Search Architecture Research Articles

GenoSiS: A Biobank-Scale Genotype Similarity Search Architecture for Creating Dynamic Patient-Match Cohorts.

Many patients do not experience optimal benefits from medical advances because clinical research does not adequately represent them. While the diversity of biomedical research cohorts is improving, ensuring that individual patients are adequately represented remains challenging. We propose a new approach, GenoSiS, which leverages machine learning-based similarity search to dynamically find patient-matched cohorts across different populations quickly. These cohorts could serve as reference cohorts to improve a range of clinical analyses, including disease risk score calculations and dosage decisions. While GenoSiS focuses on finding genetic similarity within a biobank, our similarity search architecture can be extended to represent other medically relevant patient characteristics and search other biobanks.

Context-Aware Search for Environmental Data Using Dense Retrieval

The search for environmental data typically involves lexical approaches, where query terms are matched with metadata records based on measures of term frequency. In contrast, dense retrieval approaches employ language models to comprehend the context and meaning of a query and provide relevant search results. However, for environmental data, this has not been researched and there are no corpora or evaluation datasets to fine-tune the models. This study demonstrates the adaptation of dense retrievers to the domain of climate-related scientific geodata. Four corpora containing text passages from various sources were used to train different dense retrievers. The domain-adapted dense retrievers are integrated into the search architecture of a standard metadata catalogue. To improve the search results further, we propose a spatial re-ranking stage after the initial retrieval phase to refine the results. The evaluation demonstrates superior performance compared to the baseline model commonly used in metadata catalogues (BM25). No clear trends in performance were discovered when comparing the results of the dense retrievers. Therefore, further investigation aspects are identified to finally enable a recommendation of the most suitable corpus composition.

Reinforcement-Learning-Based Multi-UAV Cooperative Search for Moving Targets in 3D Scenarios

Most existing multi-UAV collaborative search methods only consider scenarios of two-dimensional path planning or static target search. To be close to the practical scenario, this paper proposes a path planning method based on an action-mask-based multi-agent proximal policy optimization (AM-MAPPO) algorithm for multiple UAVs searching for moving targets in three-dimensional (3D) environments. In particular, a multi-UAV high–low altitude collaborative search architecture is introduced that not only takes into account the extensive detection range of high-altitude UAVs but also leverages the benefit of the superior detection quality of low-altitude UAVs. The optimization objective of the search task is to minimize the uncertainty of the search area while maximizing the number of captured moving targets. The path planning problem for moving target search in a 3D environment is formulated and addressed using the AM-MAPPO algorithm. The proposed method incorporates a state representation mechanism based on field-of-view encoding to handle dynamic changes in neural network input dimensions and develops a rule-based target capture mechanism and an action-mask-based collision avoidance mechanism to enhance the AM-MAPPO algorithm’s convergence speed. Experimental results demonstrate that the proposed algorithm significantly reduces regional uncertainty and increases the number of captured moving targets compared to other deep reinforcement learning methods. Ablation studies further indicate that the proposed action mask mechanism, target capture mechanism, and collision avoidance mechanism of the AM-MAPPO algorithm can improve the algorithm’s effectiveness, target capture capability, and UAVs’ safety, respectively.

The next-gen federated search architecture for biomedical knowledge repositories — The LIT-FED-SEARCH engine

The primary objective of LIT-FED-SEARCH software is to develop a user-friendly solution tailored to researchers and scientists. This solution aims to enhance their impact by facilitating the analysis of data from modern, extensive datasets like PubMed and Clinical Trials, alongside real-world evidence. The central concept we offer is a Federated Search Workflow Engine, which has been designed and maintained to accommodate various infrastructure configurations for the convenience of users. In line with this approach, potential users have the flexibility to configure their own computing environment and a set of interesting data repositories, based on their specific requirements and capabilities. This customization can significantly reduce the time and resources invested in research. LIT-FED-SEARCH is constructed with the support of OpenSearch full-text search engine as its heart. This paper offers an overview of the system’s architecture, capabilities, and potential applications in the field of biomedical research.

A novel re-ranking architecture for patent search

Patent search presents unique challenges due to the intricate structure and specialized terminology embedded in patent documents. While neural models have been successfully applied in various information retrieval (IR) tasks, these inherent complexities have hindered their effectiveness in patent search. To address these challenges, we propose a novel re-ranking architecture that effectively handles long, structured patent documents and leverages AI models to interpolate lexical and semantic signals of relevance. Additionally, the architecture incorporates query-specific weights for the final re-ranking process. To address partial relevance between patent sections our method effectively models the relevance relationships between different sections of patent documents. We calculate lexical and semantic signals of relevance from each document section and feed them as input features to AI models that estimate a combined relevance score. Finally, we compute query-specific weights to determine the relative contributions of lexical and semantic relevance for the final re-ranking. Extensive experiments on the CLEF-IP dataset demonstrate that our method outperforms several baselines, achieving substantial and statistically significant improvements in retrieval performance. We further assess the adaptability of our method using the MSMARCO dataset, where it exhibits limited performance, indicating its suitability for domain-specific patent research.

A Hierarchical Heuristic Architecture for Unmanned Aerial Vehicle Coverage Search with Optical Camera in Curve-Shape Area

This paper focuses on the problem of dynamic target search in a curve-shaped area by an unmanned aerial vehicle (UAV) with an optical camera. Our objective is to generate an optimal path for UAVs to obtain the maximum detection reward by a camera in the shortest possible time, while satisfying the constraints of maneuverability and obstacle avoidance. First, based on prior qualitative information, the original target probability map for the curve-shaped area is modeled by Parzen windows with 1-dimensional Gaussian kernels, and then several high-value curve segments are extracted by density-based spatial clustering of applications with noise (DBSCAN). Then, given an example that a target floats down river at a speed conforming to beta distribution, the downstream boundary of each curve segment in the future time is expanded and predicted by the mean speed. The rolling self-organizing map (RSOM) neural network is utilized to determine the coverage sequence of curve segments dynamically. On this basis, the whole path of UAVs is a successive combination of the coverage paths and the transferring paths, which are planned by the Dubins method with modified guidance vector field (MGVF) for obstacle avoidance and communication connectivity. Finally, the good performance of our method is verified on a real river map through simulation. Compared with the full sweeping method, our method can improve the efficiency by approximately 31.5%. The feasibility is also verified through a real experiment, where our method can improve the efficiency by approximately 16.3%.

A novel transfer learning-based model for diagnosing malaria from parasitized and uninfected red blood cell images

Malaria represents a potentially fatal communicable illness triggered by the Plasmodium parasite. This disease is transmitted to humans through the bites of Anopheles mosquitoes that carry the infection. This disease has significant and devastating consequences on the health systems of fragile countries, particularly in sub-Saharan Africa. Malaria affects red blood cells by invading and replicating within them, destroying them, and releasing toxic byproducts into the bloodstream. The parasite’s ability to stick and modify the surface of red blood cells can cause them to become sticky, obstructing blood flow in vital organs such as the brain and spleen. Therefore, efficient approaches for the early detection of malaria are critical to saving patients’ lives. The main aim of this study is to develop an efficient model for early malaria diagnosis. We used malaria images based on parasitized and uninfected red blood cells for the study experiments. We applied neural network-based approaches such as Neural Search Architecture Network (NASNet) and compared its performance with machine learning techniques. Moreover, we proposed a novel NNR (NASNet Random forest) method for feature engineering. The proposed NNR approach first extracts spatial features from input malaria images, then class prediction probability features are extracted from these spatial features. The feature set obtained from the data extraction trains machine learning models. Our comprehensive experiments show that the support vector machine outperformed state-of-the-art models, achieving a high-performance score of 99% and having an inference time near 0.025 s. We validated the performance using k-fold cross-validation and optimized the hyperparameters through tuning. Our proposed research has improved the early diagnosis of malaria and can assist medical specialists in reducing the mortality rate.

Toward Forward and Backward Private Dynamic Searchable Symmetric Encryption Supporting Data Deduplication and Conjunctive Queries

In Internet of Things (IoT) application scenarios such as intelligent logistics, secure data access control and sharing based on dynamic searchable symmetric encryption (DSSE) has become a research hotspot in recent years. DSSE is an encryption technology which gratifies the above requirements, while protecting the sensitive data during the operation. The existing DSSE schemes do not achieve data deduplication and conjunctive queries concurrently, and exist problems such as complex update process, inflexible query method and privacy disclosure, etc. For this purpose, the paper proposes dynamic searchable symmetric encryption for data deduplication and conjunctive queries (DSSE-DC), and constructs a concrete DSSE-DC scheme. Our scheme uses symmetric encryption with homomorphic addition and bitmap index to develop a secure and reliable search architecture, and updates through modular addition. Meanwhile, we add a deduplication mechanism to realize data deduplication which avoids the waste of cloud storage resources. Furthermore, we introduce the idea of inner product matching to achieve efficient conjunctive queries. The adaptive security of DSSE-DC scheme is proved in the random oracle model. Whilst our scheme satisfies forward and backward privacy. In comparison to the existing DSSE schemes for conjunctive queries, our scheme has advantage in update and search performances, which is applicable to IoT applications like intelligent logistics.

A look-up table-based processing-in-SRAM architecture for energy-efficient search applications

This paper presents an efficient in-memory computing architecture for search and logic function applications. The proposed design benefits from an SRAM cell, using two single-ended read bit-lines to enable binary and ternary content addressable memories (BCAM and TCAM) and logic functions. As our proposed cell benefits from Schmitt trigger inverters based on independent gate FinFETs. No data distraction occurs due to the half-selection issue. The results indicate no failure in the proposed method in the half-selected cells in the BCAM and TCAM functions. Moreover, the proposed cell shows 58% and 89% lower power in the write and read operations than the conventional SRAM-based IMC design. Also, by providing basic logic operations in the proposed design, applications with high-accuracy requirements can be implemented. Moreover, a neural network is also implemented to assess the proposed design in a practical application. The results show that our design consumes 41% and 50% lower energy than its counterparts.

EPC-DARTS: Efficient partial channel connection for differentiable architecture search

With weight-sharing and continuous relaxation strategies, the differentiable architecture search (DARTS) proposes a fast and effective solution to perform neural network architecture search in various deep learning tasks. However, unresolved issues, such as the inefficient memory utilization, and the poor stability of the search architecture due to channels randomly selected, which has even caused performance collapses, are still perplexing researchers and practitioners. In this paper, a novel efficient channel attention mechanism based on partial channel connection for differentiable neural architecture search, termed EPC-DARTS, is proposed to address these two issues. Specifically, we design an efficient channel attention module, which is applied to capture cross-channel interactions and assign weight based on channel importance, to dramatically improve search efficiency and reduce memory occupation. Moreover, only partial channels with higher weights in the mixed calculation of operation are used through the efficient channel attention mechanism, and thus unstable network architectures obtained by the random selection operation can also be avoided in the proposed EPC-DARTS. Experimental results show that the proposed EPC-DARTS achieves remarkably competitive performance (CIFAR-10/CIFAR-100: a test accuracy rate of 97.60%/84.02%), compared to other state-of-the-art NAS methods using only 0.2 GPU-Days.

ОПТИМИЗАЦИЯ НА ОСНОВЕ ОБЪЕДИНЕНИЯ МОДЕЛЕЙ АДАПТИВНОГО ПОВЕДЕНИЯ РОЯ АГЕНТОВ

A bionic search architecture has been developed to solve the problem of placing VLSI elementsbased on the hybridization of the algorithms of a bee colony and a swarm of chromosomes,which allows you to get out of "local holes" and increases the convergence of the placement algorithm. The initial iterations are implemented by the bee algorithm to provide a broad overview ofthe search area, and the final iterations are implemented by the chromosome swarm algorithm,which ensures the exact localization of the extremum found by the bee algorithm. Agents are representedas a population of chromosomes, which are genotypes for solving the placement problem.The paper describes a modified paradigm of a swarm of chromosomes, which, in contrast to thecanonical method, provides the possibility of searching for solutions in the affine space of positionswith integer values of the parameters. In the search population method of optimization by aswarm of chromosomes, the agents of the population are chromosomes. The chromosome is thegenotype of the optimization object. The essence of the search procedure is the successive changeof the states of the object of optimization (chromosome) by the directed mutation operator and thesearch for the optimal state. An affine-relaxation model (ARM) of a swarm of chromosomes isproposed - this is a graph whose vertices correspond to chromosomes, and arcs correspond toaffine bonds between them. The transition of the chromosome to a new state is carried out using arelaxation procedure. In the work, the directed mutation operator acts as a means of changing thesolution, the essence of which is to change the integer values of genes in the chromosome. Thepurpose of the transition is to reduce the weight of the affine bond between chromosomes. Themechanisms of the directed mutation operator are described. A modified structure of the bee algorithmis proposed. For each base chromosome, a probabilistic choice of a set of chromosomeslocated in the vicinity of the base chromosome is implemented. It is possible to improve the qualityof the developed algorithm by adjusting the values of the control parameters. The time complexityof the algorithm for fixed values of the population size and the number of generations is O(n). Ingeneral, the dependence of the running time of the hybrid algorithm is O(n2) – O(n3).

Research on Military Logistics Data Warehouse

According to military logistics data warehouse dealing with large-scale data effect is poor. This paper puts forward the construction scheme of dual computing engine military logistics data warehouse based on hive in order to improve the military logistics support capability. The scheme adopts Hadoop and Spark dual search architecture to support efficient storage of heterogeneous data. In addition, a data block placement strategy based on correlation analysis was used. This article use the intersection relationship matrix and the concurrency relationship matrix to calculate the correlation between the data block placed on the node and the data block to be placed. The access frequency of the target data block was integrated to select the appropriate node storage. The scheme can effectively improve hive’s query efficiency and provide more effective information aided decision support for commanders.

Efficient and Privacy-Preserving Search Over Edge–Cloud Collaborative Entity in IoT

Limited by the storage and computing capacity of Internet of Things (IoT) devices, outsourcing encrypted entity data has become a prevalent trend. The existing IoT entity search methods lack the integration and utilization of both edge and cloud resources and the protection of user privacy. Besides, the traditional searchable encryption mode is inapplicable to state-time-varying entities in IoT. Therefore, in this article, an edge–cloud collaborative entity search method with privacy protection in IoT is proposed, fusing the advantages of edge and cloud resources to fulfill users’ needs for real-time search and privacy protection. Specifically, a secure search architecture and search method for edge–cloud collaboration is designed to support various needs of users, such as real-time search and global search. Then, an adaptive discrimination method for similar interested entities through attribute analysis and feature extraction is proposed to construct attribute-distinguished encrypted index and query vector groups, enabling efficient entity search meanwhile ensuring fast index update. Simulation results demonstrate that the proposed method with privacy protection can effectively improve the efficiency of entity search in IoT while safeguarding user privacy.

Artificial intelligence fusion for predicting survival of rectal cancer patients using immunohistochemical expression of Ras homolog family member B in biopsy.

The process of biomarker discovery is being accelerated with the application of artificial intelligence (AI), including machine learning. Biomarkers of diseases are useful because they are indicators of pathogenesis or measures of responses to therapeutic treatments, and therefore, play a key role in new drug development. Proteins are among the candidates for biomarkers of rectal cancer, which need to be explored using state-of-the-art AI to be utilized for prediction, prognosis, and therapeutic treatment. This paper aims to investigate the predictive power of Ras homolog family member B (RhoB) protein in rectal cancer. This study introduces the integration of pretrained convolutional neural networks and support vector machines (SVMs) for classifying biopsy samples of immunohistochemical expression of protein RhoB in rectal-cancer patients to validate its biologic measure in biopsy. Features of the immunohistochemical expression images were extracted by the pretrained networks and used for binary classification by the SVMs into two groups of less and more than 5-year survival rates. The fusion of neural search architecture network (NASNet)-Large for deep-layer feature extraction and classifier using SVMs provided the best average classification performance with a total accuracy = 85%, prediction of survival rate of more than 5 years = 90%, and prediction of survival rate of less than 5 years = 75%. The finding obtained from the use of AI reported in this study suggest that RhoB expression on rectal-cancer biopsy can be potentially used as a biomarker for predicting survival outcomes in rectal-cancer patients, which can be informative for clinical decision making if the patient would be recommended for preoperative therapy.

Natural Language Processing with Optimal Deep Learning-Enabled Intelligent Image Captioning System

The recent developments in Multimedia Internet of Things (MIoT) devices, empowered with Natural Language Processing (NLP) model, seem to be a promising future of smart devices. It plays an important role in industrial models such as speech understanding, emotion detection, home automation, and so on. If an image needs to be captioned, then the objects in that image, its actions and connections, and any silent feature that remains under-projected or missing from the images should be identified. The aim of the image captioning process is to generate a caption for image. In next step, the image should be provided with one of the most significant and detailed descriptions that is syntactically as well as semantically correct. In this scenario, computer vision model is used to identify the objects and NLP approaches are followed to describe the image. The current study develops a Natural Language Processing with Optimal Deep Learning Enabled Intelligent Image Captioning System (NLPODL-IICS). The aim of the presented NLPODL-IICS model is to produce a proper description for input image. To attain this, the proposed NLPODL-IICS follows two stages such as encoding and decoding processes. Initially, at the encoding side, the proposed NLPODL-IICS model makes use of Hunger Games Search (HGS) with Neural Search Architecture Network (NASNet) model. This model represents the input data appropriately by inserting it into a predefined length vector. Besides, during decoding phase, Chimp Optimization Algorithm (COA) with deeper Long Short Term Memory (LSTM) approach is followed to concatenate the description sentences produced by the method. The application of HGS and COA algorithms helps in accomplishing proper parameter tuning for NASNet and LSTM models respectively. The proposed NLPODL-IICS model was experimentally validated with the help of two benchmark datasets. A widespread comparative analysis confirmed the superior performance of NLPODL-IICS model over other models.

A Novel Efficient Patient Monitoring FER System Using Optimal DL-Features

Automated Facial Expression Recognition (FER) serves as the backbone of patient monitoring systems, security, and surveillance systems. Real-time FER is a challenging task, due to the uncontrolled nature of the environment and poor quality of input frames. In this paper, a novel FER framework has been proposed for patient monitoring. Preprocessing is performed using contrast-limited adaptive enhancement and the dataset is balanced using augmentation. Two lightweight efficient Convolution Neural Network (CNN) models MobileNetV2 and Neural search Architecture Network Mobile (NasNetMobile) are trained, and feature vectors are extracted. The Whale Optimization Algorithm (WOA) is utilized to remove irrelevant features from these vectors. Finally, the optimized features are serially fused to pass them to the classifier. A comprehensive set of experiments were carried out for the evaluation of real-time image datasets FER-2013, MMA, and CK+ to report performance based on various metrics. Accuracy results show that the proposed model has achieved 82.5% accuracy and performed better in comparison to the state-of-the-art classification techniques in terms of accuracy. We would like to highlight that the proposed technique has achieved better accuracy by using 2.8 times lesser number of features.

Enabling Remote Responder Bio-Signal Monitoring in a Cooperative Human-Robot Architecture for Search and Rescue.

The roles of emergency responders are challenging and often physically demanding, so it is essential that their duties are performed safely and effectively. In this article, we address real-time bio-signal sensor monitoring for responders in disaster scenarios. In particular, we propose the integration of a set of health monitoring sensors suitable for detecting stress, anxiety and physical fatigue in an Internet of Cooperative Agents architecture for search and rescue (SAR) missions (SAR-IoCA), which allows remote control and communication between human and robotic agents and the mission control center. With this purpose, we performed proof-of-concept experiments with a bio-signal sensor suite worn by firefighters in two high-fidelity SAR exercises. Moreover, we conducted a survey, distributed to end-users through the Fire Brigade consortium of the Provincial Council of Málaga, in order to analyze the firefighters' opinion about biological signals monitoring while on duty. As a result of this methodology, we propose a wearable sensor suite design with the aim of providing some easy-to-wear integrated-sensor garments, which are suitable for emergency worker activity. The article offers discussion of user acceptance, performance results and learned lessons.

A recursive reinforced blockchain performance evaluation and improvement architecture: Maximising diversity to improve scalability

SummaryNowadays, Blockchain technology has received widespread attention because of its ability to effectively solve the trust problem in transactions. However, the throughput of crypto currency using Blockchain technology has never been comparable to that of centralized payment institutions. The fundamental reason is the architecture of the Blockchain itself, and the existing methods often focus on the part of the Blockchain but lack the overall control. In this paper, we design, a novel Recursively Reinforced Blockchain Architecture Search (BORAS) architecture, from the perspective of the overall structure of the Blockchain, which can search the various attributes of the various levels in a adaptive manner, aiming at selecting the best configuration in a dynamical environment. First, we introduce a recursive and scalable reinforcement learning framework, to find the optimal block size and block interval of the Blockchain, so as to improve the throughput of the Blockchain system. Second, we add the selection scheme of consensus algorithm to the dynamic space and also use the framework to select the optimal consensus mechanism to realize the optimal configuration of the Blockchain system in the dynamic environment. Third, we adopt a recursive method, which greatly reduces the time complexity required for the optimal configuration of the Blockchain system and improves the configuration efficiency. Comprehensive experiments on three representative Blockchain platforms demonstrate that BOBAS can consistently improve the throughput, while also reducing latency, with remarkable and efficient performances.

How does Integrative Oncology Influence Patients' Physical and Psychosocial Outcomes, and What are Patients, Carers and Healthcare Professionals' Experiences? An Integrative Review.

To identify the relationship between integrative oncology (IO) services on patients' physical and psychosocial outcomes and to explore the experiences of IO among patients, carers and healthcare professionals. This integrative review was reported according to PRISMA guidelines. A search architecture was developed using key words and the following databases were searched: Medline (OVID), EmCare for Nurses (OVID), PsycINFO (OVID); AMED (OVID), CINAHL (EBSCO), Pubmed, the Cochrane Library (CCRT and CDSR) controlled trials databases and ANZ CTR. All articles were assessed according to a pre-determined selection criterion. 426 articles were assessed and 18 were included (4 qualitative, 9 quantitative and 5 mixed methods). Patients reported a reduction in some cancer related symptoms and treatment related side effects. Positive psychosocial impacts were reported such as an increased ability to cope with their cancer diagnosis and treatment. The experiences of healthcare professionals highlighted the importance of a collaborative approach among the Multi-Disciplinary Team (MDT), ongoing education and research to ensure Complementary Integrative Therapies (CIT) were evidence-based. The provision of IO impacts positively on patients' self-reported physical and emotional wellbeing and quality of life at all stages of their cancer experience. Patients reported that IO supported their engagement in their own health and wellbeing by increasing feelings of control and empowerment. However, to successfully integrate CIT with conventional cancer treatments it is imperative that cancer centres adopt a collaborative and evidence-based informed approach to CIT.

Multi-Agent Tree Search with Dynamic Reward Shaping

Sparse rewards and their representation in multi-agent domains remains a challenge for the development of multi-agent planning systems. While techniques from formal methods can be adopted to represent the underlying planning objectives, their use in facilitating and accelerating learning has witnessed limited attention in multi-agent settings. Reward shaping methods that leverage such formal representations in single-agent settings are typically static in the sense that the artificial rewards remain the same throughout the entire learning process. In contrast, we investigate the use of such formal objective representations to define novel reward shaping functions that capture the learned experience of the agents. More specifically, we leverage the automaton representation of the underlying team objectives in mixed cooperative-competitive domains such that each automaton transition is assigned an expected value proportional to the frequency with which it was observed in successful trajectories of past behavior. This form of dynamic reward shaping is proposed within a multi-agent tree search architecture wherein agents can simultaneously reason about the future behavior of other agents as well as their own future behavior.