Wireless sensor network (WSN) installation and power allocation optimization for cybersecurity purposes are difficult tasks which include a well-thought-out method that strikes a balance between goals such as the energy efficiency, connectivity and cyber threat resistance while improving overall system performance and cybersecurity. A novel multi-task walrus optimization (MTWO) technique is offered in this study. The installation and power allocation problem (IPAP) with several jobs is established in this research. The suggested MTWO is used to split the IPAP into numerous scalar components, which are consequently grouped and treated by their desired goals. We evaluate the proposed MTWO approach using simulations and show that it works well in practice. The findings demonstrate that power distribution and installation for cybersecurity activities on WSNs can be improved by the MTWO. The superiority of the problem specific MTWO over the MOGA has been demonstrated by simulation outcome in several network cases, offering a wide range of excellent network designs to aid in the decision-maker selection.
Applications such as industrial automation, healthcare, and environmental monitoring need the use of wireless sensor networks (WSNs). However, due to their dispersed organizational makeup, they have become vulnerable to security risks, particularly clone assaults. To protect confidentiality, availability, and confidentiality, several attacks must be recognized and prevented. This project aims to offer an effective method for identifying and averting clone assaults. To identify cloned both nationally and internationally use a low-cost verification process. In this study, we offer a new adaptive sea-horse optimized light gradient boosting machine (ASHO-LGBM) technique for protecting the network against node identity duplicates. The ASHO approach is used in the ASHO-LGBM framework to improve the recognition accuracy of the light gradient boosting machine (LGBM) characteristics. The replications with the nodes intrusion detection (ID) are used to choose a most trustworthy communication mode. The procedure is intended to be implemented and used for gathering data through an internet component. Using a Python tool, the suggested technique is simulated and its delay, packet delivery ratio, packet drop, and energy are evaluated. When compared to other approaches, the study’s results show that the ASHO-LGBM strategy’s performance analysis achieves the highest accuracy rate.
The innovations in functional genomics have provided a pathway for the identification and prediction of potential druggable human genes that help in the innovation of drug discovery and development. This is obtained through hybrid optimization techniques that involve decision trees and random forest algorithms. This helps to identify the genome-wide druggable human genes using functional genomics data. This is achieved through multiple stages of its analysis. The first stage involves the collection of genomic and proteomic data with numerous disease classifications and tissue structures. The data quality and normalization are achieved through data preprocessing techniques through the integration of various parameters. The hybrid optimization process functions with the aid of a decision tree. These are the primary classifiers that help to determine the individual features within the datasets. This helps to obtain the fundamental selection of potential druggable gene candidates. This helps to provide both the numerical and categorical data. This is suitable for the multifaceted nature of functional genomics data structures. Then the random forest algorithm connects the strength of multiple decision trees to improve the predictive accuracy and overfitting process. Feature importance score is obtained from the random forest model that provides the functional information of the genes with disease mechanisms. The predictive capabilities of the proposed approach are achieved through a cross-validation process. Comparative analysis is done with the proposed system with the existing model through analyzing various performance matrices involving AUC-ROC curves. This helps to obtain the complex relationships between genomic features and druggability. The proposed model provides various innovations in the drug discovery process.
In the ever-evolving domain of medical imaging, the integration of deep learning techniques holds the promise of transformative advancements. This research delved into the potential of employing data transfer within deep learning architectures for the automated detection of three distinct lung cancer types. Leveraging sophisticated methodologies like linear discriminant analysis (LDA), t-SNE, and PCA, the study aimed to enhance accuracy and efficiency in detecting malignancies from lung CT scan images. On rigorous evaluation, the models demonstrated compelling accuracy rates: salivary gland-type lung tumors at $\mathbf{9 0. 5 \%}$, pleomorphic (spindle/giant cell) carcinoma at $88.2 \%$, and primary pulmonary sarcomas at $91.3 \%$. Additionally, ROC curve analysis further highlighted the robust discriminative capability of the models across varied decision thresholds. The promising results accentuate the potential of integrating data transfer techniques with deep learning in a clinical setting. This research not only exhibits a significant stride in lung cancer detection but also paves the path for further innovations in automated medical image analysis.
Breast cancer, a predominant health concern globally, necessitates advanced diagnostic tools for timely and precise detection. This study endeavored to amalgamate the capabilities of magnetic resonance imaging (MRI) scans with machine learning (ML) to foster enhanced diagnostic accuracy. Employing a comprehensive dataset sourced from three major hospitals, our approach utilized preprocessing techniques to refine MRI image quality, followed by intricate feature extraction focusing on shape, texture, and intensity. Three ML models were implemented, with the Random Forests model emerging as the standout, achieving an impressive accuracy of 92%. This represents a notable improvement over traditional MRI analysis, which registered an accuracy of 84%. When benchmarked against contemporary methods like Deep Learning ConvNets at 88% and Gradient Boosted Trees at 87%, our method consistently outperformed. The results underscore the potential of integrating advanced computational models with medical imaging, promising more reliable and early breast cancer detection. This research serves as a testament to the profound impact of technology on medical diagnostics, offering a promising direction for future endeavors in the realm of breast cancer detection.
In this paper, we present a reinforcement learning (RL)-based strategy for placing optimal charging stations (CS) of electric vehicles (EVs) in the case of Urban planning and smart city development under digital twin. The objective is to minimize the energy required by EVs to reach the CS for recharging. Our approach shows the efficacy of computationally identified CS placement over random placement. Extensive research has demonstrated that an RL-based strategy yields better results in identifying suitable CS locations than random positioning. Based on our investigation, the proposed method finds the most effective positions and some alternative locations for the placement of CS. This study presents a novel approach with $\mathbf{2 0. 9 7 \%}$ enhancement in energy efficiency compared to related research findings. Furthermore, our proposed approach demonstrates expedited attainment of an optimal policy, outperforming existing literature.
This research attempts to tackle the prevailing challenges in bandwidth estimation (BWE) for real-time communication systems, with a special emphasis on applying offline reinforcement learning to craft a more accurate neural network for BWE than those built using traditional heuristics. The developed model, “CQLBWE”, represents a data-driven approach to BWE, operating offline. The model exploits heuristic-based techniques from the past to formulate a proficient BWE policy. Furthermore, the successful usage of CQLBWE underscores the practicability of deploying offline reinforcement learning algorithms in the field of BWE.
In the rapidly advancing era of 6 G networks, an efficient resource allocation (RA) is necessitated. Consequently, our paper reveals a sophisticated mathematical model based on evolutionary game theory and replicator dynamics designed to optimize and stabilize resource distribution. The model delineates how evolutionary stable strategies (ESS) can be systematically identified and employed to enhance network efficiency and fairness significantly. Further, strategic interaction analysis and dynamic modeling integration demonstrate that ESS respond adeptly to changing network conditions and robustly guards against inefficiencies caused by signal degradation and user demand variability. Furthermore, we proposed a few algorithms, such as ESS sustainability and stabilization criteria for ESS, to depict the change in strategy population, which turns into the strategy fitness change and convergence of strategic population, respectively. Lastly, our empirical simulations validate the model’s effectiveness in fostering resilient and equitable RA, setting a foundation for future 6G network designs prioritizing adaptability and sustainability. In conclusion, our paper aims to highlight the innovative approach succinctly, as well as the theoretical foundation and practical outcomes of our research, focusing on engaging and addressing a more expansive audience effectively in the upcoming era of next-generation communication technologies.
In today’s highly connected digital era, security has become a crucial concern for online learning platforms, particularly in programs such as the TOEFL ITP (Test of English as a Foreign Language Institutional Testing Program) and Massive Open Online Courses (MOOCs). This study examines the use of Internet of Things (IoT) technology to strengthen security through two-factor authentication (2FA) on TOEFL ITP MOOCs platforms. IoT provides an advanced and efficient solution for securing user access by utilizing connected devices, such as smartphones and wearables, to verify user identity in real time. The research explores the architecture of IoT technology that facilitates 2FA integration, analyzing its advantages and challenges, and evaluating its impact on both security and user experience. By implementing IoT-based 2FA, the system protects personal data and prevents unauthorized access, ensuring that only authenticated users can access learning resources or participate in the TOEFL ITP exam. Furthermore, this study underscores how enhanced security through IoT can foster trust among users and encourage wider adoption of online learning technologies. The findings suggest that IoT-based 2FA not only bolsters security but also upholds academic integrity in an increasingly complex digital landscape.
Knowledge distillation (KD), particularly in multiteacher settings, presents significant challenges in effectively transferring knowledge from multiple complex models to a more compact student model. Traditional approaches often fall short in capturing the full spectrum of useful information. In this paper, we propose a novel method that integrates local and global frequency attention mechanisms to enhance the multiteacher KD process. By simultaneously addressing both fine-grained local details and broad global patterns, our approach improves the student model’s ability to assimilate and generalize from the diverse knowledge provided by multiple teachers. Experimental evaluations on standard benchmarks demonstrate that our method consistently outperforms existing multiteacher distillation techniques, achieving superior accuracy and robustness. Our results suggest that incorporating frequency-based attention mechanisms can significantly advance the effectiveness of KD in multiteacher scenarios, offering new insights and techniques for model compression and transfer learning.
This paper proposes a monopolar microstrip antenna with symmetric ring-shaped trapezoid ground slots. The center patch facilitates gap-coupling feeding directly connected with a 50 Ohm coaxial line, while six gap-coupled radiators are arranged in a quasi-circle configuration. The trapezoid ground slots beneath the six radiators serve to adjust the impedance bandwidth and reduce the overall antenna size. The proposed antenna exhibits an omnidirectional radiation pattern with broad bandwidth. From the optimization, we have obtained a smaller size and a thinner substrate of the proposed antenna.
The present study investigates the monitoring of reservoir water levels to help prevent floods by accurately measuring and controlling the water flow through the reservoir’s door. The system uses multiple technologies, such as the OTT C31 universal current meter, float level switches, and the YagiUda antenna, to gather real-time data from substations and main stations around the dam. The data collected includes water level before and after the dam door, and water velocity at the dam door. Ensuring the efficiency and security of data transmission, the system employs an HT12E encoder and HT12D decoder, which are used to encode and decode data for secured transmission. The data was processed in Arduino Mega 2560 and sent to Raspberry Pi due to its ability to connect to WiFi, which could host a website providing users with real-time data and reducing the damage caused by flooding. The real-time data transmission allows the system to significantly improve the capability for proactive flood management, making it a vital tool for protecting public safety and infrastructure. The simulation and experimental results demonstrate the effectiveness of the proposed system in both controlled and real-world environments. Key findings include the performance of the Yagi-Uda antenna at 433 MHz and the water velocity measurement through varied cross-sectional dam doors.
