In the rapidly developing world, artificial intelligence (AI) is one of the emerging applications in the medical domain. Early detection of cancer is one of the most difficult processes, especially when it comes to the differentiation of cancer’s structure, length, and size. AI methodology offers innovations and decision-making applicable to specific processes like data collecting, management, results, and conclusions. Due to the lack of particular indicators, the difficult location of the pancreas, and the absence of early symptoms, pancreatic cancer (PC) is difficult to identify with low and late analysis. However, the imaging approach is slightly improving analysis, but there is still potential for enhancement in systematizing guidelines. This comprehensive review will mainly focus on various applications of AI in pancreatic cancer diagnosis. Furthermore, this review presents various architectures based on machine learning (ML) and deep learning (DL) methods for applications such as early detection, classification, tumor staging, and pancreatic cancer survival prediction. In order to better comprehend challenging cases, clinical practitioners can benefit from the supplementary information and useful recommendations provided by various techniques. Finally, this review potentially analyzes the advantages and drawbacks present in pancreatic cancer. This review provides an overview of research based on AI methods and algorithms that provide superior performance from a variety of pancreatic cancer patients while also providing viable future perspectives with significant advancements to overcome drawbacks in previous research and provide enhanced performance, stating their effectiveness and robustness analysis.
The personalized recommendation systems are important in increasing user engagement and discovery of content in the contemporary digital platforms. In this paper, we suggest the MRGE-PRS, a Multi-Relational Graph Enhanced Personalized Recommendation System, which is intended to produce context and adaptive recommendation in the heterogeneous data setting. The framework additionally proposes complex user-item-context relations as structured heterogeneous knowledge graph, with user nodes encoding demographic and behavioral factors, item nodes encoding categorical/descriptive factors, and contextual nodes encoding time and place-based information. Multi-relational edges combine different forms of interaction cues, like ratings, reviews, as well as implicit feedback like clicks. The architecture has the latent interaction-aware representation learning and multi-level personalized attention mechanism which refine the node embeddings and dynamically focus the importance on various relational interactions. Temporal knowledge graph with an exponential decay is presented to balance between long-term user preferences and recent behavioral trends and allow modeling preferences adaptively. Besides, textual review semantics improvement also improves feature representation, whereas multi-hop reasoning reflects the dependency indirectly across the knowledge graph structure. The contextual indicators such as time-dependent trends and patterns of interaction frequency more contribute to the real-time flexibility of the recommendation process. The framework proposed will overcome most of the problematic areas of the recommendation system as it is meant to deal with such issues as data sparseness, preference drift, and heterogeneous interaction modeling. The benchmark data of MovieLens, Yelp, and Dianping where experimental assessments have been undertaken show that MRGE-PRS outperforms the current baseline techniques. Optimally configured, the model has a AUC and F1-score of 0.99, indicating that it can give scalable, robust, and context-sensitive personalized recommendations in a broad spectrum of applications.
The intersection of open-source processor architectures and machine learning is driving the demand for customizable, efficient, and accessible hardware. This survey examines the state of the RISC-V ISA in machine learning applications, analyzing current capabilities, challenges, and future directions based on recent research. The analysis covers academic and commercial implementations, software frameworks, and real-world applications. The RISC-V machine learning ecosystem is evaluated, from instruction set extensions and core implementations to compiler optimizations and deployment strategies. Key contributions include a unified taxonomy of RISC-V ML implementations, a comparative analysis of performance and design trade-offs, an evaluation of software toolchain maturity, and the identification of emerging trends in instruction set extensions and specialized accelerators. Findings reveal progress in energy efficiency, specialized instruction development, and framework integration, while highlighting challenges in standardization, verification complexity, and ecosystem fragmentation. The analysis proposes four research directions to address current limitations: specialized neural processing extensions, adaptive and modular processor architectures, security frameworks, and energy-efficient multi-domain architectures. These directions provide a roadmap for advancing RISC-V as a foundational platform for next-generation machine learning systems.
Silver nanoparticles (AgNPs) have been synthesized using Hypoestes phyllostachya Baker leaf extract by a green synthesis method. No past research work has been reported on this plant including assessments of silver nanoparticles. The extract used here is a green and natural reducing and capping agent. The synthesis of AgNPs is confirmed by surface plasmon resonance at 424 nm by UV-Visible Spectroscopy. The AgNPs synthesized here have been characterized by FTIR, SEM, EDX, XRD, DLS, and zeta potential measurements, showing spherical shape, crystalline structure, and average particle size of 211.8 nm, and zeta potential of -22.3 mV, indicating moderate stability.The FTIR results confirmed the role of phytochemicals in the reduction and stabilization of AgNPs. The AgNPs showed promising biological activities such as antioxidant activity (IC₅₀ = 215.26 µg/mL by DPPH assay), α-amylase inhibitory activity (IC₅₀ = 144.68 µg/mL), and anti-inflammatory activity (IC₅₀ = 144.29 µg/mL). In addition, the AgNPs synthesized by H. phyllostachya showed promising antibacterial activity against both Gram-positive bacteria such as Staphylococcus aureus and Bacillus subtilis, and Gram-negative bacteria including Escherichia coli and Pseudomonas aeruginosa in concentration-dependent manner. The results of the present study revealed the potential of H. phyllostachya-mediated AgNPs as promising candidates for biomedical applications.
Because of the heavy data and communication advances, the utilization of Internet of Things (IoT) gadgets has expanded dramatically. In the improvement of IoT, Wireless Sensor Network (WSN) plays out a crucial part and involves easy keen gadgets for data gathering. In any case, such savvy gadgets have requirements regarding calculation, preparing, memory, and energy assets. Alongside such requirements, the major difficulties for WSN are to accomplish dependability with the security of communicated information in a weak climate alongside pernicious nodes. This paper intends to build up an Anomalous Intrusion Detection Protocol and Intrusion Prevention Protocol for interruption evasion in IoT dependent on WSN to expand the network time frame and information reliability. The proposed framework makes dissimilar energy-efficient groups dependent on the natural characteristics of nodes. Also, in view of the (k, n) limit related Shamir mystery sharing plan, the unwavering quality also, the security of the tangible data within the Base Station and group head are accomplished. The proposed security conspires demonstrates a trivial answer to adapt to interruptions produced by malignant nodes. The trial results utilizing the network test system Network Simulator-2 show that the proposed directing convention accomplished improvement as far as network lifetime, end-to-end delay as 24%, packet delay ratio as 30%, when contrasted and the current work under unique network characteristics.
