Digital image forgery detection is crucial in addressing the rapid spread of fake information through manipulated images, especially on social media platforms. Traditional techniques often focus on specific types of forgery, limiting their effectiveness in real-world scenarios. Traditional methods heavily depend on manual feature engineering, which often results in overlooked manipulations, decreased accuracy, adaptability, and scalability issues when handling large datasets or high-resolution images. Deep learning has emerged as a powerful tool for addressing the challenges associated with image forgery detection. The proposed work introduces an innovative method for detecting image forgeries using deep learning techniques, employing convolutional neural networks (CNNs) and specifically evaluating the performance of the EfficientNetb7 model. This method leverages transfer learning to detect copy-move image forgery. It involves generating featured images by calculating the difference between the input image and compressed versions, which are then fed into pre-trained CNN model. The model undergo fine-tuning to adapt to forgery detection. Additionally, the output of the forgery detection process includes both text and audio. This combination enhances the accessibility and interpretability of the detection results, making them more understandable for users with different sensory preferences or impairments. This added feature ensures that the detection outcomes are easily comprehensible and usable across a broader range of users and applications.
Efficient routing techniques are critical in a wireless sensor network (WSN) to extend network lifetime and preserve energy, providing uninterrupted data transfer across a wide range of applications. In this research, we intend to develop a novel dynamic waterwheel plant optimization (DWPO) strategy for efficient path planning with blockchain (BC) for enhancing cybersecurity in WSN. Our proposed approach utilizes a fitness function that incorporates inter cluster distance along with other relevant parameters. This function strategically contributes to the selection of ideal routes, contributing to the efficacy of our method for improving cybersecurity in WSN contexts. The experimental validation of the DWPO model is conducted using the MATLAB program and examined in terms of the network lifetime ($93.6 \%$), packet delivery ratio ($90.2 \%$), and energy consumption ($\mathbf{1 8. 2} \mathrm{J}$). The experimental results illustrate that the proposed DWPO approach performed better than the other existing approaches for protecting the routing nodes through efficient path planning with BC-assisted DWPO in a WSN.
There are significant cybersecurity challenges that face wireless sensor networks (WSNs) as a result of their decentralized nature and limited resources although they are highly important in most fields. Traditional security mechanisms frequently fail to cope with the changing and diverse conditions in WSNs. To reduce data transfer but maintain WSNs sensor saturation and data security, this work proposes a prediction-based data fusion and sensing strategy. The suggested method called the ARIMA-SK-EELM system, which is made up of autoregressive integrated moving average (ARIMA), stable kernel-enhanced extreme learning machine (SK-EELM), and Threefish algorithm (TFA). In the procedure on data sensing and fusion, ARIMA predicts initially from a few data elements, SK-EELM for precise accuracy on initial expected value similar to actual value while TFA is used during transmissions for both encoded and decoded data. This paper introduces an ARIMA-SK-EELM model with high predictability, low interferences, strong scalability, and secrecy. The results of simulation show that this technique suggested can be effective in reducing unnecessary transfers by accurate forecasting.
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.
