The defocus or motion effect in images is one of the main reasons for the blurry regions in digital images. It can affect the image artifacts up to some extent. However, there is a need for automatic defocus segmentation to separate blurred and sharp regions to extract the information about defocus-blur objects in some specific areas, for example, scene enhancement and object detection or recognition in defocus-blur images. The existence of defocus-blur segmentation algorithms is less prominent in noise and also costly for designing metric maps of local clarity. In this research, the authors propose a novel and robust defocus-blur segmentation scheme consisting of a Local Ternary Pattern (LTP) measured alongside Pulse Coupled Neural Network (PCNN) technique. The proposed scheme segments the blur region from blurred fragments in the image scene to resolve the limitations mentioned above of the existing defocus segmentation methods. It is noticed that the extracted fusion of upper and lower patterns of proposed sharpness-measure yields more noticeable results in terms of regions and edges compared to referenced algorithms. Besides, the suggested parameters in the proposed descriptor can be flexible to modify for performing numerous settings. To test the proposed scheme’s effectiveness, it is experimentally compared with eight referenced techniques along with a defocus-blur dataset of 1000 semi blurred images of numerous categories. The model adopted various evaluation metrics comprised of Precision, recall, and F1-Score, which improved the efficiency and accuracy of the proposed scheme. Moreover, the proposed scheme used some other flavors of evaluation parameters, e.g., Accuracy, Matthews Correlation-Coefficient (MCC), Dice-Similarity-Coefficient (DSC), and Specificity for ensuring provable evaluation results. Furthermore, the fuzzy-logic-based ranking approach of Evaluation Based on Distance from Average Solution (EDAS) module is also observed in the promising integrity analysis of the defocus blur segmentation and also in minimizing the time complexity.

Curtailing the information loss due to network partitioning is imperative for the reliable communication in a distributed hash table (DHT)-based routing protocol for mobile ad hoc networks (MANETs). Both the limited transmission range and mobility of nodes cause recurrent network partitioning in MANETs that leads to an inaccessibility of the nodes' mapping information, loss of the LID space, and partitioning of the DHT structure. To address the above-mentioned issues, this paper proposes a protocol that employs pre-partitioning measures, such as the critical link/node detection, the replication of the nodes' mapping information, and so on, to augment the inter-node communication, but also reflects substantial improvement in the lookup delay. The proposed protocol effectively exploits the local neighbor information in a 3-D logical structure to identify/detect the critical nodes by employing a localized distributed algorithm. Simulation results show the significance of the proposed protocol in terms of end-to-end lookup delay, routing overhead, and success ratio.

In Distributed Hash Table (DHT)-based Mobile Ad Hoc Networks (MANETs), a logical structured network (i.e., follows a tree, ring, chord, 3D, etc., structure) is built over the ad hoc physical topology in a distributed manner. The logical structures guide routing processes and eliminate flooding at the control and the data plans, thus making the system scalable. However, limited radio range, mobility, and lack of infrastructure introduce frequent and unpredictable changes to network topology, i.e., connectivity/dis-connectivity, node/link failure, network partition, and frequent merging. Moreover, every single change in the physical topology has an associated impact on the logical structured network and results in unevenly distributed and disrupted logical structures. This completely halts communication in the logical network, even physically connected nodes would not remain reachable due to disrupted logical structure, and unavailability of index information maintained at anchor nodes (ANs) in DHT networks. Therefore, distributed solutions are needed to tolerate faults in the logical network and provide end-to-end connectivity in such an adversarial environment. This paper defines the scope of the problem in the context of DHT networks and contributes a Fault-Tolerant DHT-based routing protocol (FTDN). FTDN, using a cross-layer design approach, investigates network dynamics in the physical network and adaptively makes arrangements to tolerate faults in the logically structured DHT network. In particular, FTDN ensures network availability (i.e., maintains connected and evenly distributed logical structures and ensures access to index information) in the face of failures and significantly improves performance. Analysis and simulation results show the effectiveness of the proposed solutions.

Accurate classification of wheat genotypes for adaptability and stability across diverse environments remains challenging in multi-environment trials. This study integrated mixed models, genotype plus genotype × environment interaction (GGE) biplot analysis, the Eberhart-Russell (ER) stability approach, and supervised machine learning to evaluate wheat genotype performance and improve ER-based classification. Seventy wheat genotypes were tested across 23 environments in Pakistan under the National Uniform Wheat Yield Trials (2023-2024). Genotype and environment effects were highly significant (p < 0.01), while genotype × environment interaction was significant (p < 0.05), confirming differential genotype responses. GGE biplot analysis identified interaction patterns, specific adaptation, and stable entries, whereas ER parameters classified genotypes into six stability classes. After SMOTE, PCA, and hyperparameter tuning, the optimized support vector machine achieved the best performance, with 94.4% accuracy and strong agreement with ER classes. This integrated framework supports the selection of stable, high-yielding wheat genotypes.