The sine-cosine algorithm (SCA) is a new population-based meta-heuristic algorithm. In addition to exploiting sine and cosine functions to perform local and global searches (hence the name sine-cosine), the SCA introduces several random and adaptive parameters to facilitate the search process. Although it shows promising results, the search process of the SCA is vulnerable to local minima/maxima due to the adoption of a fixed switch probability and the bounded magnitude of the sine and cosine functions (from -1 to 1). In this paper, we propose a new hybrid Q-learning sine-cosine- based strategy, called the Q-learning sine-cosine algorithm (QLSCA). Within the QLSCA, we eliminate the switching probability. Instead, we rely on the Q-learning algorithm (based on the penalty and reward mechanism) to dynamically identify the best operation during runtime. Additionally, we integrate two new operations (Lévy flight motion and crossover) into the QLSCA to facilitate jumping out of local minima/maxima and enhance the solution diversity. To assess its performance, we adopt the QLSCA for the combinatorial test suite minimization problem. Experimental results reveal that the QLSCA is statistically superior with regard to test suite size reduction compared to recent state-of-the-art strategies, including the original SCA, the particle swarm test generator (PSTG), adaptive particle swarm optimization (APSO) and the cuckoo search strategy (CS) at the 95% confidence level. However, concerning the comparison with discrete particle swarm optimization (DPSO), there is no significant difference in performance at the 95% confidence level. On a positive note, the QLSCA statistically outperforms the DPSO in certain configurations at the 90% confidence level.

• MeSH
• algoritmy * MeSH
• heuristika * MeSH
• počítačová simulace MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

Telemedicine is an emerging development in the healthcare domain, where the Internet of Things (IoT) fiber optics technology assists telemedicine applications to improve overall digital healthcare performances for society. Telemedicine applications are bowel disease monitoring based on fiber optics laser endoscopy, gastrointestinal disease fiber optics lights, remote doctor-patient communication, and remote surgeries. However, many existing systems are not effective and their approaches based on deep reinforcement learning have not obtained optimal results. This paper presents the fiber optics IoT healthcare system based on deep reinforcement learning combinatorial constraint scheduling for hybrid telemedicine applications. In the proposed system, we propose the adaptive security deep q-learning network (ASDQN) algorithm methodology to execute all telemedicine applications under their given quality of services (deadline, latency, security, and resources) constraints. For the problem solution, we have exploited different fiber optics endoscopy datasets with images, video, and numeric data for telemedicine applications. The objective is to minimize the overall latency of telemedicine applications (e.g., local, communication, and edge nodes) and maximize the overall rewards during offloading and scheduling on different nodes. The simulation results show that ASDQN outperforms all telemedicine applications with their QoS and objectives compared to existing state action reward state (SARSA) and deep q-learning network (DQN) policy during execution and scheduling on different nodes.

• MeSH
• algoritmy MeSH
• deep learning * MeSH
• internet věcí * MeSH
• lidé MeSH
• technologie optických vláken MeSH
• telemedicína * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

• MeSH
• buňky * MeSH
• molekulární biologie MeSH

• Konspekt
• Biochemie. Molekulární biologie. Biofyzika
• NLK Obory
• molekulární biologie, molekulární medicína
• NLK Publikační typ
• učebnice vysokých škol

• MeSH
• algoritmy MeSH
• informatika MeSH

• Konspekt
• Lékařské vědy. Lékařství
• NLK Obory
• lékařská informatika

The Differential Evolution (DE) is a widely used bioinspired optimization algorithm developed by Storn and Price. It is popular for its simplicity and robustness. This algorithm was primarily designed for real-valued problems and continuous functions, but several modified versions optimizing both integer and discrete-valued problems have been developed. The discrete-coded DE has been mostly used for combinatorial problems in a set of enumerative variants. However, the DE has a great potential in the spatial data analysis and pattern recognition. This paper formulates the problem as a search of a combination of distinct vertices which meet the specified conditions. It proposes a novel approach called the Multidimensional Discrete Differential Evolution (MDDE) applying the principle of the discrete-coded DE in discrete point clouds (PCs). The paper examines the local searching abilities of the MDDE and its convergence to the global optimum in the PCs. The multidimensional discrete vertices cannot be simply ordered to get a convenient course of the discrete data, which is crucial for good convergence of a population. A novel mutation operator utilizing linear ordering of spatial data based on the space filling curves is introduced. The algorithm is tested on several spatial datasets and optimization problems. The experiments show that the MDDE is an efficient and fast method for discrete optimizations in the multidimensional point clouds.

• MeSH
• algoritmy * MeSH
• biologická evoluce * MeSH
• datové soubory jako téma * MeSH
• interpretace obrazu počítačem * MeSH
• rozpoznávání automatizované * MeSH
• Publikační typ
• časopisecké články MeSH

Non-Hodgkin lymphoma (NHL) is the third most common malignancy diagnosed in children. The vast majority of paediatric NHL are either Burkitt lymphoma (BL), diffuse large B-cell lymphoma (DLBCL), anaplastic large cell lymphoma (ALCL), or lymphoblastic lymphoma (LL). Multi-agent chemotherapy is used to treat all of these types of NHL, and survival is over 90% but the chemotherapy regimens are intensive, and outcomes are generally poor if relapse occurs. Therefore, targeted therapies are of interest as potential solutions to these problems. However, the major problem with all targeted agents is the development of resistance. Mechanisms of resistance are not well understood, but increased knowledge will facilitate optimal management strategies through improving our understanding of when to select each targeted agent, and when a combinatorial approach may be helpful. This review summarises currently available knowledge regarding resistance to targeted therapies used in paediatric anaplastic lymphoma kinase (ALK)-positive ALCL. Specifically, we outline where gaps in knowledge exist, and further investigation is required in order to find a solution to the clinical problem of drug resistance in ALCL.

• Publikační typ
• časopisecké články MeSH
• přehledy MeSH

The important task of generating the minimum number of sequential triangle strips (tristrips) for a given triangulated surface model is motivated by applications in computer graphics. This hard combinatorial optimization problem is reduced to the minimum energy problem in Hopfield nets by a linear-size construction. In particular, the classes of equivalent optimal stripifications are mapped one to one to the minimum energy states reached by a Hopfield network during sequential computation starting at the zero initial state. Thus, the underlying Hopfield network powered by simulated annealing (i.e., Boltzmann machine), which is implemented in the program HTGEN, can be used for computing the semioptimal stripifications. Practical experiments confirm that one can obtain much better results using HTGEN than by a leading conventional stripification program FTSG (a reference stripification method not based on neural nets), although the running time of simulated annealing grows rapidly near the global optimum. Nevertheless, HTGEN exhibits empirical linear time complexity when the parameters of simulated annealing (i.e., the initial temperature and the stopping criterion) are fixed and thus provides the semioptimal offline solutions, even for huge models of hundreds of thousands of triangles, within a reasonable time.

• MeSH
• algoritmy MeSH
• finanční podpora výzkumu jako téma MeSH
• lidé MeSH
• neuronové sítě MeSH
• počítačová grafika MeSH
• počítačová simulace MeSH
• umělá inteligence MeSH
• Check Tag
• lidé MeSH

In this paper, Self-Organizing Map (SOM) for the Multiple Traveling Salesman Problem (MTSP) with minmax objective is applied to the robotic problem of multigoal path planning in the polygonal domain. The main difficulty of such SOM deployment is determination of collision-free paths among obstacles that is required to evaluate the neuron-city distances in the winner selection phase of unsupervised learning. Moreover, a collision-free path is also needed in the adaptation phase, where neurons are adapted towards the presented input signal (city) to the network. Simple approximations of the shortest path are utilized to address this issue and solve the robotic MTSP by SOM. Suitability of the proposed approximations is verified in the context of cooperative inspection, where cities represent sensing locations that guarantee to "see" the whole robots' workspace. The inspection task formulated as the MTSP-Minmax is solved by the proposed SOM approach and compared with the combinatorial heuristic GENIUS. The results indicate that the proposed approach provides competitive results to GENIUS and support applicability of SOM for robotic multigoal path planning with a group of cooperating mobile robots. The proposed combination of approximate shortest paths with unsupervised learning opens further applications of SOM in the field of robotic planning.

• MeSH
• algoritmy * MeSH
• cestování MeSH
• lidé MeSH
• neuronové sítě * MeSH
• pohyb těles * MeSH
• robotika * MeSH
• rozpoznávání automatizované * MeSH
• umělá inteligence * MeSH
• vnímání prostoru MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

• MeSH
• genetika MeSH
• genom MeSH

• Konspekt
• Obecná genetika. Obecná cytogenetika. Evoluce
• NLK Obory
• genetika, lékařská genetika

"Genetics and Genomics in Medicine is a new textbook written for undergraduate and graduate students, as well as medical researchers, which explains the science behind the uses of genetics and genomics in medicine today. It is not just about rare inherited and chromosomal disorders, but how genetics affects the whole spectrum of human health and disease. DNA technologies are explained, with emphasis on the modern techniques that have revolutionized the use of genetic information in medicine and are indicating the role of genetics in common complex diseases. The detailed, integrative coverage of genetic approaches to treatment and prevention includes pharmacogenomics and the prospects for personalized medicine. Cancers are essentially genetic diseases and are given a dedicated chapter that includes new insights from cancer genome sequencing. Clinical disorders are covered throughout and there are extensive end-of-chapter questions and problems"--Provided by publisher.