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In limited–overs cricket, rain interruptions require adjustment of the target score for the chasing team. The Duckworth–Lewis–Stern (DLS) method is widely used for this purpose but does not explicitly include factors such as pitch condition, dew accumulation, or player strength. To address this limitation, we propose a Fuzzy-DLS model that includes fuzzy logic into a generalized resource function. The method allows batting quality index (BQI), bowling threat index (BTI), pitch state, and weather effects to adjust the resource curve in a continuous and interpretable way. For the 30 illustrative cases in Appendix A, the proposed formulation differs from the DLS resources by a mean absolute amount of 0.0152, with a mean relative difference of and a mean absolute par-score difference of 2.13 runs. In the 100-match ODI sample analysed here, chasing teams won of winter matches compared with in non-winter matches, giving an odds ratio of 2.21 and a chi-square p -value of 0.081. These results support the use of fuzzy match-condition inputs transparently.

Skin cancer is a severe and rapidly advancing condition that can be impacted by multiple factors, including alcohol and tobacco use, allergies, infections, physical activity, exposure to UV light, viral infections, and the effects of climate change. While the steep death tolls continue rising at an alarming rate, lack of symptoms recognition and its preventive measures further worsen the case. In this article, we employ the ISBI-2017 dataset to present an improved FrCN-based hybrid image segmentation method with U-Net to improve detection performance. This paper proposes a hybrid approach using the FrCN-(U-Net) image segmentation technique to enhance results compared to an advanced method for detecting skin cancer types, such as Benign or Melanoma. The classification phase is then handled using the R-CNN algorithm. Our model shows better performance in both training and testing accuracy than any other existing approaches. The results show that the combined method is effective in enhancing early disease diagnosis, which in turn improves treatment outcomes and prognosis. This paper presents an alternative technique for skin cancer detection, which can serve as a guide for clinical practices and public health strategies on how to lower skin-cancer-related deaths.

The DISC framework is widely used to describe behavioral styles in organizations, but it is often applied through static and qualitative interpretation. This study combines graph-based clustering with supervised learning to analyze DISC-style profiles, competencies, and stress outcomes. Using a real-world dataset of 195 employees described by 97 heterogeneous attributes, we construct a weighted similarity graph by fusing (i) cosine similarity of 17 ordinal competency levels, (ii) exact-match similarity of organizational context variables, and (iii) Jaccard similarity of trait-like descriptors. Modularity-based community detection is applied to reveal latent behavioral groups. Random Forest models are then used to predict stress-related outcomes. For 4-class stress prediction (Low, Medium, High, High (Work-related)), stratified 5-fold cross-validation yields an average accuracy of 52.82% . This is above the uniform random baseline (25%) but below the majority-class baseline ( ), indicating moderate predictive signal. Variable-importance analysis suggests that sales-related competency levels contribute strongly to stress differentiation in this cohort. A separate experiment on competency-group prediction reaches near-perfect accuracy, but this is expected because the target is derived from the same competency descriptors used as inputs and therefore reflects information leakage rather than generalizable prediction. Overall, the study shows how DISC assessments can be extended into graph-based and predictive organizational analytics, while also clarifying the limits of what can be inferred from cross-sectional survey attributes.

Secure communication is essential in today’s rapidly evolving digital environment, and strong encryption methods are required to protect private data from unwanted access. The aim of this study is to strengthen the security and complexity of encrypted communications by adopting a new form of cryptographic encryption technique based on the principles of an intuitionistic fuzzy graph. Key graph-theoretic measures, such as domination number, vertex categorization (alpha-strong, beta-strong, and gamma-strong), vertex order coloring, and chromatic number, play important roles in this process. Domination number finds the key vertices of the network, while vertex strength categorization and fuzzy graph coloring provide multiple encryption layers, hence the encoded message is highly resistant to decryption unless a proper key is used. The chromatic number offers further security through various patterns of vertex coloring. The comparative analysis shows the proposed approach to be superior compared to RSA, AES, ECC, and Blowfish due to its increased security, computational efficiency, and resilience to attacks. This framework can be applied to the protection of banking PINs, military access codes, government identification numbers, cryptographic keys, and medical records, so it is an extremely versatile solution for protecting sensitive data. This multi-step approach to encryption through the proposed technique ensures safe transfer and efficient encoding as it establishes a complicated framework.

Skin cancer is a severe and rapidly advancing condition that can be impacted by multiple factors, including alcohol and tobacco use, allergies, infections, physical activity, exposure to UV light, viral infections, and the effects of climate change. While the steep death tolls continue rising at an alarming rate, lack of symptoms recognition and its preventive measures further worsen the case. In this article, we employ the ISBI-2017 dataset to present an improved FrCN-based hybrid image segmentation method with U-Net to improve detection performance. This paper proposes a hybrid approach using the FrCN-(U-Net) image segmentation technique to enhance results compared to an advanced method for detecting skin cancer types, such as Benign or Melanoma. The classification phase is then handled using the R-CNN algorithm. Our model shows better performance in both training and testing accuracy than any other existing approaches. The results show that the combined method is effective in enhancing early disease diagnosis, which in turn improves treatment outcomes and prognosis. This paper presents an alternative technique for skin cancer detection, which can serve as a guide for clinical practices and public health strategies on how to lower skin-cancer-related deaths.

The agricultural economy heavily relies on poultry production, making accurate forecasting of poultry data crucial for optimizing revenue, streamlining resource utilization, and maximizing productivity. This research introduces a novel application of the N-BEATS architecture for multi-dimensional poultry data forecasting with enhanced interpretability through an integrated Explainable AI (XAI) framework . Leveraging its advanced capabilities in time series modeling, N-BEATS is applied to predict multiple facets of poultry disease diagnostics using a multivariate dataset comprising key environmental parameters. The methodology empowers decision-making in poultry farm management by providing transparent and interpretable forecasts. Experimental results demonstrate that N-BEATS outperforms conventional deep learning models, including LSTM, GRU, RNN, and CNN, across various error metrics, achieving MAE of 0.172, RMSE of 0.313, MSLE of 0.042, R-squared of 0.034, and RMSLE of 0.204. The positive R-squared value indicates the model’s robustness against underfitting and overfitting, surpassing the performance of other models with negative R-squared values. This study establishes N-BEATS as a superior and interpretable solution for complex, multi-dimensional forecasting challenges in poultry production, with significant implications for enhancing predictive analytics in agriculture.

To solve fractional differential equations, they are typically converted into their corresponding crisp problems through a process known as the embedding method. This paper introduces a novel direct approach to solving fuzzy differential equations using fuzzy calculations, bypassing the need for this transformation. In this study, we develop the fuzzy Adams–Bashforth (A-B) method and the fuzzy Adams–Moulton (A-M) method to find numerical solutions for fuzzy fractional differential equations (FFDEs) with fuzzy initial values. To demonstrate the accuracy and efficiency of the proposed methods, we determine both the local truncation error and the global truncation error. Additionally, we establish the convergence and stability of these methods in detail. Finally, numerical examples are provided to illustrate the flexibility and effectiveness of the proposed methods.

Decision makers (DMs) are often viewed as autonomous in the majority of multicriteria group decision making (MCGDM) situations, and their psychological behaviors are seldom taken into account. Once more, we are unable to prevent both positive and negative flows of varying alternative preferences due to the nature of attributes or criteria in complicated decision-making problems. However, DMs’ perspectives are likely to affect one another in complicated MCGDM issues, and they frequently use subjective limited rationality while making decisions. The multicriteria quantum decision theory-based group decision making integrating the TODIM-PROMETHEE II strategy under linguistic Z-numbers (LZNs) is designed to overcome the aforementioned problems. In our established technique, the PROMETHEE II controls the positive and negative flows of distinct alternative preferences, the TODIM method manages the experts’ personal regrets over a criterion, and the quantum probability theory (QPT) addresses human cognition and behavior. Because LZNs can convey linguistic judgment and trustworthiness, we provide expert LZNs for their viewpoints in this work. We determine the criterion weights for each expert after first obtaining their respective expert weights. Second, to represent the limited rational behaviors of the DMs, the TODIM-PROMETHEE II approach is introduced. It is employed to determine each alternative’s dominance in both positive and negative flows. Third, a framework for quantum possibilistic aggregation is developed to investigate the effects of interference between the views of DMs. The views of DMs are seen in this procedure as synchronously occurring wave functions that affect the overall outcome by interfering with one another. The model’s efficacy is then assessed by a selection of renewable energy case studies, sensitive analysis, comparative analysis, and debate.

Paper is based on research and P°C with goal to unlock business value in delivery optimization, using advanced analytics and interactive visualization. There are several common approaches including travelling salesman problem (TSP) that has various applications even in its purest formulation, such as planning or logistics. Knowledge base powered by large data set is described through development and final, interactive, form. Using common principles, paper describe business value that can be extracted using advanced analytics and interactive visualization, how to structure steps as best practice to achieve success and what additional benefits can be expected by supporting this approach. As part of research, various insights are extracted from PoC and presented in forms suitable for general understanding and future research.

The growing sophistication of cyber-attacks demands encryption processes that go beyond the confines of conventional cryptographic methods. Traditional cryptographic systems based on numerical algorithms or standard graph theory are still open to structural and computational attacks, particularly in light of advances in computation power. Fuzzy logic’s in-built ability to manage uncertainty together with the representation ability of fuzzy hypergraphs for describing complex interrelations offers an exciting avenue in the direction of developing highly evolved and secure cryptosystems. This paper lays out a new framework for cryptography using fuzzy hypergraph networks in which a hidden value is converted into a complex structure of dual fuzzy hypergraphs that remains completely connected. This technique not only increases the complexity of the encryption process, but also significantly enhances security, thus making it highly resistant to modern-day cryptographic attacks and appropriate for high security application. This approach improves security through enhanced entropy and the introduction of intricate multi-path data exchange through simulated nodes, rendering it highly resistant to contemporary cryptographic attacks. It ensures effective key distribution, accelerated encryption–decryption processes, and enhanced fault tolerance through dynamic path switching and redundancy. The adaptability of the framework to high-security, large-scale applications further enhances its robustness and performance.