To combat cyber threats in the smart grid, an intrusion detection system can be integrated into the advanced metering infrastructure. Anomaly-based intrusion detection can detect even the tiniest changes in the parameter under investigation, whereas signature-based intrusion detection only recognises known attacks. The growing usage of smart grids necessitates the classification, identification, and implementation of countermeasures to threats. At the absolute least, smart grids must be protected against cyberattacks; thus, the highest level of information security must be offered. As a result of digitisation and the usage of more smart applications, the research looked at a variety of attack types, smart grid assaults, and major cyber threats on the voltage regulation. Machine learning techniques that analyse data in real time and formulate patterns to recognise an attack and scan through huge data for anomalies can be implemented into the advanced metering infrastructure (AMI) for intrusion detection for anomaly-based intrusion detection. The comparative test study done for the research found that the proposed method, median absolute deviation for anomaly identification in smart metering datasets, produced the most accurate and precise differentiations with the highest accuracy and precision. The median absolute deviation (MAD) algorithm model is trained using test data, and raw predictions are made, before true data are used to derive final test result parameters, precision, recall, and F1 scores. The methodology of the entire study is discussed in this paper, as well as how the MAD algorithm is best suited for anomaly-based intrusion detection, as well as result comparisons of other machine learning algorithms.

To solve the difficulty in selecting the crossover probability and mutation probability in genetic algorithms, a fuzzy immune algorithm based on adaptive estimation of crossover probability and mutation probability in a fuzzy reasoning system is proposed, and it is used in the parameter optimization design of a two‐degree‐of‐freedom PID controller. According to the experiment and simulation results, classic genetic algorithm evolution tends to halt after 37 generations, with a fitness value of 7.135, whereas fuzzy genetic algorithm evolution tends to stop after 20 generations, with a fitness value of 7.486. The 2‐DOF PID controller that was created can give the system strong target value following and interference suppression features at the same time.

In the twenty first century, the education system is in transaction phase. The contents are taking shape into digital form even reading, study material/s and class rooms are marching towards digitization. Thereby, academic institutions and study contents are easy to access and copying also. Consequently, the plagiarism incidents occur more in the digital world. The same incidents were happened in non-digital era also but these were not traceable usually, unless/until any one may file the complaint against concerned. Therefore, with the availability of digital contents, the scholars/students are being trapped under plagiarism act easily and frequently. Presently, among Indian academic activities, the plagiarism is a well known and overwhelming issue and stood like a giant in front of the stakeholders as most of the institutions/universities are not having the proper guidelines and policy for the same so far. Even, our education system is bearing the consequences of academic misconduct. Thereon, students and research scholars are in a dilemma that how to tackle with the same as plagiarism is not limited up to undergraduates and undergraduate courses. To overcome from this dilemma and to save the students/scholars from accusations of plagiarism, the University Grants Commission (UGC) has taken the initiative to frame the policy on plagiarism and after framing the same, it is available on the website of the University Grants Commission (UGC). Any academic institution can adopt the same or with little-bit changes according to the requirement of their academic pursuit. Though, in policy the commission has not been recommended the name of any deterrence tool; however, as a deterrence tool as depicted by many studies (Melvin, 2002), Turnitin is a very prominent and effective detection tool. But its services are available on payment basis. It compares the data of uploaded documents with the data which are available online even paid vendors’ data also and flagging the suspected material, if any. Turnitin or any other plagiarism detection tools detect only the similarity of text in the uploaded document/s instead of plagiarism. However, similarity does not lead the substantial form of plagiarism. After analyzing the contents, the originality report may be uploaded. The present paper will try to explain the concept of plagiarism, deterrence tools and working of Turnitin and its role to combat with plagiarism.

Accurate wind power forecasting is essential for the seamless integration of wind energy into modern power systems, yet it remains a challenging task due to the inherent variability of wind, complex atmospheric dynamics, and the influence of local terrain. Traditional forecasting models often struggle to capture these complexities, particularly for short-term and intra-hour predictions. This gap necessitates a shift towards machine learning (ML) methodologies, which have emerged as transformative tools in addressing these challenges. By leveraging large datasets and advanced algorithms, ML models can identify intricate patterns and significantly enhance prediction accuracy. Techniques such as deep learning, ensemble methods, and hybrid approaches integrate weather data with historical power output, improving both spatial and temporal resolutions. Despite their promise, challenges such as data quality, model interpretability, and computational demands require further research to fully optimize ML applications in wind power forecasting. The global transition toward smart grids, driven by the increasing penetration of renewable energy sources (RES), underscores the importance of reliable forecasting. Wind energy, as a key RES, plays a pivotal role in reducing greenhouse gas emissions and mitigating global warming. However, the stochastic nature of wind energy complicates power system analysis and management. Accurate forecasting is critical for enhancing power system security, supporting sustainability, and facilitating economic transactions in energy markets. This review examines ML-based methodologies for wind power forecasting, categorizing them into supervised, unsupervised, semi-supervised, and reinforcement learning techniques. It highlights their adaptability, scalability, and real-time capabilities while addressing challenges posed by noisy data, dynamic system behaviors, and complex grid configurations. Hybrid and ensemble models, in particular, demonstrate exceptional potential in overcoming these challenges. Furthermore, this study provides a detailed summary of the latest advancements in wind power forecasting using AI-based approaches. Key aspects such as data preparation, feature selection, and model assessment are explored, offering insights into improving both the precision and efficiency of wind energy prediction algorithms. By identifying research gaps and emerging trends, this review presents strategic directions for the development of innovative ML-driven forecasting methods. Ultimately, the findings underscore the significance of integrating advanced ML techniques to enhance forecasting reliability, support effective grid management, and contribute to a sustainable energy future.