A new proactive feature selection model based on the enhanced optimization algorithms to detect DRDoS attacks

Cyberattacks have grown steadily over the last few years. The distributed reflection denial of service (DRDoS) attack has been rising, a new variant of distributed denial of service (DDoS) attack. DRDoS attacks are more difficult to mitigate due to the dynamics and the attack strategy of this type of attack. The number of features influences the performance of the intrusion detection system by investigating the behavior of traffic. Therefore, the feature selection model improves the accuracy of the detection mechanism also reduces the time of detection by reducing the number of features. The proposed model aims to detect DRDoS attacks based on the feature selection model, and this model is called a proactive feature selection model proactive feature selection (PFS). This model uses a nature-inspired optimization algorithm for the feature subset selection. Three machine learning algorithms, i.e., k-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM), were evaluated as the potential classifier for evaluating the selected features. We have used the CICDDoS2019 dataset for evaluation purposes. The performance of each classifier is compared to previous models. The results indicate that the suggested model works better than the current approaches providing a higher detection rate (DR), a low false-positive rate (FPR), <span>and increased accuracy detection (DA).</span> The PFS model shows better accuracy to detect DRDoS attacks with 89.59%.

Smallholders Beef Cattle Commercialization in Southwest Ethiopia

In this study, the beef cattle markets in Southwest Ethiopia are analyzed based on a survey of 172 producers. The first part emphasized the characterization and commercialization of the beef cattle market in the study area. The second part is dedicated to identifying the factors affecting households’ participation in the beef cattle market using the Heckman two-step selection model. In the findings, the beef cattle market is characterized by the dominance of few traders, asymmetric information, lack of contract enforcement, lack of transparency among market actors, and poorly developed market infrastructure. There is very low net commercial off-take rate of cattle for smallholder farmers in the study area. The result from the Heckman two-step selection model revealed that having positive stock of cattle, better access to extension service & feed, and a better level of literacy enhanced market participation and sales volume. On the contrary, market participation and sales volume were negatively affected by cattle keeper’s age, non-livestock income, and poor road and health infrastructure. The study suggested that improving the market and health infrastructure, providing capacity building for producers, and improving access to feed could enhance the intensity of smallholder beef cattle market participation.

Corn Leaf Disease Detection with Pertinent Feature Selection Model Using Machine Learning Technique with Efficient Spot Tagging Model

Crop diseases constitute a substantial threat to food safety but, due to the lack of a critical basis, their rapid identification in many parts of the world is challenging. The development of accurate techniques in the field of image categorization based on leaves produced excellent results. Plant phenotyping for plant growth monitoring is an important aspect of plant characterization. Early detection of leaf diseases is crucial for efficient crop output in agriculture. Pests and diseases cause crop harm or destruction of a section of the plant, leading to lower food productivity. In addition, in a number of less-developed countries, awareness of pesticide management and control, as well as diseases, is limited. Some of the main reasons for decreasing food production are toxic diseases, poor disease control and extreme climate changes. The quality of farm crops may be influenced by bacterial spot, late blight, septoria and curved yellow leaf diseases. Because of automatic leaf disease classification systems, action is easy after leaf disease signs are detected. Applying image processing and machine learning methodologies, this research offers an efficient Spot Tagging Leaf Disease Detection with Pertinent Feature Selection Model using Machine Learning Technique (SPLDPFS-MLT). Different diseases deplete chlorophyll in leaves generating dark patches on the surface of the leaf. Machine learning algorithms can be used to identify image pre-processing, image segmentation, feature extraction and classification. Compared with traditional models, the proposed model shows that the model performance is better than those existing.

DESIGNING GIS-BASED SITE SELECTION MODEL FOR URBAN INVESTMENT PLANNING IN SMART CITIES WITH THE CASE OF ELECTRIC VEHICLE CHARGING STATIONS

It is of great importance that different sectoral investments such as energy, technology, education, logistics, health, industry, transportation, construction, tourism, which will be realized in globalizing and crowded cities, are made in the most suitable city areas. In order to obtain the maximum efficiency from the unit city area for any sectoral investment activity and to develop more planned and liveable cities, many decision parameters in investment management should be handled rationally by integrated a geographical perspective. In this study, designing GIS-based site selection models was examined for effective decision-making in the investment planning process for smart cities. In this context, different sectoral investment applications examining implementation requirements were determined for smart cities. Electric vehicle charging stations (EVCS) site selection application was determined as a case study, to design a GIS-based integrated site selection model for investment planning in smart city concept. Data preparation and analysis models were designed for determining the most suitable EVCSs location. EVSC site selection affecting criteria and criteria weights (by MCDA techniques) were researched in the literature. For this purpose, 15 criteria defined by three main criteria groups, namely Environmental/ Geographical, Economic Criteria, and Urbanity Criteria were determined. Designed models were performed analysing EVCSs suitability map in Pendik district of Istanbul. Normalized raster maps related to 15 criteria and EVCSs suitability map were produced with five suitability degrees for the Pendik district. Also, by designing the models, an integrated and planned investment mechanism can be developed for the impressive and efficient use of urban resources in smart city investments.

Solution of Mixed-Integer Optimization Problems in Bioinformatics with Differential Evolution Method

The solution of the so-called mixed-integer optimization problem is an important challenge for modern life sciences. A wide range of methods has been developed for its solution, including metaheuristics approaches. Here, a modification is proposed of the differential evolution entirely parallel (DEEP) method introduced recently that was successfully applied to mixed-integer optimization problems. The triangulation recombination rule was implemented and the recombination coefficients were included in the evolution process in order to increase the robustness of the optimization. The deduplication step included in the procedure ensures the uniqueness of individual integer-valued parameters in the solution vectors. The developed algorithms were implemented in the DEEP software package and applied to three bioinformatic problems. The application of the method to the optimization of predictors set in the genomic selection model in wheat resulted in dimensionality reduction such that the phenotype can be predicted with acceptable accuracy using a selected subset of SNP markers. The method was also successfully used to optimize the training set of samples for such a genomic selection model. According to the obtained results, the developed algorithm was capable of constructing a non-linear phenomenological regression model of gene expression in developing a Drosophila eye with almost the same average accuracy but significantly less standard deviation than the linear models obtained earlier.