---

Some samples were collected from tomato fields in Qazvin from tomato plants with big bud symptoms such as plant droop and purplish vein under the leaf, enlarged and sac-like pistils and malformed buds. DNA was extracted from the veins and vascular tissues of the plant with CTAB-based methods. In symptomatic plants, DNA fragments of 1800 and 1200bp were amplified by PCR using P1/P7, R16F2n/R16R2 primers. Restriction fragment length polymorphism (RFLP) analysis of nested R16F2n/R16R2 primed PCR product (1200bp) showed that the tomato big bud phytoplasma from Qazvin (TOM-Qazvin) is a member of clover proliferation (16SrVI). Phylogenetic analysis of 16SrRNA and putative restriction site analysis of the R16F2n/R16R2 primed sequence classified TOM phytoplasma in clover proliferation (16SrVI) group and belonged to subgroup 16SrVI-A. Virtual RFLP by using 1200bp sequencing of 16SRNA and 17 restriction enzymes confirmed that TOM-Qazvin belonged to the subgroup 16SrVI-A and16SrVI group. To our knowledge, this is the first report of tomato big bud disease in Qazvin province.

---

Aims Due to increase of demand for industrial and agricultural products, many tropical regions of Iran have experienced landscape changes. The present study aims to detect the land use/land cover (LULC) using some pixel/object-based approaches.
 
Method This research was conducted in Jiroft area using some pixel-based and object-based image analysing methods (PBIA and OBIA respectively). To this end, at the first phase, the LULC maps were extracted using PBIA for September, 2020. The PBIA are including as Mahalanobis distance (MD), maximum likelihood (ML), neural network (NN), support vector machine (SVM). At the second phase, the LULC was produced using OBIA approach, encompassing the multi-resolution method and decision tree (DT) technique, for segmentation and classification respectively. Using a hybrid methodology, the high-resolution images of Worldview-2 were segmented. The segmented objects were later combined with the 7-month time series of NDVI, to find the necessary thresholds for DT.
 
Findings Results of the LULC maps demonstrated that the kappa coefficient and overall accuracy for ISODATA, MD, ML, NN, and SVM methods were calculated to be (51%, 66%), (81%, 86%), (88%, 91%), (90%, 93%) and (88% and 92%), respectively. The outcomes of the second phase for mapping the LULC showed the OBIA achieved a high overall accuracy of about 96%.
Conclusion among the PBIA techniques and regarding both accuracy and execution time, the ML was the best. Although both PBIA and OBIA approaches are applicable in mapping LULC, the OBIA significantly outperformed the PBIA classifiers by higher overall accuracy and Kappa statistics

---

Since it is essential to deliver smoothed sinusoidal voltage to the customers, diagnosing power quality (PQ) events has played important role in power delivery and conversion. This diagnostic scheme should be accurate to classify PQ events from other events in power system. Also it should be fast enough to rapidly mitigate PQ events. In this paper, an algorithm based on Core Vector Machine (CVM) has been introduced to classify power quality events. Feature selection method has also been applied to increase the accuracy of the classifier’s algorithm. Some features have been selected among several others extracted by wavelet transform. In addition, eight different classes are simulated due to the corresponding equations used in previous studies. Evaluating the performance of the algorithm, different indices have been used to assess the operation of the classifier’s algorithm. Simulations results show the robust capability of the proposed algorithm to classify the PQ events

---

Development of an autonomous weeding machine requires a vision system capable of detecting and locating the position of the crop. It is important for the vision system to be able to recognize the accurate position of the crop stem to be protected during weeding. Several shape features of corn plants and common weed species in the location were extracted by means of morphological operations. Effective features in the classification of corn and weeds were analyzed using stepwise discriminant analysis. Among the seven features used in the analysis, four were sufficient to classify the two target groups of weeds and corn. These shape features were fed to artificial neural networks to discriminate between the weeds and the main crop. 180 images consisting of corn plants and four species of common weeds were collected from normal conditions of the field. Results showed that this technique was able to distinguish corn plants with an accuracy of 100% while at most 4% of the weeds were incorrectly classified as corn. In the final stage, the position of the main crop was also approximated and its accuracy was measured with respect to the real position of the crop. The position of the crop is necessary for the weeding machine to root up all of the plants except the main crop. It was concluded that the high accuracy of this method is due to the significant difference between corn and weeds in the critical period of weeding in the region.

---

Identification and classification of signals which are heard by underwater microphones (hydrophones) can be used extensively in harbor traffic management, especially in economical harbors. However, automatic identification and classification of acoustic signals which are received by passive sonar system is a challenging problem, because of variation in temporal and frequency characteristics of signals (even they are received from a same source). In this paper, a novel method for classification of acoustic signals is presented, based on DWT as preprocessing, a diverse range of feature extraction methods (principal component analysis and its variations (6 methods) and discriminant analysis and its variations (3 methods)), and 4 ensemble learning methods with 3 classifiers (multilayer perceptron (MLP), probabilistic neural network (PNN) and support vector machine (SVM)). Performing a diverse range of performance tests, the performances of different methods are assessed and the best ones are chosen for the proposed method. The proposed method is used to extract features and classify acoustic signals of 8 ships. Using the proposed method, some real signals and their noisy version are classified. The accuracy of the proposed method in classification of test signals with Gaussian white noise with -5, -10 and -15 signal-to-noise ratio is obtained as 99.83%, 97.06% and 83.56%, respectively.

---

In the present article, an improved Learning Classifier Systems (LCS) is proposed to control the balance of a moving unmanned bicycle. Significant characteristics of learning classifier systems is that they can learn through a set of system actions in the real world (similar to intelligent creatures) while no dynamic model of the system is needed. Contrary to studies reported in the literature where action domain of the controller is discrete and accordingly such controller cannot be used in real world applications, in the present study efficacy of the classifier system is enhanced by definition of continuous domain for the outputs, and then is used to control the balance of unmanned bicycle. A scheme based upon fuzzy membership functions is proposed which makes it possible for the domain of actions to be continuous. The proposed LCS features a dynamic reward assignment mechanism which is invented to cope with the bicycle’s delayed response due to its mass inertias. This allows the rapid calculation of the reward and hence enables the controller to be used in such real time applications as the balance control of unmanned vehicles. A standard 2 degree of freedom (2-DOF) bicycle model is employed to demonstrate the efficiency of the enhanced LCS. Simulation results show that the proposed classifier system outperforms traditional classifier system as well as some of the more common balance-control strategies reported in the literature.

---

During the past few years, the number of malware designed for Android devices has increased dramatically. To confront with Android malware, some anomaly detection techniques have been proposed that are able to detect zero-day malware, but they often produce many false alarms that make them impractical for real-world use. In this paper, we address this problem by presenting DroidNMD, an ensemble-based anomaly detection technique that focuses on the network behavior of Android applications in order to detect Android malware. DroidNMD constructs an ensemble classifier consisting of multiple heterogeneous one-class classifiers and uses an ordered weighted averaging (OWA) operator to aggregate the outputs of the one-class classifiers. Our work is motivated by the observation that combining multiple one-class classifiers often produces higher overall classification accuracy than any individual one-class classifier. We demonstrate the effectiveness of DroidNMD using a real dataset of Android benign applications and malware samples. The results of our experiments show that DroidNMD can detect Android malware with a high detection rate and a relatively low false alarm rate.