ISMSI

No single machine learning algorithm is most accurate for all problems due to the effect of an algorithm's inductive bias. Research has shown that a combination of experts of the same type, referred to as a mixture of homogeneous experts, can increase ...

Several decades ago, traditional neural networks were the most efficient machine learning technique. Then it turned out that, in general, a different technique called support vector machines is more efficient. Reasonably recently, a new technique called ...

Item-based collaborative filtering is widely used in industry to build recommendation systems because of its explanatory and efficiency in personalized recommendation. However, item-based collaborative filtering is mostly a shallow linear model, which ...

Nowadays, the majority of data on the Internet is held in an unstructured format, like websites and e-mails. The importance of analyzing these data has been growing day by day. Similar to data mining on structured data, text mining methods for handling ...

The growth of ubiquitous healthcare systems, particularly for general and residential healthcare, is increasing dramatically. One of the most significant components of such systems is the gateway, which acts as a middleware between Internet of Things (...

Successes of deep learning are partly due to appropriate selection of activation function, pooling functions, etc. Most of these choices have been made based on empirical comparison and heuristic ideas. In this paper, we show that many of these choices -...

This research work focusses on the optimization of a robotic manipulation problem. The problem is modeled with the robot simulation software V-REP. The objectives are the optimization movement path of the robot and its robotic arm for certain positions ...

For intelligent systems to become autonomous in any real sense, they need an ability to make decisions on situations that were not entirely conceived of at compile-time. Machine learning algorithms are excellent in mimicking the behaviour of some gold ...

In this paper, the time-dependent travelling salesman problem (TDTSP) is reviewed and the heuristic based on ant colony optimization for solving the TDTSP is proposed. The TDTSP is an extension of the classical travelling salesman problem in which the ...

This paper discusses the combined application of two metaheuristic algorithms, a Genetic Algorithm (GA) and Ant Colony Optimization (ACO). The GA optimizes ACO parameters to find the optimal parameter settings automatically to solve a given Capacitated ...

In this paper, we present an analysis of microscopic behaviors of ants to understand ant interactions that lead to jam-free ant traffic. For the analysis here, we use an agent-based model of ant traffic and mathematical analysis of key scenarios on the ...

Electroencephalogram (EEG) is an electrophysiological monitoring method to record the electrical activity of the brain. EEG is most often used to diagnose epilepsy, which causes abnormalities in EEG readings. It is also used to diagnose sleep disorders, ...

Studies on standard many-objective optimisation problems have indicated that multi-objective optimisation algorithms struggle to solve optimisation problems with more than three objectives, because many solutions become dominated. Therefore, the ...

In this paper, a hybrid of genetic algorithm (GA) and simulated annealing (SA) algorithm (HGSA) is developed to optimize the retardance in citrate (citric acid, CA) coated ferrofluids (FFs). The HGSA not only can overcome the deficiency of GA but also ...

Many optimisation problems have more than three objectives, referred to as many-objective optimisation problems (MaOPs). As the number of objectives increases, the number of solutions that are non-dominated with regards to one another also increases. ...

Real-world networks are often extremely polarized, because the communication between groups of vertices can be weak and, most of the time, only vertices in the same groups or sharing the same beliefs communicate to each other. We formulate the Minimum-...

This paper describes an approach for solving a tardiness constrained flow shop with simultaneously loaded stations using a Genetic Algorithm (GA). This industrial based problem is modeled from a filter basket production line and is generally solved ...

We consider a linear programming problem with uncertain input coefficients. The only information we have are lower and upper bounds for the uncertain values. This gives rise to the so called interval linear programming. The challenging problem here is ...

Cuckoo search is a bio-inspired algorithm based on the reproduction behavior of some cuckoo species. This metaheuristics seems promising to solve the capacitated vehicle routing problem. This paper analyzes the standard capacitated vehicle routing ...

The Time Dependent Traveling Salesman Problem (TD TSP) is an extension of the classic Traveling Salesman Problem towards more realistic conditions. TSP is one of the most extensively studied NP-complete graph search problems. In TD TSP, the edges are ...

Tabu Search (TS) metaheuristic improves simple local search algorithms (e.g. steepest ascend hill-climbing) by enabling the algorithm to escape local optima points. It has shown to be useful for addressing several combinatorial optimization problems. ...

Since the GWO (Grey wolf optimization) has some limitation in application to real-wold problems, such as slow convergence speed, low precision and it easily falls into the local minimal in the later stage of complex optimization problems, a novel grey ...

Collaborative Filtering algorithm is widely used in plentiful personal recommendation system. However, it has low accuracy prediction in sparse data set. Current mainstream collaborative filtering algorithm filter neighbor of target user by calculating ...