Computational Mathematics articles list

This article addresses the implementation of the new generalized (G'∕G)-expansion method to the Caudrey–Dodd–Gibbon (CDG) equation and the Lax equation which are associated with the fifth-order KdV (fKdV) equation. The method works well to derive a variety of standard and functional closed-form wave solutions with distinct physical structures, such as, soliton, kink, periodic soliton, and bell-shaped soliton solutions. The solutions obtained using this method are useful and adequate than other methods. In order to understand the physical aspects and importance of the method, the attained solutions have been simulated graphically. The extracted results definitely establish that the new generalized(G'∕G)-expansion method is an effective mathematical tool to work out new solutions to different types of local nonlinear evolution equations emerging in applied science and engineering, but this method is not effective in solving nonlocal equations.

Md Khorshed Alam

In this article, we form the exact wave solutions of the Jimbo-Miwa equation and the Calogero-Bogoyavlenskii-Schiff equation by applying the new generalized (G'/G)-expansion method. We explained the new generalized (G'/G)-expansion method to look for more general traveling wave solutions of the above mentioned equations. The traveling wave solutions attained by this method are in terms of hyperbolic, trigonometric and rational functions. The graphical representation of the obtained solutions is kink soliton, singular kink soliton, singular soliton and singular periodic solution. This method is very significant for extracting exact solutions of NLEEs which habitually occur in mathematical physics, engineering sciences and applied mathematics.

Md Khorshed Alam

In this article, we establish the exact wave solutions of the Boussinesq equation and the (2 + 1)-dimensional extended shallow water wave equation by applying the new generalized (G'/G)-expansion method. When the condition of the fluid is such that the horizontal length scale is much greater than the vertical length scale, the shallow water equations are mostly suitable. In Ocean engineering, Boussinesq-type equations are commonly used in computer simulations for the model of water waves in shallow seas and harbors. We explained the new generalized (G'/G)-expansion method to seek further general traveling wave solutions of the above mentioned equations. The traveling wave solutions attained by this method are exposed in terms of hyperbolic, trigonometric and rational functions. The shape of the obtained solutions are bell shaped soliton, kink soliton, singular kink soliton, singular soliton, singular periodic solution and compaction. This method is very influential mathematical tool for extracting exact solutions of NLEEs which frequently arise in mathematical physics, engineering sciences and many scientific real world application fields.

Md Khorshed Alam

The transportation problem is widely applied in the real world. This problem aims to minimize the total shipment cost from a number of sources to a number of destinations. This paper presents a new method named Dhouib-Matrix-TP1, which generates an initial basic feasible solution based on the standard deviation metric with a very reduced number of simple iterations. A comparative study is carried out in order to verify the performance of the proposed Dhouib-Matrix-TP1 heuristic.

Souhail Dhouib

In this article, we study an Inventory Routing Problem with deterministic customer demand in a two-tier supply chain. The supply chain network consists of a supplier using a single vehicle with a given capacity to deliver a single product type to multiple customers. We are interested in population-based algorithms to solve our problem. A Memetic Algorithm (MA) is developed based on the Genetic Algorithm (GA) and Variable Neighborhood Search methods. The proposed meta-heuristics are tested on small and large reference benchmarks. The results of the MA are compared to those of the classical GA and to the optimal solutions in the literature. The comparison shows the efficiency of using MA and its ability to generate high quality solutions in a reasonable computation time.

Mohamed Salim Amri Sakhri

This mathematical model forms machine cells, optimises the costs of unassigned machines and components, and designs the shop floor cell layout to have minimal movement of materials. The complete similarity measure algorithm forms machine cells and part families in a refined form. Later, exceptional elements are eliminated in the optimisation model by using machine duplication and sub-contracting of parts. Then the shop floor layout is designed to have optimised material movements between and within cells. An evaluation of the cell formation algorithm’ performance is done on the benchmark problems of various batch sizes to reveal the process’s capability compared with other similar methods. The data of machining times are acquired and tabulated in a part incidence matrix, which is used as input for the algorithm. The results from the linear programming optimisation model are that costs are saved, machines are duplicated, parts are sub-contracted, and there are inter- and intra-cellular movements. Finally, the output of the inbound facility design is the floor layout, which has machine cell clusters within the optimised floor area.

S RAMESH

This proposed work is used to optimize the costs of exceptional elements of machine cells for a variety of components in changing environments to have reduced material movements in cell layout. The exceptional ele¬ments are eliminated in the optimization model by doing machine duplication and part subcontract. Then the shop floor layout is designed to have optimized material movements between cells and within a cell. The result of a linear programming optimization model is cost savings, machines duplicated, parts subcon¬tracted, inter intracellular movements. Finally, the output of the inbound facility design is the floor layout which has machine cell clusters with optimized floor areas. The optimization model is provided with budg¬etary constraints for duplication and the economic tradeoff between machine duplication and part subcontract. Cell layout is prepared to reveal the saving in floor area and material movement lengths than in process layout with the help of distance matrix and dimensions of cells.

S RAMESH

The high density of H-CRAN associated with frequent UE handover may degrade the throughput. The infrastructure equipment like RRHs and BBUs consumes more energy to reduce UE energy consumptions. In this paper, we propose a utility-based joint power control and resource allocation (UJPCRA) algorithm for heterogeneous cloud radio access network (H-CRAN). In this framework, the power consumption of baseband units (BBUs), remote radio heads (RRHs), and macrocell base station (MBS) are estimated by predicting their dynamic loads. The data rate achievable for UE associated with each RRH and MBS on resource block RBk is then estimated. The user wishing to connect to a RRH or MBS then checks the corresponding utility with minimum expected energy consumption and the maximum expected data rate. If any UE with high priority traffic connected to MBS could not achieve its desired data rate requirements, then it can cooperatively seek the assistance of any RRH for assigning the balance RBs. The throughput may be enhanced by the high density of H-CRAN and frequent UE handover. Inter- and intracell interference causes the H-CRAN macrocells’ improved data rate to diminish. To lower UE energy consumption, infrastructure devices like RRHs and BBUs need more energy. As a result, there is a trade-off between operators and UE energy conservation. It is possible to determine the power consumption of BBUs, RRHs, and MBS using predictions of their dynamic loads. The UE may then forecast the data rate for each RRH and MBS on the resource block. When a user wishes to connect to an RRH or MBS, they look at the utility with the highest expected data rate and the least predicted energy usage first. A UE with high priority traffic connected to the MBS can cooperatively ask any RRH for assistance in allocating the remaining RBs if it is unable to achieve its intended data rate needs. Experimental results have shown that the proposed JRAUA algorithm achieves higher throughput, resource utilization, and energy efficiency with reduced packet loss ratio, when compared to the existing techniques.

Dr H Shaheen

Alzheimer Disease is a chronic neurological brain disease. Early diagnosis of Alzheimer illness may the prevent the occurrence of memory cellular injury. Neuropsychological tests are commonly used to diagnose Alzheimer’s disease. The above technique, has a limited specificity and sensitivity. This article suggests solutions to this issue an early diagnosis model of Alzheimer’s disease based on a hybrid meta-heuristic with a multi-feed-forward neural network. The proposed Alzheimer’s disease detection model includes four major phases: pre-processing, feature extraction, feature selection and classification (disease detection). Initially, the collected raw data is pre-processed using the SPMN12 package of MATLAB. Then, from the pre-processed data, the statistical features (mean, median and standard deviation) and DWT are extracted. Then, from the extracted features, the optimal features are selected using the new Hybrid Sine cosine firefly (HSCAFA). This HSCAFA is a conceptual improvement of standard since cosine optimization and firefly optimization algorithm, respectively. Finally, the disease detection is accomplished via the new regression- based multi-faith neighbors’ network (MFNN). The final detected outcome is acquired from regression-based MFNN. The proposed methodology is performed on the PYTHON platform and the performances are evaluated by the matrices such as precision, recall, and accuracy.

Dr. Rajasekhar Butta

Smartphones are constantly changing in today's world, and as a result, security has become a major concern. Security is a vital aspect of human life, and in a world where security is lacking, it becomes a concern for mobile users' safety. Malware is one of the most serious security risks to smartphones. Mobile malware attacks are becoming more sophisticated and widespread. Malware authors consider the open-source Android platform to be their preferred target as it came to lead the market. State-of-the-art mobile malware detection solutions in the literature use a variety of metrics and models, making cross-comparison difficult. In this paper various existing methods are compared and a significant effort is made to briefly address android malwares, various methods for detecting android malwares and to give a clear image of the progress of the android platform and various malware detection classifiers.

Jebin Bose S

Social engineering is a method of information security that allows for system or network access. When victims are unaware of techniques, models, and frameworks to prevent them, social engineering attacks happen. In order to stop social engineering attacks, the current research describes user studies, constructs, assessment, concepts, frameworks, models, and techniques. Sadly, there isn't any specific prior research on mitigating social engineering attacks that thoroughly and efficiently analyzes it. Health campaigns, human security sensor frameworks, user-centric frameworks, and user vulnerability models are examples of current social engineering attack prevention techniques, models, and frameworks. Guidance is required to examine cybersecurity as super-recognizers, possibly acting as police for a secure system, for the human as a security sensor architecture. This research aims to critically and systematically analyze earlier material on social engineering attack prevention strategies, models, and frameworks. Based on Bryman & Bell's methodology for conducting literature reviews, we carried out a systematic review of the available research. Using a protocol, we discovered a novel strategy to stop social engineering assaults in addition to approaches, frameworks, models, and assessments, based on our review. We discovered that the protocol can successfully stop social engineering assaults, including health campaigns, the susceptibility of social engineering victims, and co-utile protocol, which can control information sharing on a social network. This comprehensive evaluation of the research is what we're presenting in order to suggest safeguards against social engineering assaults.

Mahesh Donga

Along with computer technology, the demand of digital image processing is too high and it is used massively in every sector like organization, business, medical etc. Image segmentation enables us to analyze any given image in order to extract information from the image. There are numerous algorithm and techniques have been industrialized in the field of image segmentation. Segmentation has become one of the prominent tasks in machine vision. Machine vision enables the machine to vision the real world problems like human does and also act accordingly to solve the problem so it is utmost important to come up with the techniques that can be applied for the image segmentations. Invention of modern segmentation methods like instance, semantic and panoptic segmentation have advances the concept of machine vision. This paper focuses on the various methods of image segmentation along with its advantages and disadvantages.

Manoj Kumar Pandey

Digital data is growing enormously as the year passes and therefore there is a need of mechanism to protect the digital contents. Image watermarking is one of the important tools for the human to provide copyright protection and authorship. For achieving the ideal balance between imperceptibility and robustness, a robust blind color image watermarking employing deep artificial neural networks (DANN), LWT and the YIQ color model has been presented. In the suggested watermarking method, an original 512-bit watermark is applied for testing and a randomly generated watermark of the same length is used for training. PCA is used to extract 10 statistical features with significant values out of 18 statistical features, and binary classification is used to extract watermarks here. For the four images Lena, Peppers, Mandril, and Jet, it displays an average imperceptibility of 52.48 dB. For the threshold value of 0.3, it does an excellent job of achieving good balance between robustness and imperceptibility. Except for the gaussian noise, rotation, and average filtering attacks, it also demonstrates good robustness against common image attacks. The results of the experiment demonstrate that the suggested watermarking method outperforms competing methods.

Manoj Kumar Pandey

Abstract- The role of support vector machine in the evaluation of English teaching effect is very important, but there is a problem of inaccurate evaluation of results. The traditional English teaching mode cannot solve the accuracy and efficiency of the effect evaluation of students' English teaching and cannot meet the requirements of English teaching effect evaluation. Therefore, this paper proposes a neural network algorithm to innovate and optimize the analysis of support vector machines. Firstly, the relevant theories are used to construct a multi-index English teaching effect evaluation system with teachers and students as the main body, and the indicators are divided according to the data requirements of English teaching effect evaluation indicators to reduce the support vector machine in the interfering factor. Then, the neural network algorithm is used to solve the optimal solution of kernel function parameters and regularization parameters of the support vector machine, and the support vector machine scheme is formed, and the support vector machine results are carried out Comprehensive analysis. MATLAB simulation shows that the evaluation accuracy of the English teaching effect of the neural network algorithm and the support vector machine under certain evaluation criteria Optimal, short evaluation time.

LOKESH N S

In this paper, we propose a deep learning approach for visual recognition of bird species in noisy environments. Bird species recognition has been a challenging task due to the high variation in bird appearances and the presence of noise and clutter in natural environments. Our approach utilizes a deep convolutional neural network (CNN) to learn discriminative features from bird images and classify them into different species. We also incorporate data augmentation techniques to increase the diversity of the training data and improve the robustness of the model. To address the issue of noisy environments, we introduce a novel noise-robust loss function that penalizes the model for incorrect predictions caused by noise. We evaluate our approach on a dataset of bird images collected from diverse environments and compare it with state-of-the-art methods. Our results demonstrate that our approach achieves superior performance in both clean and noisy environments, highlighting the effectiveness of our noise-robust loss function. Our approach has the potential to be applied in real world scenarios for bird species recognition and conservation.

PK Dutta