In Software Defined Networking (SDN) the data plane is separated from the controller plane to achieve better functionality than the traditional networking. Although this approach poses a lot of security vulnerabilities due to its centralized approach. One significant issue is compromised SDN switches because the switches are dumb in SDN architecture and in absence of any intelligence it can be a easy target to the attackers. If one or more switches are attacked and compromised by the attackers, then the whole network might be down or defunct. Therefore, in this work we have devised a strategy to successfully detect the compromised SDN switches, isolate them and then reconstruct the whole network flow again by bypassing the compromised switches. In our proposed approach of detection, we have used two controllers, one as primary and another as secondary which is used to run and validate our algorithm in the detection process. Flow reconstruction is the next job of the secondary controller which after execution is conveyed to the primary controller. A two-controller strategy has been used to balance the additional load of detection and reconstruction activity from the master controller and thus achieved a balanced outcome in terms of running time and CPU utilization. All the propositions are validated by experimental analysis of the results and compared with existing state of the art to satisfy our claim.

The modern world looks for employable graduates from the outcome based educational institutions. Outcome based education (OBE), creates competitive graduates who can reasonable in world wide. Due to this, now a day’s many educational institutions are starts to implement OBE instead of traditional educational system. OBE has gained much impetus in the education system. With no specific methods of teaching or performance assessment, the outcomes are built on scalability, accuracy, and real-time data. Studies say that OBE increases student centric learning instead of teacher centric. This helps the institutions in accreditations like national board of accreditation (NBA), national assessment and accreditation council (NAAC) etc. Recently some countries are accepted graduates for employment only from the NBA accredited programs. Course outcomes (CO’s), program outcomes (PO’s) and program specific outcomes (PSO’s) are the key terms in OBE. This article provides the course plan, attainment calculation of CO’s, Po’s and PSO’s. Here, author’s considered digital circuits and systems (DCS) as a sample course from undergraduate Electronics and Communication Engineering program. The DCS course considered here have 5 CO’s in apply level (K3). In order to calculate the attainment of CO’s, PO’s and PSO’s, first the mapping of CO’s with PO’s and PSO’s are presented. Then different direct assessment (Formative and Summative) tools like internal tests, assignment, quiz and end semester examinations and indirect assessment tools like course end survey are conducted to evaluate the attainment level of 5 course outcomes. Based on the performance of 128 students, attainment of CO’s are calculated  first. PO’s and PSO’s attainment are calculated from the CO’s attainment. From the calculated attainment values suggestions/proposals are made for the upcoming semester.

In this study, we investigate the effectiveness of deep learning models with thermal images for gender categorization. In order to explore the possibilities of thermal imaging as a tool for gender identification, the study focuses on two sophisticated convolutional neural network (CNN) architectures: InceptionV3 and AlexNet. Thermal imaging is a powerful substitute for traditional visual data because it provides distinct physiological insights.A collection of thermal imaging datasets was assembled, methodically preprocessed, and divided into training and testing sets. For this comparison analysis, two well-known CNNs AlexNet, a fundamental model recognised for its straightforward yet efficient design, and InceptionV3, a complex model acclaimed for its inception modules were chosen. The training subset was used to carefully refine both models so they could accurately capture the subtleties of thermal-based gender traits.Accuracy was the main criterion used to assess the performance of the revised models on the testing subset. According to our results, InceptionV3 performs noticeably better than AlexNet, with an accuracy of 92.3% as opposed to 82.6% for AlexNet. This disparity in performance demonstrates how much better InceptionV3 is at identifying and deciphering minute thermal patterns and physiological indicators that are essential for precise gender categorization. This study highlights how sophisticated CNN architectures may improve gender categorization using thermal images, both in terms of accuracy and dependability. We provide a path for future research to investigate more intricate and integrated strategies, like multi-modal fusion and sophisticated feature extraction techniques, to further enhance the resilience of thermal-based gender classification systems by proving the efficacy of InceptionV3 over a more conventional model like AlexNet.

Cyclones, with their high-speed winds and enormous quantities of rainfall, represent severe threats to global coastal regions. The ability to quickly and accurately identify cyclonic cloud formations is critical for the effective deployment of disaster preparedness measures. Our study focuses on a unique technique for precise delineation of cyclonic cloud regions in satellite imagery, concentrating on images from the Indian weather satellite INSAT-3D. This novel approach manages to achieve considerable improvements in cyclone monitoring by leveraging the image capture capabilities of INSAT-3D. It introduces a refined image processing continuum that extracts cloud attributes from infrared imaging in a comprehensive manner. This includes transformations and normalization techniques, further augmenting the pursuit of accuracy. A key feature of the study's methodology is the use of an adaptive threshold to correct complications related to luminosity and contrast; this enhances the detection accuracy of the cyclonic cloud formations substantially. The study further improves the preciseness of cloud detection by employing a modified contour detection algorithm that operates based on predefined criteria. The methodology has been designed to be both flexible and adaptable, making it highly effective while dealing with a wide array of environmental conditions. The utilization of INSAT-3D satellite images maximizes the performing capability of the technique in various situational contexts. 

Recent advancements in pest classification using deep learning models have shown promising results in various agricultural contexts. The VGG16 model, known for its robust performance in image classification, has been applied to the task of classifying pests in oil palm plants. This study aims to evaluate the effectiveness of the VGG16 model in identifying pests on oil palm, comparing the performance of default settings with models fine-tuned using grid search and random search techniques. We employed a quantitative approach, training the VGG16 model with three different configurations: default, fine-tuned with grid search, and fine-tuned with random search. Evaluation metrics including precision, recall, F1-Score, and overall accuracy were used to assess model performance across different pest categories: Metisa plana, Setora nitens, and Setothosea asigna. The default VGG16 model achieved precision, recall, and F1-Score values around 90% for Metisa plana, Setora nitens, and Setothosea asigna, with an overall accuracy of 91.00%. Fine-tuning with grid search improved these metrics, with precision, recall, and F1-Score reaching approximately 93.88%, 92%, and 92.93% respectively, and an overall accuracy of 93%. The random search fine-tuning resulted in even higher performance, with precision of about 95.92%, recall of 94%, and F1-Score of 94.95% for Metisa plana, and overall accuracy of 94.67%. The VGG16 model demonstrated strong performance in pest classification on oil palm, with significant improvements achieved through fine-tuning techniques. The study confirms that grid search and random search fine-tuning can substantially enhance model accuracy and efficacy. Future research should focus on expanding the dataset to include more diverse pest species, incorporating attention mechanisms, and leveraging automated control technologies like drones and the Internet of Things (IoT) to further improve pest management practices.

Sentiment analysis on Twitter provides organizations and persons with quick and effective instrument to observe the public's perceptions of them and their competition. A modest number of assessment datasets have been produced in recent years to check the efficiency of sentiment analysis algorithms on Twitter. Researchers offer a review of eight publicly accessible as well as manually annotated assessment datasets for analyzing Twitter sentiment in this research. As a result of this evaluation, we demonstrate that is a widespread weakness of many when using these datasets performing at sentiment analysis the objective (entity) level is indeed the absence of different sentiment classifications across tweets as well as the objects contained in them.[1], As an example all of that "I love my iPhone but I despise my iPad." Could be marked with a made-by-mixing classify however the object iPhone contained within this Twitter post should be annotated with just a label with an optimism. To get around this restriction and enhance existing assessment We have datasets that provide STS-Gold a novel assessment of datasets in which tweets or objects (entities) remain tagged separately hence might show alternative opinion labels. Though research furthermore compares the various datasets on multiple characteristics such as an entire quantity of posts as well as vocabulary size and sparsity.[2] In addition, look at pair by pair relationships between these variables and how they relate to sentiment classifier performance on various data. In this study we used five different classifiers and compared them and, in our experiment, we found that the bagging ensemble classifier performed best among them and have an accuracy level of 94.2% for the GASP dataset and 91.3% for the STS-Gold dataset.

High blood pressure (BP) monitoring Blood pressure (BP) is one of the common cardiovascular diseases and therefore the early high blood pressure (hypertension) detection, management, and prevention are mandatory. One promising method of continuous, non-invasive blood pressure estimation is photoplethysmography (PPG). In this study, a novel method was proposed to introduce the AlexNet framework into the time-frequency domain for classification of BP levels based on PPG signals. The study was conducted using the publicly available Figshare dataset which offers PPG signals, and the blood pressure labels against them. Data balancing techniques were used to alleviate class imbalances. Preprocessing and Feature Extraction of PPG Signals. The PPG signals were preprocessed with noise filtering and signals were then transformed from 1D-time to image to facilitate robust feature extraction. The proposed classification model, based on AlexNet showed the best result, with 98.89% accuracy, recall, and precision, and 99.44% specificity. This model outperformed alternative models (VGG16, DenseNet, ResNet50, GoogleNet) for classifying BP levels into the JNC 7 report standard categories normotension, prehypertension and hypertension. This study has two primary contributions. Initially, it demonstrates the efficacy of AlexNet model to extract meaningful features from PPG signals by its hierarchical convolutional and max-pooling layers thereby enabling accurate classification of BP levels. This study underscores the potential of deep learning and PPG signals for developing a highly accurate and truly non-invasive BP monitoring system. In the second aspect, the study offers a systematic assessment and comparison of the proposed over other well-known deep-learning networks, presenting the effectiveness of the AlexNet-based one. These results are of critical importance in the development of novel non-invasive BP monitoring modalities and optimization of cardiovascular health managements and personalized health cares.

In the software development industry, ensuring software quality holds immense significance due to its direct influence on user satisfaction, system reliability, and overall end-users. Traditionally, the development process involved identifying and rectifying defects after the implementation phase, which could be time-consuming and costly. Determining software development methodologies, with a specific emphasis on Test-Driven Development, aims to evaluate its effectiveness in improving software quality. The study employs a mixed-methods approach, combining quantitative surveys and qualitative interviews to comprehensively investigate the impact of Test-Driven Development on various facets of software quality. The survey findings unveil that Test-Driven Development offers substantial benefits in terms of early defect detection, leading to reduced costs and effort in rectifying issues during the development process. Moreover, Test-Driven Development encourages improved code design and maintainability, fostering the creation of modular and loosely coupled code structures. These results underscore the pivotal role of Test-Driven Development in elevating code quality and maintainability. Comparative analysis with traditional development methodologies highlights Test-Driven Development's effectiveness in enhancing software quality, as rated highly by respondents. Furthermore, it clarifies Test-Driven Development's positive impact on user satisfaction, overall product quality, and code maintainability. Challenges related to Test-Driven Development adoption are identified, such as the initial time investment in writing tests and difficulties adapting to changing requirements. Strategies to mitigate these challenges are proposed, contributing to the practical application of Test-Driven Development. Offers valuable insights into the efficacy of Test-Driven Development in enhancing software quality. It not only highlights the benefits of Test-Driven Development but also provides a framework for addressing challenges and optimizing its utilization. This knowledge is invaluable for software development teams, project managers, and quality assurance professionals, facilitating informed decisions regarding adopting and implementing Test-Driven Development as a quality assurance technique in software development.

The rise of online education has changed the way students usually learn by making educational materials easier to get to and creating a global learning community. While online education offers numerous benefits, it is also crucial to acknowledge its certain drawbacks, such as the potential reduction in interaction between students and teachers, which might increase signs of isolation among students and impede opportunities for collaborative learning. Therefore, Student Evaluations of Teaching (SET) play a critical role in identifying areas for improvement from the students' standpoint, thereby promoting constructive communication between students and teachers. This research conducts a comparison among the traditional Educational Data Mining (EDM) techniques to find out the best-performing classifier for analyzing student evaluations of teaching online. It is accomplished by first extracting the dataset from the student evaluations of teaching at X-University and then applying six different classifiers to the dataset that were extracted. The results demonstrated that Logistic Regression, Naive Bayes, and K-Nearest Neighbors (KNN) exhibited a notably high level of accuracy compared to other classification techniques. The findings of this research will provide guidance for future researchers in applying a wider range of classification techniques to extensive datasets and in implementing the necessary adjustments to achieve superior results.

Microstrip Patch Antennas (MPAs) play a critical role in modern wireless communication systems due to their compact size, easy integration, and capability to ensure reliable communication across wide frequency ranges. This paper introduces enhanced designs of rectangular MPAs aimed at overcoming the narrow bandwidth limitation commonly found in traditional designs. Three innovative configurations are proposed: one featuring a simple rectangular slot on the ground plane, another integrating polygonal Defected Ground Structures (DGS), and a third utilizing rectangular DGS. These antennas are optimized at a frequency of 4 GHz using High Frequency Structural Simulator (HFSS) software to significantly improve antenna performance. The MPA without DGS showed a return loss of -21.124 dB at a resonant frequency of 4 GHz, with a Voltage Standing Wave Ratio(VSWR) of 4.8038 and a gain of 3.88 dBi. In contrast, the MPA with Polygonal DGS exhibited significant improvements, achieving a return loss of -26.87 dB at a resonant frequency of 4.1 GHz, along with a VSWR of 1.3721 and a gain of 4.38 dBi. Similarly, the MPA with Rectangular DGS demonstrated superior characteristics, with a return loss of -27.08 dB, resonance at 3.825 GHz, a VSWR of 1.4399, and a gain of 4.00 dBi. These results underscore the effectiveness of DGS in broadening the bandwidth and improving the performance of MPAs for applications below 6 GHz, making them highly suitable for next-generation wireless communication systems.