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The recent advancements in big data and natural language processing (NLP) have necessitated proficient text mining (TM) schemes that can interpret and analyze voluminous textual data. Text summarization (TS) acts as an essential pillar within recommendation engines. Despite the prevalent use of abstractive techniques in TS, an anticipated shift towards a graph-based extractive TS (ETS) scheme is becoming apparent. The models, although simpler and less resource-intensive, are key in assessing reviews and feedback on products or services. Nonetheless, current methodologies have not fully resolved concerns surrounding complexity, adaptability, and computational demands. Thus, we propose our scheme, GETS, utilizing a graph-based model to forge connections among words and sentences through statistical procedures. The structure encompasses a post-processing stage that includes graph-based sentence clustering. Employing the Apache Spark framework, the scheme is designed for parallel execution, making it adaptable to real-world applications. For evaluation, we selected 500 documents from the WikiHow and Opinosis datasets, categorized them into five classes, and applied the recall-oriented understudying gisting evaluation (ROUGE) parameters for comparison with measures ROUGE-1, 2, and L. The results include recall scores of 0.3942, 0.0952, and 0.3436 for ROUGE-1, 2, and L, respectively (when using the clustered approach). Through a juxtaposition with existing models such as BERTEXT (with 3-gram, 4-gram) and MATCHSUM, our scheme has demonstrated notable improvements, substantiating its applicability and effectiveness in real-world scenarios. [ABSTRACT FROM AUTHOR]

In recent years, both machine learning and computer vision have seen growth in the use of multi-label categorization. SMOTE is now being utilized in existing research for data balance, and SMOTE does not consider that nearby examples may be from different classes when producing synthetic samples. As a result, there can be more class overlap and more noise. To avoid this problem, this work presented an innovative technique called Adaptive Synthetic Data-Based Multi-label Classification (ASDMLC). Adaptive Synthetic (ADASYN) sampling is a sampling strategy for learning from unbalanced data sets. ADASYN weights minority class instances by learning difficulty. For hard-to-learn minority class cases, synthetic data are created. Their numerical variables are normalized with the help of the Min-Max technique to standardize the magnitude of each variable's impact on the outcomes. The values of the attribute in this work are changed to a new range, from 0 to 1, using the normalization approach. To raise the accuracy of multi-label classification, Velocity-Equalized Particle Swarm Optimization (VPSO) is utilized for feature selection. In the proposed approach, to overcome the premature convergence problem, standard PSO has been improved by equalizing the velocity with each dimension of the problem. To expose the inherent label dependencies, the multi-label classification ensemble of Adaptive Neuro-Fuzzy Inference System (ANFIS), Probabilistic Neural Network (PNN), and Clustering-Based Decision tree methods will be processed based on an averaging method. The following criteria, including precision, recall, accuracy, and error rate, are used to assess performance. The suggested model's multi-label classification accuracy is 90.88%, better than previous techniques, which is PCT, HOMER, and ML-Forest is 65.57%, 70.66%, and 82.29%, respectively. [ABSTRACT FROM AUTHOR]

In this study, a new switched-capacitor-based seven-level inverter topology with a photovoltaic system is presented. The proposed topology requires a smaller number of devices and has the ability to self-balance the voltage across the capacitor. The proposed topology configuration is simple and has the ability to extend to higher levels of voltage. This multilevel inverter topology is suitable for low- and medium-voltage applications with photovoltaic (PV) system integration. To improve the PV system efficiency as an input of a DC–DC boost converter, a Fuzzy logic-based maximum power point controller technique is used. A PV system with a DC–DC boost converter integrates with the proposed seven-level inverter topology. The anti-predatory particle swarm optimization (APSO) technique is used to solve the non-linear transduction equations of the seven-level PV switched-capacitor-based multilevel inverter (7L−PV−SCMLI) topology. The proposed APSO is described to minimize the harmonics in the multilevel inverter (MLIs), which is a complex optimization problem involving a non-linear transcendental equation. Furthermore, APSO can be applied in order to solve non-linear transcendental equations for all symmetrical and asymmetrical MLIs that have equal and non-equal DC sources. The APSO-based selected harmonic elimination (SHE) technique obtained the best switching angle value, and the optimized obtained switching angles reduced the total harmonic distortion (THD) of 7L−PV−SCMLI. [ABSTRACT FROM AUTHOR]

In this study, the load frequency control of a two-area thermal generation system based on renewable energy sources is considered. When solar generation is used in one of the control areas, the system becomes nonlinear and complicated. Zero deviations in the frequencies and the flow of power through the tie lines are achieved by considering load disturbances. A novel grey wolf optimizer, which is a metaheuristic algorithm motivated by grey wolves is utilized for tuning the controller gains. The proportional, integral, and derivative gains values are optimized for the two-area Solar integrated Thermal Plant (STP). As the load connected to the system varies continuously with time, random load variation is also applied to observe the effectiveness of the proposed optimization method. Sensitivity analyses have also been adopted with the deviation in the time constants of different systems. Inertia constant variations of both areas are considered from −25% to +25%, with or without STP. The proposed algorithm shows good dynamic performance as shown from the simulation results in terms of settling time, overshoot values, and undershoot values. The power in the tie line achieves zero deviation quite rapidly in solar-based cases compared to those without STP. [ABSTRACT FROM AUTHOR]

Phishing leverages people's tendency to share personal information online. Phishing attacks often begin with an email and can be used for a variety of purposes. The cybercriminal will employ social engineering techniques to get the target to click on the link in the phishing email, which will take them to the infected website. These attacks become more complex as hackers personalize their fraud and provide convincing messages. Phishing with a malicious URL is an advanced kind of cybercrime. It might be challenging even for cautious users to spot phishing URLs. The researchers displayed different techniques to address this challenge. Machine learning models improve detection by using URLs, web page content and external features. This article presents the findings of an experimental study that attempted to enhance the performance of machine learning models to obtain improved accuracy for the two phishing datasets that are used the most commonly. Three distinct types of tuning factors are utilized, including data balancing, hyper-parameter optimization and feature selection. The experiment utilizes the eight most prevalent machine learning methods and two distinct datasets obtained from online sources, such as the UCI repository and the Mendeley repository. The result demonstrates that data balance improves accuracy marginally, whereas hyperparameter adjustment and feature selection improve accuracy significantly. The performance of machine learning algorithms is improved by combining all fine-tuned factors, outperforming existing research works. The result shows that tuning factors enhance the efficiency of machine learning algorithms. For Dataset-1, Random Forest (RF) and Gradient Boosting (XGB) achieve accuracy rates of 97.44% and 97.47%, respectively. Gradient Boosting (GB) and Extreme Gradient Boosting (XGB) achieve accuracy values of 98.27% and 98.21%, respectively, for Dataset-2. [ABSTRACT FROM AUTHOR]

DNA (Deoxyribonucleic Acid) Cryptography has revolutionized information security by combining rigorous biological and mathematical concepts to encode original information in terms of a DNA sequence. Such schemes are crucially dependent on corresponding DNA-based cryptographic keys. However, owing to the redundancy or observable patterns, some of the keys are rendered weak as they are prone to intrusions. This paper proposes a Genetic Algorithm inspired method to strengthen weak keys obtained from Random DNA-based Key Generators instead of completely discarding them. Fitness functions and the application of genetic operators have been chosen and modified to suit DNA cryptography fundamentals in contrast to fitness functions for traditional cryptographic schemes. The crossover and mutation rates are reducing with each new population as more keys are passing fitness tests and need not be strengthened. Moreover, with the increasing size of the initial key population, the key space is getting highly exhaustive and less prone to Brute Force attacks. The paper demonstrates that out of an initial 25 × 25 population of DNA Keys, 14 keys are rendered weak. Complete results and calculations of how each weak key can be strengthened by generating 4 new populations are illustrated. The analysis of the proposed scheme for different initial populations shows that a maximum of 8 new populations has to be generated to strengthen all 500 weak keys of a 500 × 500 initial population. [ABSTRACT FROM AUTHOR]

In low voltage (LV) distribution systems, the problem of overvoltage is common during the lower load intervals. This problem arises because of the high value of R/X ration of these systems. Many techniques are available in literature to cope up with this problem at the converter level; mostly these methods control the reactive or active power of the photovoltaic (PV) systems. However, there are certain restrictions and complications with the reactive power control of PV systems. Most of the active power control methods have been implemented at the inverter stage of the PV system, resulting in implementation complexities and excessive oversizing of the converter. Therefore, in this paper, a simple, de-rating based voltage control algorithm is proposed to overcome the problem of overvoltage. So far de-rating technique has been used to enable frequency support functions in PVs; in a first of its kind, de-rating technique is used here to control the voltages in PV rich LV distribution systems. The entire control is implemented on the dc/dc converter stage of the PV system and the inverter stage is kept untouched. The effectiveness of the control is verified by simulating a sample PV-rich three bus LV distribution system on the MATLAB software. The proposed control avoids the overvoltage by approx. 700 V for the best-case scenario. [ABSTRACT FROM AUTHOR]

Recently, with the massive growth of IoT devices, the attack surfaces have also intensified. Thus, cybersecurity has become a critical component to protect organizational boundaries. In networks, Intrusion Detection Systems (IDSs) are employed to raise critical flags during network management. One aspect is malicious traffic identification, where zero-day attack detection is a critical problem of study. Current approaches are aligned towards deep learning (DL) methods for IDSs, but the success of the DL mechanism depends on the feature learning process, which is an open challenge. Thus, in this paper, the authors propose a technique which combines both CNN, and GRU, where different CNN-GRU combination sequences are presented to optimize the network parameters. In the simulation, the authors used the CICIDS-2017 benchmark dataset and used metrics such as precision, recall, False Positive Rate (FPR), True Positive Rate (TRP), and other aligned metrics. The results suggest a significant improvement, where many network attacks are detected with an accuracy of 98.73%, and an FPR rate of 0.075. We also performed a comparative analysis with other existing techniques, and the obtained results indicate the efficacy of the proposed IDS scheme in real cybersecurity setups.