mimic the human visual searching under the static and dynamic scenes. To build up an artificial vision system that performs the visual searching could be helpful to medical and psychological application development to human machine interaction. Recent state-of-the-art researches focus on the bottom-up and top-down saliency maps. Saliency maps indicate that the saliency likelihood of each pixel, however, understanding the visual searching process can help an artificial vision system exam details in a way similar to human and they will be good for future robots or machine vision systems which is a deeper digest than the saliency map. This paper proposed a computational model trying to mimic human visual searching process and we emphasis the motion cues on the visual processing and searching. Our model analysis the attention shifts by fusing the top-down bias and bottom-up cues. This model also takes account the motion factor into the visual searching processing. The proposed model involves five modules: the pre-learning process; top-down biasing; bottom-up mechanism; multi-layer neural network and attention shifts. Experiment evaluation results via benchmark databases and real-time video showed the model demonstrated high robustness and real-time ability under complex dynamic scenes.
•The learning experience of some other learners is introduced into TLBO so as to improve its performance.•We design a random learning strategy whatever in the Learner Phase or in the Teacher ...Phase.•18 benchmark functions and two real-world problems are used in experimental study.•The results indicate that the proposed algorithm has shown interesting outcomes.
To improve the global performance of the standard teaching–learning-based optimization (TLBO) algorithm, an improved TLBO algorithm (LETLBO) with learning experience of other learners is proposed in the paper. In LETLBO, two random possibilities are used to determine the learning methods of learners in different phases. In the Teacher Phase, the learners improve their grades by utilizing the mean information of the class and the learning experience of other learners according to a random probability. In Learner Phase, the learner learns knowledge from another learner which is randomly selected from the whole class or the mutual learning experience of two randomly selected learners according to a random probability. Moreover, area copying operator which is used in Producer–Scrounger model is used for parts of learners to increase its learning speed. The feasibility and effectiveness of the proposed algorithm are tested on 18 benchmark functions and two practical optimization problems. The merits of the improved method are compared with those of some other evolutionary algorithms (EAs), the results show that the proposed algorithm is an effective method for global optimization problems.
Teaching–learning-based optimization (TLBO) algorithm is one of the recently proposed optimization algorithms. It has been successfully used for solving optimization problems in continuous spaces. To ...improve the optimization performance of the TLBO algorithm, a modified TLBO algorithm with differential and repulsion learning (DRLTLBO) is presented in this paper. In the proposed algorithm, the differential evolution (DE) operators are introduced into the teacher phase of DRLTLBO to increase the diversity of the new population. In the learner phase of DRLLBO, local learning method or repulsion learning method are adopted according to a certain probability to make learners search knowledge from different directions. In the local learning method, learners learn knowledge not only from the best learner but also from another random learner of their neighbors. In the repulsion learning method, learners learn knowledge from the best learner and keep away from the worst learner of their neighbors. Moreover, self-learning method is adopted to improve the exploitation ability of learners when they are not changed in some continuous generations. To decrease the blindness of random self-learning method, the history information of the corresponding learners in some continuous generations is used in self-learning phase. Furthermore, all learners are regrouped after a certain iterations to improve the local diversity of the learners. In the end, DRLTLBO is tested on 32 benchmark functions with different characteristics and two typical nonlinear modeling problems, and the comparison results show that the proposed DRLTLBO algorithm has shown interesting outcomes in some aspects.
Although scholarship in business history has long recognised the importance of managerial and technological knowledge capital as drivers of corporate growth, scholars have made little progress ...towards the classification and measurement of these factors. We demonstrate how Alfred D. Chandler's 'integrated learning base' framework offers a novel approach for classifying and measuring the costs and benefits associated with intellectual capital and corporate innovation. Recasting the integrated learning base into an accounting-based framework provides information useful to corporate management teams for internal decision processes and for reporting to external stakeholders. Our insights are important given corporations' ever-growing reliance on intellectual capital and innovation to develop and sustain a competitive advantage in today's high-tech and global business environment.
The research target was to create a learning innovation through comic package in the form of civilization education which was packed by simply story and interesting picture from the daily life of ...children. By using developing research and collecting data technique such as observation, interview, and test it was resulted as the following: a) the first year resulted prototype learning base-comic to civilization education, and b) the second year resulted effectiveness test of attaining affective domain in learning civilization education by applying learning model base-comic for difficulty learning children who was very significant in Sidoarjo District, Mojokerto District and Surabaya city. The end of this result is comic book for third, the fourth, and fifth classes which had got ISBN. ABSTRAKTarget peneliti ini adalah menciptakan terobosan belajar melalui paket komik berupa materi Pendidikan Kewarganegaraan yang dikemas dengan cerita sederhana dan gambar yang menarik dari kondisi kehidupan nyata sehari-hari anak. Dengan desain penelitian pengembangan dan teknik pengumpulan data observasi, wawancara, dan tes dihasilkan penelitian sebagai berikut: a) tahun pertama menghasilkan prototipe pembelajaran berbasis komik pada pendidikan kewarganegaraan, dan b) tahun kedua menghasilkan uji efektivitas pencapaian ranah afektif dalam pembelajaran pendidikan kewarganegaraan dengan penerapan model pembelajaran berbasis komik pada anak berkesulitan belajar yang sangat signifikan di kabupaten Sidoarjo, kabupaten Mojokerto dan kota Surabaya. Hasil akhir penelitian ini adalah buku komik belajar untuk kelas 3, kelas 4 dan kelas 5 yang telah mendapatkan pengesahan berupa ISBN.
Currently, research on content based image copy detection mainly focuses on robust feature extraction. However, due to the exponential growth of online images, it is necessary to consider searching ...among large scale images, which is very time-consuming and unscalable. Hence, we need to pay much attention to the efficiency of image detection. In this paper, we propose a fast feature aggregating method for image copy detection which uses machine learning based hashing to achieve fast feature aggregation. Since the machine learning based hashing effectively preserves neighborhood structure of data, it yields visual words with strong discriminability. Furthermore, the generated binary codes leads image representation building to be of low-complexity, making it efficient and scalable to large scale databases. Experimental results show good performance of our approach.
A Review on Various Techniques for Spam Detection Kawale, Nitesh J.; Sait, Saad Yunus
2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS),
2021-March-25
Conference Proceeding
Spam has become a major Internet and electronic communication problem in recent years. Many techniques have been developed to combat them. This paper provides an overview of the current method of ...revision of spam filtering. The classification of conventional and learning methods, evaluation and correlation are provided. Some personal enemies are tested and compared fo r spam articles. The statement for a new spam filtering methodology is taken into account.
Objective: To design, implement and verify the efficacy of an original stress management education program for certified care workers that is presented in a large-scale lecture format and is based ...on content that promotes conceptual understanding. Method : A questionnaire survey was conducted before, immediately after and one-month after program implementation with 69 students on a 2-year care worker training course who participated in the program and 185 non-participant control subjects. Results: Two-way repeated measures and analysis of variance revealed an interaction between survey timing and program participation regarding self-efficacy. Self-efficacy increased in the participation group immediately after program implementation but this decreased to pre-participation levels one month later. Conclusion: The present findings demonstrate the efficacy of the program. However, in order to ensure that post-intervention effects become firmly established, it is necessary to develop a long-term support program to be implemented over a fixed follow-up period.
Recently the methods based on visual words have become very popular in near- duplicate retrieval and content identification. However, obtaining the visual vocabulary by quantization is very ...time-consuming and unscalable to large databases. In this paper, we propose a fast feature aggregating method for image representation which uses machine learning based hashing to achieve fast feature aggregation. Since the machine learning based hashing effectively preserves neighborhood structure of data, it yields visual words with strong discriminability. Furthermore, the generated binary codes leads image representation building to be of low-complexity, making it efficient and scalable to large scale databases. The evaluation shows that our approach significantly outperforms state-of-the-art methods.
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