مجله رایانش نرم و فناوری اطلاعات
سال دوازدهم شماره 1 (Spring 2023)

Most color reduction methods that are based on image clustering in a 3D color space have extremely high computational costs, especially for large size images. In this paper, a new fast adaptive color reduction method is proposed which, computationally, is independent of the image size and reduces the pixel depth from 24 bits (used to represent tristimulus values in the most commonly hardware-oriented RGB model) to a maximum of 15 bits. To achieve this purpose, by introducing a new hybrid cost function and using a modified version of the Gravitational Search Algorithm (GSA), an adaptive histogram binning approach has been developed. Although the cube re-quantization accuracy in the histogram binning approach is lower compared to the 3D data clustering method, it leads to a significant reduction in computational cost. In this paper, while taking this advantage, we seek to reduce re-quantization error using the adaptive histogram binning of RGB color components. Despite a significant reduction in pixel depth, the proposed color reduction approach, due to the adaptive reduction of image colors, results in an appropriate color reduction for a wide variety of images.

Vehicular ad hoc networks (VANETs) are a subclass of mobile ad hoc networks (MANETs) which are a promising approach for the intelligent transportation system (ITS). VANET protocols face high challenges due to dynamic topology and symmetric links of networks. A suitable and effective routing mechanism helps to extend the vehicular ad-hoc networks successfully. Sending data from a source node to the base station is a critical issue in VANETs. In these network, one of the efficient routing techniques is geographical routing. In this technique, vehicles are divided into several multicast groups based on their geographical regions to prevent broadcasting packets. In this paper, we improve the inter-vehicle geo-cast routing protocol using fuzzy logic and call it f-IVG. In the proposed protocol, we utilize fuzzy logic to choose a link with most link expiration time (LET) and probability adapts density (PAD) to prevent from rebroadcasting the packet. In the proposed routing protocol, effective parameters such as distance, direction and velocity are used to find the next-hop node. Results of simulation show that the proposed protocol increases packet delivery rate and decreases packet loss rate and end-to-end delay.

- Ensemble feature selection methods are used to improve the robustness of feature selection algorithms. These approaches are a combination of several feature selection methods to achieve the final ranking of features. The reason for using such approaches is derived from the fact that the variety of different methods is more effective than only one method. Each feature selection algorithm may find feature subsets that can be considered local optima in the feature subsets space. Ensemble feature selection is a solution to address this problem. In this paper, we have proposed a bi-objective feature selection algorithm based on Pareto-based ranking. The maximum relevancy and minimum redundancy are considered as our two objectives. Both of the objectives are obtained by the ensemble of three feature selection methods. The final evaluation of features is according to a bi-objective optimization process and the crowding distance of features in this space for ranking the features. The proposed method results are compared with recent ensemble feature selection algorithms and simple feature selection algorithms. The results show that our classification accuracy method is superior to other similar methods and performs in a short time.

The expansion of Internet technologies during the last decades has led to the dependence of user’s activities in cyberspace on services provided by computer networks. One of the most important services is Intrusion Detection System (IDS) which controls network traffic for detecting abnormal behavior as well as anomaly activities. The robustness of the IDS is considered as an essential issue in the networks. In this paper, a brand new model based on meta-heuristic algorithms is projected to detect abnormal packets. In order to develop a high-performance strategy, a benchmark dataset (NSL-KDD), high-accuracy feature selection method and four meta-heuristic algorithms are employed. The dataset consists of 150490 normal and abnormal packets which are captured from a military network connection, and 16 most important features are extracted among 41 features using wrapper feature selection method. The mentioned feature selection method uses the naïve-bayesian approach to evaluate feature subsets. After the feature selection process, four meta-heuristic algorithms are utilized to detect the anomalies in network. The parameters of the cost function (a combination of non-linear regression and sigmoid) are optimized using meta-heuristic algorithms. The experimental results show that the imperialist competitive algorithm (ICA) outperforms other implemented meta-heuristic algorithms in terms of accuracy.

The model checking technique is a formal and effectual way in verification of software systems. By generation and investigation of all model states, it analyses the software systems. The main issue in the model checking of the complicated systems having wide or infinite state space is the lack of memory in generation of all states which is referred to as "state space explosion". The Random Forest algorithm which is capable of knowledge discovery faces the above-cited problem by selecting a few promising paths. In our suggested method, first a small model of the system is developed by the formal language of graph transformation system (GTS). A training data set is created from the small state space. The generated training data set is made available to the Random Forest algorithm to detect and discover the logical relationships existent in it. Then, the knowledge acquired in this way is used in the smart and incomplete exploration of the large state space. The proposed approach is run in GROOVE which is an open source tool for designing and studying the model of graph transformation systems. The results indicate that the suggested method in accordance with the two criteria (average running time and the number of explored states) performs better compared with the other approaches.

The wireless sensor network is a wireless network of self-organized sensors that are distributed at intervals. These sensors are used to group measurements of specific physical quantities or environmental conditions such as temperature, sound, vibration, pressure, motion, or pollutants at various locations. Energy efficiency to extend a wireless sensor network's lifetime should be considered in all network design areas, including hardware and software. Wireless sensor networks may be used in critical applications and transmit sensitive data, so they need methods to secure the data. Secure routing in wireless sensor networks is vital due to the need for data confidentiality, integrity, energy efficiency, authentication, and resilience against attacks. It ensures sensitive data remains private, prevents tampering, optimizes energy usage, verifies node authenticity, and defends against attacks. This paper presents a secure routing method in wireless sensor networks. In the proposed method, due to the nodes' processing limitations and to ensure the security of the exchanged messages, the lightweight columnar transposition cipher method is used. The routing process is done hop by hop, and the next hop is selected based on the parameters of remaining energy, distance to the base station, and node traffic. The proposed routing method is implemented by MATLAB and compared with SMEER and KID-SASR methods. The simulation results show a reduction of end-to-end delay of 50% and 38%, reduction of energy consumption of 39% and 20%, reduction of packet loss rate, and increased number of live nodes by 66% and 59% compared to SMEER and KID-SASR.