Synthesis: A Multidisciplinary Research Journal
Welcome to Synthesis: A Multidisciplinary Research Journal Bridging Disciplines, Connecting Ideas ISSN (Online): [To be assigned] At Synthesis: A Multidisciplinary Research Journal, we believe in the power of bringing together diverse perspectives to spark innovation, drive progress, and solve the complex challenges of our time. As a leading multidisciplinary research journal, we are dedicated to publishing high-quality, peer-reviewed articles that push the boundaries of knowledge and open new paths for investigation. Synthesis: A Multidisciplinary Research Journal invites you to contribute your latest research and join a growing community of contributors who are shaping the future of multidisciplinary studies. Whether you’re unveiling novel findings, proposing innovative theories, or presenting comprehensive reviews, your work will find a home here.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. You are free to share and adapt the material, but only for non-commercial purposes. You must give appropriate credit to the author(s).
Digital-Twin Driven AI Framework for Climate-Adaptive Crop Yield Forecasting Using Multi-Source Satellite and Weather Data
Accurate crop yield forecasting under climate variability remains a critical challenge for global food security, as extreme weather events intensify and conventional tools fail to capture agro-ecosystem complexity. Existing approaches including process-based crop models, statistical regression frameworks, and remote sensing-driven empirical methods suffer from limited adaptability, poor spatial transferability, and inability to assimilate real-time data under non-stationary climate conditions. This paper presents a Digital-Twin Driven Artificial Intelligence (DTAI) framework integrating multi-source satellite data from Sentinel-2, MODIS, ERA5 reanalysis, and GPM precipitation within a physics-informed crop growth simulation environment. A field-scale digital twin is constructed using an Ensemble Kalman Filter for continuous state estimation, feeding a hierarchical deep learning ensemble comprising a Spatiotemporal Transformer Network, a Crop-Field Graph Neural Network, and a Bayesian Output Integration Module. Climate adaptability is achieved through a Model-Agnostic Meta-Learning mechanism that detects distributional shift via Maximum Mean Discrepancy and recalibrates model parameters using minimal in-season observations. Experimental evaluation across four agro-climatic zones covering seven crop-region combinations over five growing seasons produced a Mean Absolute Percentage Error of 4.3%, Root Mean Squared Error of 0.31 t/ha, and R-squared of 0.94, outperforming four established baselines by 18 to 43 percent. The DTAI framework achieves full recalibration within 12 days during anomalous seasons and near-supervised accuracy through twenty-shot domain adaptation, establishing a scalable and climate-resilient decision-support tool for food security monitoring
Digital Twin, Crop Yield Forecasting, Spatiotemporal Deep Learning, Remote Sensing, Climate Adaptation, Graph Neural Networks, Meta-Learning, Precision Agriculture.
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc/4.0
Multimodal Learning for Anomaly Detection in Cyber-Physical Smart Infrastructure: A Systematic Survey
Cyber-physical smart infrastructures (CPSIs) refer to an ever-expanding number of networks of connected systems, including smart electrical grids and intelligent transportation systems, industrial automation systems and smart health systems. These systems generate uninterrupted, high-speed and multi-dimensional data of a wide variety of sensing, communication and control systems. Identifying deviations in such an environment, be it due to equipment malfunctions, deviations in operations, or intentional cyber-attack, is of utmost importance to safety and non-disruptive provision of services. Conventional single-modality methods only deal in one type of data at a time, which dramatically constrains their ability to detect cross modal signatures of the complex data that define anomalies in realworld CPSIs. Multimodal learning overcomes this weakness by modeling jointly complementary information between heterogeneous sources of data, such as sensor readings or network traffic logs, surveillance video streams, text messages and context metadata. The current survey is a well-structured and extensive analysis of multimodal learning techniques used in the context of detecting anomalies in CPSIs. We establish a taxonomy of fusion strategies and architectural paradigms and examine in depth 75 peer-reviewed publications released between 2018 and 2024, and how they are applied in five key domains of CPSI. We further discuss benchmark datasets, evaluation practices, and a set of clearly identified open challenges covering data heterogeneity, label scarcity, real-time constraints, adversarial threats, and explainability requirements. The survey concludes with concrete research directions that reflect the practical demands of deploying multimodal anomaly detection in real infrastructure environments
Multimodal learning, anomaly detection, cyber-physical systems, smart infrastructure, sensor fusion, deep learning, graph neural networks, transformer models, industrial IoT, intrusion detection, time-series analysis, federated learning
Explainable Hybrid Graph Neural Networks for Financial Fraud Detection and Credit Risk Propagation
A rise of complex digital financial systems has prompted a surge of advanced fraudulent actions and escalating issues in proper credit risk evaluation. Conventional machine learning methods would tend to overlook the relational relationships and time dynamics of financial data, which leads to lower accuracy with detection limited and slower risk detection. To overcome these difficulties, we present in the current paper Explainable Hybrid Graph Neural Network (EH-GNN) model to do financial fraud early detection and credit risk propagation modeling. The proposed solution models financial transactions as a heterogeneous graph and incorporates a relational attention, time memory model, and risk propagation systems in the same architecture. It also includes a dual-task prediction module that simultaneously predicts fraud risk and credit risk as well as a multi-level explainability module that offers explainable understanding at a feature and a graph level. The proposed framework has been proved effective through extensive experiments carried out on the PaySim, and Credit Card Fraud Detection datasets. The EH-GNN has a higher accuracy of 98.6, F1-score of 0.93 and ROC-AUC of 0.968 compared to the state-of-the-art baseline models. Moreover, it minimizes the error in credit risk prediction with 0.089 RMSE which depicts a better reliability of Ms.Credit risk estimation. The findings support that the suggested framework is able to represent complicated financial dynamics and offer predictions that can be interpreted, hence is a powerful and viable way to apply to real-life fraud detection and risk management mechanisms.
Financial Fraud Detection, Graph Neural Networks (GNN), Credit Risk Prediction, Explainable Artificial Intelligence (XAI), Risk Propagation, Temporal Graph Learning, Heterogeneous Graph Modeling
Copyright (c) 2023 https://creativecommons.org/licenses/by-nc/4.0
Explainable AI Framework for Transparent and Stable Autonomous Traffic Decision-Making
More intelligent transportation systems are turning to the use of artificial intelligence to implement adaptive, data-driven traffic control. Although modern machine learning and deep learning models have a significant positive impact on traffic forecasting and signal operation, their black box does not provide transparency and trust in the real-time autonomous decision-making. According to recent research, explainability in traffic systems should be incorporated to enhance reliability and accountability. Nevertheless, the majority of the currently available methods are based on an explanation of post-hoc AI methodologies, which cannot offer consistent explanations in unstable settings. This paper aims to solve these shortcomings by introducing a Context-Aware Explainable Decision Framework (CA-XDF) a unified pipeline that incorporates prediction, explanation generation, temporal consistency validation and confidence-aware decision filtering. The framework uses lightweight machine learning models with real time feature attribution and stability aware mechanism to achieve reliable decision-making. Experimental analysis on real-world traffic data and neuro-simulation based settings show that the proposed framework yields an actuality of 92.6, which is surpassing the baseline models, and enhances the stability of the explanation by about 17 percent and shortening the mean vehicle waiting period to 12 percent. The findings substantiate the importance of installing explainability in the decision loop as it improves interpretability and operational performance. The suggested framework will give a viable and scalable approach to transparent autonomous traffic systems, covering critical issues in describing in real-time decisions
Explainable Artificial Intelligence (XAI), Autonomous Traffic Systems, Transparent Decision-Making, Temporal Consistency, Intelligent Transportation Systems, Machine Learning, Decision Reliability, Traffic Signal Control
Meta-Reasoning Framework for Enhancing Multi-Step Inference in Large Language Models
Large Language Models (LLMs) have shown impressive capabilities in natural language processing activities, but do not yet perform effective multi-step reasoning and commonly generate logically inconsistent intermediary steps. This is because it is limited by the failure to control reasoning in a structured manner, and insufficient measures to justify intermediate claims. In a bid to mitigate this challenge, a Meta-Reasoning Learning Framework is proposed which explicitly represents reasoning as a multi stage process that incorporates planning, step-wise inference and self-reflective validation. The framework proposes a pipeline that is coordinated to bring out a model that would generate a reasoning blue print, guided inference and repeatable refinement of a reasoning, using feedback that is used to correct the reasoning. The suggested method is tested on a variety of benchmark reasoning problems, showing great improvements compared to baseline models. The results of the experiments have demonstrated that the framework has an accuracy of 91.3, and it is stronger than the strongest base by about 6 or 8, besides the reasoning consistency of 0.88 and error correction rate of 46.5. These results indicate enhanced robustness and reliability in multi-step inference. The results point out that the use of systematic meta-reasoning and refinement processes significantly enhance performance and reasoning. The work offers a legitimate and explainable method towards progressive credible reasoning in LLMs, and possible applications in complex decision-making and intelligent systems
Large Language Models, Multi-Step Reasoning, Meta-Reasoning Framework, Self-Reflective Learning, Explainable Artificial Intelligence, Structured Inference
Agentic AI Framework for Multi-Step Decision Making in Complex Dynamic Environments
The ability of autonomous decision-making in complex dynamic settings has evolved as a pressing issue in the contemporary application of artificial intelligence, and such as robotics, cyber-physical systems and intelligent control. These settings have partial observability, long-horizon dependencies and are uncertain and limit the applicability of classic reinforcement learning and sequence-based decision models. Current solutions are not always structured, lack memory integration and free-flowing adaptive reasoning, resulting in sub-optimal performance of multi-step tasks. To overcome these drawbacks, this paper suggests an Agentic Multi-Step Decision Intelligence (AMSDI) system that leverages a perceptual system that takes into consideration the context, a goal decomposition system with task graphs, memory-enhanced reasoning, and uncertainty-based policy learning, all within a single architecture. The framework allows autonomous agents to make plans, reason and change action iteratively with long decision horizons. The proposed model is tested in a variety of benchmark settings, such as MiniGrid, ALFWorld, and Meta-World, and compared to practically relevant baseline models, including PPO, Options Framework, Decision Transformer, ReAct Agent, and Neural Episodic Control. The experimental findings indicate that AMSDI has a Task Success Rate of 0.89, which is better than baselines by up to 7-18 and more efficient in multi-step processing and reduced uncertainty in decisions by approximately 20%. Such results suggest that the developed framework delivers a scalable and versatile model of autonomous multi-step decision-making, and has a high chance of implementation in dynamic and uncertain settings in real-life.
Agentic Artificial Intelligence, Multi-Step Decision Making, Reinforcement Learning, Hierarchical Planning, Memory-Augmented Reasoning, Uncertainty-Aware Learning, Dynamic Environments
A Comprehensive Review of Agentic AI Systems for Autonomous Reasoning, Planning, and Collaborative Decision-Making
Another paradigm that has been created is Agentic Artificial Intelligence (AI) which goes beyond the old time generative models and moves to autonomous, goal-oriented systems which can reason, plan and collaborate in decision making. The growing complexity of the real-world application requires intelligent systems, which are able to work with limited human interventions and adapt to changing environments. This review is a systematic analysis of agentic AI systems, which examine their principles, architectural paradigms, reasoning, and planning strategies, as well as collaborative structures. A common taxonomy is created that classifies agentic systems by their operational structure, model of interaction, and the degree of autonomy. The review also gives a comparison analysis of the important architectures, such as large language model-based, cognitive, planner-executor, reinforcement learning-based, as well as hybrid ones. Moreover, various reasoning paradigms like symbolic, probabilistic, and language-based reasoning are examined and their respective advantages and disadvantages are brought out. The research provides insights into key areas of application such as software automation, robotics, medical services, financial systems, and intelligent infrastructure, proving the utility of agentic AI. Although huge advancements have been made, problems like hallucination, scaling limitations, complexity of coordination, and alignment problems still exist. The conclusion of the review to self-improving systems, trustful AI, and scalable multi-agent ecosystems presents future research directions, which gives a systematic base to the development of next-generation autonomous intelligent systems.
Agentic Artificial Intelligence, Autonomous Agents, Multi-Agent Systems, Large Language Models, Autonomous Reasoning, Task Planning, Collaborative Decision-Making, Intelligent Systems
A Comprehensive Survey on Artificial Intelligence Blockchain Integration: Architectures, Applications, Challenges, and Future Directions
The convergence of Artificial Intelligence (AI) and blockchain has emerged as a transformative paradigm for enabling secure, decentralized, and intelligent systems across diverse application domains. While AI provides advanced data-driven analytics and decision-making capabilities, blockchain ensures data integrity, transparency, and trust through decentralized architectures. However, the integration of these technologies introduces significant challenges related to scalability, computational efficiency, privacy, and interoperability. This survey presents a comprehensive analysis of AI–blockchain integration by systematically reviewing existing literature, proposing a structured taxonomy of integration architectures, and examining key application domains, including healthcare, finance, supply chain, Internet of Things (IoT), and smart cities. The taxonomy categorizes integration approaches into AI on blockchain, blockchain for AI, hybrid architectures, and decentralized AI systems, providing a unified framework for understanding the evolving landscape. Furthermore, a comparative analysis is conducted to evaluate the performance trade-offs among different integration paradigms, highlighting the balance between scalability, security, and efficiency. The study also identifies critical challenges and limitations that hinder large-scale adoption, followed by a detailed discussion of emerging research directions, including decentralized learning, privacy-preserving AI, and energy-efficient blockchain systems. Overall, this survey provides valuable insights and a structured foundation for advancing research and development in AI–blockchain convergence, supporting the design of next-generation decentralized intelligent systems.
Artificial Intelligence, Blockchain, Machine Learning, Decentralized Systems, Federated Learning, Smart Contracts, Internet of Things (IoT), Secure Data Sharing
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc/4.0
Green Artificial Intelligence: A Critical Review of Energy-Efficient Machine Learning and Sustainable Computing Frameworks
The rapid advancement of artificial intelligence (AI) and machine learning (ML) has significantly increased computational demands, leading to substantial energy consumption and environmental impact. Recent studies highlight that large-scale AI models contribute notably to carbon emissions due to intensive training and deployment processes. This has motivated the emergence of Green Artificial Intelligence (Green AI), which emphasizes the development of energy-efficient, resource-aware, and environmentally sustainable AI systems.This paper presents a comprehensive review of energy-efficient machine learning techniques and sustainable computing approaches across model, system, and hardware levels. The study systematically analyzes key methodologies, including model compression, pruning, quantization, knowledge distillation, efficient neural architectures, edge computing, and hardware-aware optimization strategies. In addition, carbon-aware evaluation frameworks and metrics for quantifying energy consumption and environmental impact are critically examined. A comparative analysis is conducted to evaluate these techniques based on energy efficiency, performance trade-offs, scalability, and implementation complexity. The findings indicate that no single approach is universally optimal, and effective Green AI solutions require a hybrid integration of multiple optimization strategies. Furthermore, the review identifies key challenges such as the lack of standardized benchmarks, hardware heterogeneity, and limited real-world validation. Overall, this work highlights the importance of integrating sustainability into AI system design and provides insights to guide future research toward developing scalable and environmentally responsible intelligent systems
Green Artificial Intelligence, Energy-Efficient Machine Learning, Sustainable Computing, Model Compression, Edge Computing, Carbon Footprint, Hardware-Aware Optimization, AI Sustainability
Edge-AI Enabled Uncertainty-Aware Intelligent Traffic Management System Using Graph-Based Forecasting for Smart Cities
The high rate of urbanization has brought about a lot of traffic congestion to the smart cities, requiring smart and scalable traffic control solutions. Conventional signal control systems are not responsive to changing traffic situations, whereas current prediction-based models do not take into account the uncertainty factor, and hence unreliable decisions are made. To discuss the issue of the real-time optimization of traffic, this paper offers a solution to it through the creation of an Edge-AI Enabled Uncertainty-Aware Traffic Management System (E-UTMS). The framework combines the edge-based traffic perception, graph based spatio-temporal prediction and uncertainty-conscious adaptive signal control into one architecture. At the edge the traffic descriptors extracted include the number of vehicles, the length of a queue and occupancy of a lane which are then modeled on a dynamic traffic graph to provide congestion patterns in the short term. A risk conscious control method has built in predictive uncertainty to enable sound decision making according to different traffic conditions. Our experimental analysis shows that the proposed method reduces the average waiting time by 19.2%, queue length by 21.9%, and increases the throughput by 18.9% compared to prediction-based control without uncertainty. Further, the system is real time with an average inference time of 28.5 ms. These findings demonstrate the efficiency of the combination of edge intelligence and uncertainty-aware control as a solution to the next-generation smart city traffic management systems, which is scalable and reliable.
Edge Artificial Intelligence, Intelligent Traffic Management, Spatio-Temporal Graph Modeling, Uncertainty-Aware Control, Smart Cities, Adaptive Traffic Signal Control
Adaptive Self-Supervised and Graph-Based Framework for Real-Time Zero-Day Intrusion Detection in IoT Networks
The rapid expansion of Internet of Things (IoT) networks has significantly increased the attack surface for cyber threats, particularly zero-day attacks that cannot be detected using traditional signature-based intrusion detection systems. Existing machine learning-based approaches, although effective for known attack patterns, often fail to generalize to unseen threats and struggle with dynamic network behavior. To address these limitations, this paper proposes an adaptive machine learning framework for real-time detection of zero-day cyber-attacks in IoT environments. The framework integrates self-supervised representation learning, temporal graph-based interaction modeling, and a hybrid open-set detection mechanism within a drift-aware continual learning architecture. This design enables robust identification of both known and previously unseen attack patterns while adapting to evolving traffic distributions. Experimental evaluation on benchmark datasets demonstrates that the proposed approach achieves a detection accuracy of 98.2%, a zero-day detection rate of 92.6%, and a reduced false positive rate of 2.3%, outperforming conventional machine learning and deep learning baselines. Furthermore, the framework maintains high performance under concept drift conditions while achieving low detection latency suitable for real-time deployment. These results highlight the effectiveness of integrating representation learning, structural modeling, and adaptive mechanisms for building scalable and resilient intrusion detection systems in modern IoT networks.
Internet of Things (IoT), Intrusion Detection System (IDS), Zero-Day Attack Detection, Adaptive Machine Learning, Self-Supervised Learning, Graph Neural Networks (GNN), Open-Set Recognition, Concept Drift, Real-Time Cybersecurity, Anomaly Detection
Adaptive Multimodal Deep Learning for Early Cardiovascular Disease Detection Using ECG Signals and Clinical Records
Cardiovascular diseases (CVDs) remain the leading cause of global mortality, emphasizing the need for accurate and timely prediction methods that can support early clinical intervention. Recent advances in artificial intelligence have demonstrated the potential of machine learning and deep learning techniques for cardiovascular risk assessment using medical data. However, many existing approaches rely on either structured clinical records or electrocardiogram (ECG) signals individually, which limits their ability to capture complementary physiological and clinical information. This study addresses this limitation by proposing a multimodal deep learning framework, termed M2CVD-Net, for early detection of cardiovascular diseases using heterogeneous medical data. The proposed architecture integrates ECG waveform signals and structured clinical variables through modality-specific encoders and an adaptive gated fusion mechanism that dynamically combines multimodal feature representations. The framework is evaluated using publicly available datasets, including clinical records from the MIMIC-IV database and ECG signals from the PTB-XL dataset. Experimental results demonstrate that the proposed model achieves superior performance compared with conventional machine learning and unimodal deep learning models, obtaining an accuracy of 89.2%, F1-score of 0.871, and AUROC of 0.932 in cardiovascular disease prediction. The findings indicate that adaptive multimodal learning significantly enhances predictive performance by effectively capturing both physiological signal patterns and patient clinical characteristics. The proposed framework therefore provides a promising approach for developing intelligent decision-support systems that assist healthcare professionals in early cardiovascular risk identification and personalized patient management.
Cardiovascular disease prediction, Multimodal medical data, Electrocardiogram (ECG) analysis, Deep learning, Electronic health records (EHR), Multimodal feature fusion
A Federated Self-Supervised Learning Framework for Privacy-Preserving Malware Detection in Distributed Edge Environments
The rapid growth of distributed edge computing environments, including Internet-of-Things (IoT) devices, mobile platforms, and enterprise endpoints, has significantly increased the exposure of modern systems to sophisticated malware attacks. Traditional malware detection systems typically rely on centralized machine learning models that require large-scale labeled datasets and the aggregation of raw telemetry data, which raises serious privacy concerns and limits deployment in distributed environments. To address these challenges, this study proposes a Federated Self-Supervised Learning Framework for Privacy-Preserving Malware Detection across Distributed Edge Devices. The proposed approach integrates self-supervised behavioral representation learning with federated collaborative training, enabling edge devices to learn robust malware detection models without sharing sensitive data. A multi-view feature representation strategy is employed to capture diverse malware characteristics derived from system behavior, application attributes, and network activity patterns. The framework further incorporates heterogeneity-aware federated aggregation and prototype-based anomaly detection to improve robustness under non-identically distributed data conditions. Experimental evaluation conducted on publicly available malware datasets demonstrates that the proposed framework significantly outperforms conventional baseline models. The proposed method achieves an accuracy of 97.2% and an F1-score of 0.968, compared with 94.7% and 0.939 achieved by the deep neural network baseline. ROC analysis further confirms superior detection capability with an AUC of 0.99. These results indicate that the proposed framework provides an effective and privacy-preserving solution for collaborative malware detection in heterogeneous edge environments while improving resilience against emerging cyber threats.
Malware Detection, Federated Learning, Self-Supervised Learning, Edge Computing Security, Privacy-Preserving Machine Learning, Distributed Cybersecurity Systems
Classification Models and Diagnostic Prediction for Detection of Kidney Disease Using Machine Learning Methods
Chronic Kidney Disease (CKD) is a progressive and potentially fatal condition that often goes undetected in its early stages, resulting in delayed treatment and increased healthcare costs. Conventional diagnostic approaches rely heavily on laboratory tests and expert interpretation, which may be time-consuming, costly, and prone to inaccuracies due to missing or imbalanced clinical data. To address these limitations, this study proposes a robust and interpretable machine learning–based framework for the early prediction of CKD. The proposed system is evaluated using a publicly available CKD dataset containing clinical and laboratory attributes such as serum creatinine, blood urea, blood pressure, albumin, hemoglobin, and diabetes status. To enhance data quality, missing values are handled using median and most-frequent imputation, while normalization and categorical encoding ensure consistency across features. Class imbalance is mitigated using the Synthetic Minority Over-sampling Technique (SMOTE). Feature dimensionality is reduced using Principal Component Analysis (PCA), and SHAP (Shapley Additive Explanations) is employed to identify the most influential biomarkers and improve model interpretability. Multiple machine learning models, including Logistic Regression, Random Forest, XGBoost, and Artificial Neural Networks (ANN), are trained and evaluated. A novel Logarithmic Ensemble model is introduced to combine the strengths of individual classifiers. Experimental results demonstrate that the proposed ensemble model achieves superior performance, with an accuracy of 97.45%, precision of 99.57%, recall of 95.30%, F1-score of 97.39%, and an AUC-ROC score of 0.9986, outperforming all baseline models. The findings indicate that ensemble learning combined with effective preprocessing and explainability techniques significantly enhances CKD prediction. This approach offers a reliable, interpretable, and scalable solution for early diagnosis, supporting timely clinical intervention and improved patient outcomes.
Logistic Regression, XGBoost, Random Forest, ANN, SMOTE, PCA, Logarithmic Ensembling.
Copyright (c) 2023 M. Susmitha, B.Venkata Seshu Kumari (Author) https://creativecommons.org/licenses/by-nc/4.0
Finite Element Analysis of Micro-Scale Contact Mechanics in Rough Surface Tribological Systems
This study examines numerical simulations of advanced contact mechanics in tribological systems in the micro-scale. The study modeled with the help of the finite element modeling (FEM) examines the linkages between normal load, real contact area, frictional forces, and maximum contact pressure in rough-surface systems. The simulations show that there is non-linear development of the real contact area as the normal force is continued to be applied because the surface asperities are deformed. The linear dependence between frictional forces and normal load is in agreement with the law of friction due to Coulomb and confirms the classic models of friction on the micro-level. Interestingly, maximum contact pressure declines with increasing load which illustrates how roughness on the surface can help in the distribution of stress and to alleviate pressure concentration. These results are of immense implication to the engineering and manufacturing of the micro-electromechanical systems (MEMS), and friction and wear are important characteristics in the reliability of the system. The research also addresses constraints and ways in which the research can be improved in future studies by inclusion of elastic-plastic transitions and environmental impact. The findings, in general, are useful in the understanding of the processes in micro-scale contacts, which contributes to the optimization of micro-scale tribological apparatus.
Micro-scale tribology, Contact mechanics, Finite element modeling (FEM), Surface roughness, Frictional forces, Maximum contact pressure
Secure Blockchain-Based Consumer Electronics Platform for Smart Homes with Efficient Access Control and Performance Evaluation
Blockchain technology has emerged as a pivotal solution in contemporary digital ecosystems, addressing challenges ranging from supply chain management to the preservation of data integrity. One prominent application domain is consumer electronics, where smart homeowners increasingly rely on interconnected devices to access services in a seamless and efficient manner. While these technologies significantly enhance convenience and quality of life, they also introduce critical security and privacy concerns due to data sharing, unauthorized access, and potential cyberattacks. Addressing these challenges requires robust and reliable security mechanisms that ensure trust and transparency. This study proposes a smart and secure blockchain-based framework tailored for consumer electronics platforms, with a primary focus on identifying potential security threats and mitigating them through appropriate blockchain solutions. A set of practical case studies is presented to demonstrate how blockchain can enhance secure service access while maintaining data authenticity and resistance to tampering. The proposed framework is implemented and evaluated using a private Geth network, enabling controlled experimentation and performance analysis. Performance evaluation primarily focuses on the time required for data loading, retrieval, and block access operations. Experimental results indicate that the proposed Blockchain-Based Consumer Electronics Store (B2CES) exhibits a steady and linear increase in processing time as the number of items scales from 1 to 500, ranging from 0.10 seconds to 8.60 seconds. This performance demonstrates improved efficiency when compared to existing approaches, which exhibit a sharper increase from 0.38 seconds to 9.29 seconds for similar operations. Furthermore, block access time for a set of 500 items is reduced to 7.12 seconds in the proposed framework, compared to 9.29 seconds in conventional implementations. These results confirm the effectiveness of the proposed blockchain solution in achieving enhanced security with reduced computational overhead.Blockchain technology, Consumer electronics, Smart homes, Data security, Access control, Performance evaluation.
Blockchain technology, Consumer electronics, Smart homes, Data security, Access control, Performance evaluation.
Sparse Coding–Driven Deep Learning for Robust Emotional State Recognition from Multichannel Speech
Human emotions can be read from a person's face, words, actions (gesture/posture), or even their heart rate. Due to recent advancements in Machine Learning and data fusion, we can now equip computers with the ability to comprehend, identify, and evaluate human sentiment. Emotional state recognition and Stress disorder diagnosis from speech signals have both been concerns for the recent decade. An increasingly useful computer-aided method for identifying emotional disorders is emotion recognition based on multichannel neurophysiologic inputs, a difficult pattern recognition challenge. Correlation information between channels and frequency components is underutilized by conventional fusion techniques. This paper reveals that deep neural networks trained on emotion data can align with prior domain knowledge and acquire representations that are more accurate than those obtained using hand-crafted techniques. Emotional state identification was the focus of this dissertation, which develops the proposed model named Sparse Coding Technique-Deep Learning (SCT-DL) network models. This is done through two methods named the Convolutional-Recurrent Neural Network (CR-NN) which is a deep learning model that can extract task-related characteristics, extract correlated data between channels and incorporate the contextual information gained from this analysis. Due to the complexity of deep belief networks, limited data sets such as the voice database are incompatible with this type of model. Hence the second method named Knowledge Transmission (KT) which is implemented to deal with the issue of limited data. The purpose is to enhance learning by drawing information from multiple source tasks and applying it to a single target activity. The proposed models have statistically and experimentally been proven to be more effective than most state-of-the-art techniques currently available for recognizing emotional states.
Sparse coding, Deep Learning, Neural network, CNN, KT Emotion recognition
Multimodal Deep Learning Framework for Dynamic Student Engagement Recognition in Online Education
Online education has become a regular practice in today’s education. Student engagement is a key factor that impacts the learning outcomes in online education. However, accurate detection of student’s engagement remains challenging due to its reliance on limited modalities, linear feature representations, and static classification models. Existing vision only methods achieved moderate success but failed to capture the multi-dimensional and dynamic nature of engagement. This study presents a robust framework, that integrates multiple modalities, including visual cues such as facial landmarks, gaze, and expressions, audio features such as MFCCs, prosody, and pauses, and physiological signals such as EEG, where available, and interaction behaviors such as keystrokes and mouse activity. Non-linear feature embeddings are generated using autoencoders to preserve complex dependencies, while temporal deep networks, such as Long Short-Term Memory (LSTM) and Transformers, are employed to capture sequential variations in engagement across time. The proposed model is evaluated on four benchmark datasets, DAiSEE, EmotiW, SEED-IV, and IIITB Online SE and achieves an overall accuracy of 96.8%, precision of 95.7%, recall of 95.4%, and F1-score of 95.5%. Compared to state-of-the-art vision only baselines whose accuracy is only 94%, our approach demonstrates an improvement of 2.5–3%, with notable gains in minority engagement classes. The outcomes substantiates that this approach strengthens robustness, reduces bias, and enhances generalization across various learning environments. This framework establishes a scalable foundation for monitoring real-time engagement in adaptive e-learning platforms, intelligent tutoring systems, and online classroom analytics.
multi-dimensional, autoencoders, Non-linear features, MFCCs, EEG, LSTM, Transformer
Attention-Based Autoencoder for Anomaly Detection in Privacy-Preserving DNS Traffic
The rapid adoption of privacy-preserving DNS protocols, such as DNS over HTTPS (DoH) and DNS over TLS (DoT), has improved the confidentiality of Internet communications by encrypting DNS queries and responses. Although these protocols strengthen user privacy, they also create major challenges for conventional intrusion detection systems, which depend heavily on payload inspection and manually labeled traffic data for identifying malicious activities. As encrypted DNS traffic continues to grow, there is an increasing need for intelligent detection mechanisms capable of identifying anomalies without relying on decrypted content. This study proposes an attention-based autoencoder framework for anomaly detection in encrypted DNS traffic using a self-supervised learning strategy. The proposed model is trained exclusively on benign DNS flows, allowing it to learn normal traffic behavior without requiring labeled attack samples. A Transformer-based autoencoder architecture is employed to capture temporal relationships within DNS flow sequences and reconstruct input patterns from flow-level metadata features. Anomalous behavior is identified through reconstruction errors generated during the decoding process, where higher deviations indicate suspicious traffic patterns. Experimental evaluation is conducted using the ISCX2021 encrypted DNS dataset containing both benign and malicious DNS flows. The proposed framework achieves an accuracy of 93.1%, precision of 91.5%, recall of 89.8%, and an F1-score of 90.6%, outperforming baseline models including PCA, Isolation Forest, and LSTM Autoencoders. In addition, the model attains an AUC score of 0.92 with an average inference latency of 43 ms per DNS flow, demonstrating its suitability for near real-time deployment. The proposed framework provides a scalable and privacy-preserving solution for detecting anomalies in encrypted DNS environments without requiring payload access or extensive manual labeling. Its lightweight and adaptive design makes it suitable for practical deployment in enterprise DNS infrastructures, ISP-level monitoring systems, and edge-based cybersecurity applications
Biohybrid–AI Marine Sensors for Ocean Health Monitoring under Climate Stress: A Systematic Review
Oceans stabilize climate, carbon cycling, and global biodiversity; however, increasing anthropogenic stresses such as warming, acidification, and deoxygenation are harming marine stability. Continuous high-resolution monitoring is essential, yet traditional sensors face issues with biofouling, calibration drift, and low ecological sensitivity. This study aims to review biohybrid marine sensors that integrate biological components with engineered materials and artificial intelligence models for adaptive, long-term ocean health monitoring. A systematic review was conducted in IEEE Xplore, ScienceDirect, SpringerLink, MDPI, and Scopus (2010-2025) based on PRISMA-2020. Research was categorized into biological detection, artificial intelligence, architecture, and material and functional engineering, as well as climate-stress systems. The most sensitive and stable microbial and biofilm-based sensors, along with ecologically specific algal and coral environments, were highlighted. Models using hybrid CNN-LSTM and reinforcement learning achieved 15-25 percent higher interpretive accuracy. Nanostructured composite materials made from polymers and carbon improved biocompatibility and antifouling capabilities by over 20 times. Biohybrid AI-enabled sensors show great potential for autonomous and adaptive ocean sensing, but challenges remain regarding biological viability, standardization, energy independence, and ethical use.
AI-driven sensing, Biohybrid sensors, Climate resilience, Marine ecosystems, Ocean health monitoring, Smart biomaterials.
Design and Development of Bio-orthogonal Feedback Loops for Synthetic Cell Memory Systems
Synthetic memory systems allow engineered cells to store information across generations and find applications in biosensing, diagnostics, and biocomputational. However, existing designs (such as toggle switches and CRISPR-based recorders) have limitations, including short retention times, irreversibility, and biosafety issues requiring genome-wide modifications. These limitations hinder the development of reliable and scalable systems. This study addresses these challenges by proposing a bio-orthogonal feedback loop-based synthetic memory model that combines orthogonal ligand-receptor signalling with dual feedback regulation. The goal is to achieve long-term stability, fault-tolerant switching, noise damping, reduced metabolism, and the maintenance of system reversibility and scalability. The research design integrates theoretical and experimental approaches. A detailed mathematical representation (Eqns. 1-13) was developed to explain the activation of the ligand, feedback, biostability, toggle behaviour, recombinase-mediated switching, and metabolic load. Simulations were conducted using both deterministic and stochastic methods, and empirical validation was performed with microbial chassis using fluorescence assays, flow cytometry, lineage tracking, and growth rate analysis. The findings demonstrated retention of 38-40 generations, a switching probability exceeding 90%, noise resistance (NR = 0.84), and a reduced metabolic load of 14.6%. Additionally, the framework achieved multi-bit fidelity of 91% in four states, which is favourable compared to classical memory systems. In conclusion, the proposed framework offers a scalable, reversible, and biosafe synthetic cell memory architecture for developing programmable bio computation and biosensing applications in synthetic biology.
Bio-orthogonal Feedback Loops, Genetic Toggle Switches, Multi-bit Memory, Noise Robustness, Synthetic Biology, Synthetic Cellular Memory
A Quantum-Inspired Framework for Assessing Volatility, Divergence, and Collapse in Public Opinion
The use of opinion polling to gauge societal preferences is not new, but social media has disrupted assumptions of stability and representativeness. Viral moments, fake news, and rapid shifts in sentiment due to quick online communication are volatile, divergent, and unpredictable, which traditional polling methods do not capture. Classical survey methods assume consistent opinions over time, leading to discrepancies between poll forecasts and real-time discussions. Additionally, the polling process itself can influence respondents' behavior. These challenges necessitate new models that account for instability, uncertainty, and context dependence. This paper presents a quantum-inspired model that views public opinion as probabilistic states that can collapse upon measurement, reflecting the inherent instability of digital political discourse. The study evaluates variables using Indian Politics Tweets and Reactions data, employing opinion dynamics based on three metrics: the Opinion Volatility Index (OVI), the Poll Divergence Metric (PDM), and the Stability Collapse Indicator (SCI). It integrates data preprocessing, network-based opinion diffusion modeling, and measurement disturbance to assess volatility and divergence. Results indicate that volatility is inherent to online discourse: OVI shows peaks during viral events, PDM reveals widening gaps between polls and social media signals, and SCI captures events where small shocks lead to significant opinion shifts. These findings suggest that the stability of polls is weakening in networked environments. The quantum-inspired approach provides a theoretical and methodological framework for rethinking opinion measurement, opening new avenues in computational political science.
Collapse Events, Measurement Disturbance, Opinion Dynamics, Poll Stability, Quantum Social Science, Social Media Volatility
A Survey on Retrieval-Augmented Generation (RAG) and Hybrid Information Retrieval for Large Language Models
Retrieval-Augmented Generation (RAG) is a popular approach that enhances large language models (LLMs) by grounding their outputs in external evidence rather than relying solely on fixed model parameters. RAG enables dynamic access to relevant information during inference through sparse, dense, and hybrid retrieval methods. Hybrid retrieval, which combines the advantages of sparse techniques with the richness of dense representations, overcomes the limitations of single retriever systems. RAG addresses two major weaknesses of existing LLMs: hallucination and static knowledge. Utilizing current, testable information reduces factual inconsistencies and increases the trustworthiness of knowledge-intensive work. This survey makes three contributions. First, it presents a complete taxonomy of RAG systems based on retriever type, integration approach, retrieval granularity, application field, and optimization method. Second, it offers a comprehensive comparative review of the literature on current surveys, powerful models, benchmark datasets, and evaluation metrics. Third, it outlines open challenges, such as scalability, latency, multimodal retrieval, and evaluation inconsistencies, and suggests areas for future research. To our knowledge, this is the first comprehensive survey that systematically combines taxonomy, comparative evaluation, and open research issues in RAG and hybrid information retrieval. It provides a balanced perspective for both academics and practitioners aiming to develop the next generation of reliable, efficient retrieval-enhanced generative systems
Hybrid Information Retrieval, Large Language Models (LLMs), Multimodal Retrieval, Retrieval-Augmented Generation (RAG), Survey, Taxonomy
AI-Designed Microenvironment Scaffolds for Predictable Organoid Morphogenesis and Functional Reliability
Organoids have emerged as robust three-dimensional systems for analyzing human development, disease, and treatment therapies. However, morphogenesis and functional outcomes vary significantly, and this high variability hinders their widespread application, particularly due to a lack of control over microenvironmental scaffolds. Traditional scaffold design methods, whether empirical, physics-based, or involving limited AI application struggle to provide predictive and reproducible guidance. Research indicates that AI-based scaffold design can enhance the predictability of organoid morphogenesis and functional reproducibility. The model integrates various modalities, including transcription, imaging, and biomechanical datasets. Generative adversarial networks were utilized to propose new scaffold designs, while physics-informed constraints ensured biological feasibility. Surrogate models were trained to predict scaffold-organoid interactions, and reinforcement learning was employed to iteratively improve scaffold configurations based on surrogate predictions. Comparative analysis was conducted against empirical, unimodal, generative-only, and physics-only baselines. The proposed framework reduced morphological variation by more than half compared to empirical baselines, achieving a structural similarity index (SSIM) of 0.89 and a structural functional reproducibility represented by an intra-class correlation coefficient (ICC) of 0.81. Surrogate models demonstrated a 50% reduction in mean squared error compared to unimodal models, while reinforcement learning resulted in a 35-fold increase in cumulative reward, indicating greater optimization efficiency. This research integrates multimodal fusion, generative modeling, and reinforcement learning with physics-informed constraints, establishing it as a pioneering study in predictive and adaptive scaffold design. The results advance organoid engineering, making it more reproducible, scalable, and clinically applicable.
Organoids, Artificial Intelligence, Scaffold Design, Reinforcement Learning, Multimodal Data Fusion, Regenerative Medicine
Copyright (c) 2025 Tom Yeh, Paul S Pang, S JanReddy, M. Harshini (Author) https://creativecommons.org/licenses/by-nc/4.0
Transfer Learning for Agile Pedestrian Dynamics Analysis Enabling Real-Time Safety at Zebra Crossings
Ensuring pedestrian safety at zebra crossings is a critical challenge in smart urban mobility, where unpredictable human behaviours and limited sensing capabilities often lead to accidents. Traditional surveillance systems lack the intelligence to interpret pedestrian intent in real time, especially in rapidly changing environments. This study aims to develop an efficient, deployable framework for real-time pedestrian behaviour classification using transfer learning techniques and temporal modelling. The proposed model integrates a convolutional neural network (CNN) with a Long Short-Term Memory (LSTM) network to extract spatial-temporal features from video sequences. Pre-trained CNNs (ResNet50 and MobileNetV2) are fine-tuned on the PIE (Pedestrian Intention Estimation) dataset, which provides over 6 hours of annotated pedestrian videos. Data augmentation and histogram normalization are applied during pre-processing, and the model is optimized using the Adam optimizer with early stopping and learning rate scheduling. Real-time deployment feasibility is tested on edge hardware (NVIDIA Jetson Nano) using TensorRT. Experimental results show that the proposed CNN-LSTM model achieves an accuracy of 92.7% and an F1-score of 0.89, outperforming baseline CNN (85.2%) and Transformer-based (91.1%) models. The system maintains inference speed of 28 FPS on Jetson Nano, with behaviour-wise F1-scores of 0.92 (safe crossing), 0.87 (hesitant), 0.85 (distracted), and 0.89 (sudden entry). A pie-chart analysis reveals that CNN computation accounts for 42% of inference time, indicating efficient design. The framework demonstrates strong potential for deployment in smart poles and connected traffic infrastructure, enabling timely pedestrian risk alerts and enhancing safety in real-world urban environments.
Pedestrian safety, transfer learning, CNN-LSTM, zebra crossings, smart transportation, real-time inference, edge computing, PIE dataset
Attention-Based Cross-Modal CT–MRI Deep Learning for Brain Tumor Classification
Accurate prediction of brain tumor progression is critical for timely diagnosis and treatment planning. Traditional unimodal diagnostic systems, relying solely on CT or MRI scans, often fail to capture the complete pathological profile due to their limited semantic scope. This study proposes a cross-modal deep learning framework that integrates CT and MRI imaging data to enhance the classification and prediction of brain tumors. We utilized the publicly available Brain Tumor Multimodal Image dataset from Kaggle, comprising over 3,000 CT and MRI scans labelled across glioma, meningioma, and pituitary tumor types. The framework employs modality-specific encoders (ResNet34 for CT and VGG16 for MRI), followed by a gated attention-based fusion layer and supervised contrastive learning to align latent representations. The final fused embeddings are classified through a fully connected softmax layer. Extensive preprocessing, augmentation, and 5-fold cross-validation were employed for robustness. The proposed model achieved a classification accuracy of 93.7%, F1-score of 0.94, and AUC-ROC of 0.97, outperforming baseline unimodal and naive fusion approaches by margins of 6–10%. It also maintained strong performance across diverse age groups and showed stable generalization across folds. The framework demonstrates significant improvements in diagnostic accuracy, reliability, and computational efficiency, making it suitable for real-time deployment in hospital PACS systems and telemedicine platforms. Its modular design allows for future integration with genomic data and clinical reports, establishing a foundation for scalable, interpretable, and patient-specific AI in neuro-oncology.
Cross-Modal Learning, Brain Tumor Classification, CT and MRI Fusion, Deep Learning, Contrastive Learning, Attention Mechanism, Medical Imaging AI, Real-Time Diagnosis, Multimodal Fusion, Clinical Decision Support.
Copyright (c) 2024 https://creativecommons.org/licenses/by-nc/4.0
Personalized Cancer Treatment Recommendation Using Federated Graph Neural Networks Across Healthcare Institutions
Personalized cancer treatment requires access to diverse and large-scale patient data to train accurate predictive models. However, privacy regulations and data silos across healthcare institutions significantly hinder the development of collaborative, generalizable AI-driven clinical decision support systems. This study aims to develop a federated graph neural network (Fed-GNN) framework that enables privacy-preserving, multi-institutional collaboration for personalized cancer treatment recommendation. The proposed framework models patients as graph nodes and constructs inter-patient similarity graphs using genomic, clinical, and therapeutic data from the BRCA Multi-Omics TCGA dataset. Each institution trains a local GNN on its private patient graph and shares only encrypted model parameters with a central server, where global aggregation is performed using the FedAvg algorithm. A local personalization layer further fine-tunes the global model to reflect institution-specific patient characteristics. Experiments conducted on a horizontally partitioned dataset across four simulated institutions show that the proposed Fed-GNN achieves an accuracy of 92.3%, precision of 0.91, recall of 0.93, and AUC-ROC of 0.94, outperforming centralized GNN, federated MLP, and standalone models. The framework demonstrates resilience under non-IID settings and client dropout scenarios, maintaining >87% accuracy even with 20% client unavailability. The Fed-GNN framework effectively enables collaborative, secure, and high-performing cancer treatment recommendation across distributed healthcare networks. This approach paves the way for real-world deployment of federated AI in clinical oncology, balancing predictive accuracy with patient data confidentiality
Federated Learning, Graph Neural Networks, Personalized Medicine, Cancer Treatment Recommendation, Privacy-Preserving AI, Clinical Decision Support, BRCA Multi-Omics, AUC-ROC, Federated Aggregation, Multi-Institution Collaboration.
Dynamic Optimization in UAV-based Ad Hoc Networks (FANETs) for Real-Time Mission Routing using Quantum Walk Search Algorithms
Unmanned Aerial Vehicles (UAVs) forming Flying Ad Hoc Networks (FANETs) are increasingly deployed in mission-critical applications such as disaster response and autonomous surveillance. However, traditional routing protocols often fail to maintain reliable and low-latency communication under the highly dynamic and probabilistic nature of FANET topologies, resulting in degraded performance in real-time scenarios. This study aims to develop a dynamic routing framework for FANETs that leverages Discrete-Time Quantum Walk (DTQW) algorithms to optimize mission paths in probabilistic graph-based network models. The network is modeled as a time-varying probabilistic graph , where link reliability is continuously updated based on UAV mobility and signal metrics. A quantum optimizer evaluates routing paths in superposition, exploiting parallelism to identify high-probability routes efficiently. This optimizer is integrated with the FANET routing layer to adaptively select optimal paths in real-time. Simulation results demonstrate that the proposed QW-Routing protocol significantly outperforms classical protocols such as AODV, DSR, and Random Walk Routing. Specifically, QW-Routing achieves a packet delivery ratio of 91.3%, reduces end-to-end latency by 22.4%, lowers energy consumption by 15%, and improves path stability by 39% compared to DSR under high-mobility conditions. The proposed framework introduces a scalable, low-overhead, and highly adaptive routing solution for FANETs, with direct applicability in next-generation autonomous aerial networks, where robust communication under uncertainty is paramount.
Quantum Walk Routing, FANETs, UAV Networks, Probabilistic Graphs, Dynamic Optimization, Mission-Critical Communication, Real-Time Routing
Real-Time Cognitive Load Estimation in Augmented Reality Interfaces Using EEG-Driven Adaptive Algorithms
Augmented reality (AR) systems increasingly support complex decision-making and user interaction, but high visual density often results in cognitive overload, reducing performance and increasing fatigue. Traditional methods for estimating cognitive load are either retrospective or too invasive for seamless real-time adaptation. This study aims to develop a real-time, EEG-driven cognitive load estimation model integrated with an adaptive AR interface to dynamically adjust content based on user mental effort. A hybrid CNN–LSTM deep learning architecture was trained on the STEW EEG dataset using band-specific power spectral density and Hjorth features extracted from preprocessed signals. The model was evaluated using subject-wise 5-fold cross-validation and deployed in an AR environment using TensorFlow Lite on Microsoft HoloLens 2. Real-time inference was achieved with a latency of 24.5 ms and a deployable model size of 9.2 MB. The proposed model achieved 90% classification accuracy and a macro-averaged F1-score of 89%, outperforming SVM, Random Forest, and CNN baselines. Usability tests in AR showed a 31.7% reduction in NASA-TLX scores, a 25.4% decrease in task error rates, and a 14.8-second improvement in task completion time using multi-class adaptive UI. This research demonstrates that real-time EEG-based cognitive estimation can significantly enhance user experience in AR systems. The proposed framework offers a scalable and efficient solution for neuroadaptive interfaces in training, healthcare, and industrial applications
Cognitive Load Estimation, EEG, Augmented Reality, CNN–LSTM, Adaptive Interface, Brain–Computer Interface, Real-Time Classification, Usability Evaluation.
Transformer-Based Prediction of CRISPR Off-Target Effects Using Pan-Genomic Embeddings Across Species
The CRISPR-Cas9 genome editing system has transformed biomedical and agricultural research by enabling precise genetic modifications. However, its off-target effects—unintended edits at genomic loci with partial sequence similarity—remain a major safety concern, particularly in therapeutic and cross-species applications. Accurate prediction of off-target cleavage sites is essential to mitigate these risks. This study aims to develop a generalizable and interpretable model for predicting CRISPR-Cas9 off-target effects across multiple species using Transformer-based sequence modeling combined with pan-genomic embeddings. We introduce a novel framework that utilizes k-mer tokenization and unsupervised FastText-based embeddings trained on concatenated multi-species genomes to represent gRNA and candidate target sites. These embeddings are fed into a custom Transformer encoder that captures contextual nucleotide dependencies and cross-sequence interactions. The model is evaluated on curated datasets comprising ~150,000 labeled CRISPR off-target events from human, mouse, zebrafish, and Arabidopsis genomes. Evaluation metrics include AUPRC, F1-score, and AUROC under both stratified 5-fold cross-validation and leave-one-species-out protocols. The proposed model achieves an AUPRC of 0.768 in cross-validation and demonstrates up to 15% improvement in generalization on unseen species compared to DeepCRISPR and DeepSpCas9 baselines. Statistical tests confirm the significance of performance gains (p < 0.01). This research contributes a robust cross-species prediction tool, offering improved safety insights for genome editing applications in non-human systems, and establishes a scalable methodology for embedding-driven CRISPR modeling.
CRISPR-Cas9, off-target prediction, Transformer model, pan-genomic embeddings, cross-species genomics, guide RNA, sequence modeling