Forward Algorithm Research Articles

Forward selection models for classifying mild cognitive impairment and Alzheimer’s disease based on single nucleotide polymorphisms

Early detection of Alzheimer’s disease (AD) is crucial for patients to begin treatment early to slow the disease’s progression. While mild cognitive impairment (MCI) is considered an early translational stage of AD, clinically diagnosing MCI is difficult due to its inconsistent symptoms and the lack of standardized diagnostic tests. In this work, we proposed forward selection models to classify patients with AD, patients with MCI and healthy controls (HCs) based on single nucleotide polymorphisms (SNPs). In the proposed method, the initial SNP data were prescreened via genome-wide association studies with a suggestive significance threshold. Then, the qualified SNPs were reselected using the forward SNP selection algorithm to create classification models. Consequently, the forward selection models significantly outperformed the preselection models, those based on all prescreened SNPs, with an area under the precision-recall curve (AUPRC) value of 0.93 in the AD-HC classification, an AUPRC value of 0.94 in the MCI-HC classification, and an AUPRC value of 0.81 in the AD-MCI classification. Moreover, the proposed method could identify AD-associated and MCI-associated SNPs, which would support the clinical diagnosis of AD and MCI in the future.

On numerical stability of continued fractions

The paper considers the numerical stability of the backward recurrence algorithm (BR-algorithm) for computing approximants of the continued fraction with complex elements. The new method establishes sufficient conditions for the numerical stability of this algorithm and the error bounds of the calculation of the $n$th approximant of the continued fraction with complex elements. It follows from the obtained conditions that the numerical stability of the algorithm depends not only on the rounding errors of the elements and errors of machine operations but also on the value sets and the element sets of the continued fraction. The obtained results were used to study the numerical stability of the BR-algorithm for computing the approximants of the continued fraction expansion of the ratio of Horn's confluent functions $\mathrm{H}_7$. Bidisc and bicardioid regions are established, which guarantee the numerical stability of the BR-algorithm. The obtained result is applied to the study of the numerical stability of computing approximants of the continued fraction expansion of the ratio of Horn's confluent function $\mathrm{H}_7$ with complex parameters. In addition, the analysis of the relative errors arising from the computation of approximants using the backward recurrence algorithm, the forward recurrence algorithm, and Lenz's algorithm is given. The method for studying the numerical stability of the BR-algorithm proposed in the paper can be used to study the numerical stability of the branched continued fraction expansions and numerical branched continued fractions with elements in angular and parabolic domains.

Sustainable Air Quality Detection Using Sequential Forward Selection-Based ML Algorithms

Air pollution has exceeded the anticipated safety limit and addressing this issue is crucial for sustainability, particularly in countries with high pollution levels. So, monitoring and forecasting air quality is essential for sustainable urban development. Therefore, this paper presents multiclass classification using two feature selection techniques, namely Sequential Forward Selection (SFS) and filtering, both with different machine learning and ensemble techniques, to predict air quality and make sure that the most relevant features are included in datasets for air quality determination. The results of the considered framework reveal that the SFS technique provides superior performance compared to filter feature selection (FFS) with different ML methods, including the AdaBoost Classifier, the Extra Tree Classifier, Random Forest (RF), and the Bagging Classifier, for efficiently determining the Air Quality Index (AQI). These models’ performances are assessed using predetermined performance metrics. The AdaBoost Classifier model with FFS has the lowest accuracy, while the RF model with SFS achieves the highest accuracy, at 78.4% and 99.99%, respectively. Based on the raw dataset, it was noted that the F1-score, recall, and precision values of the RF model with SFS are 99.96%, 99.97%, and 99.98%, respectively. Therefore, the experimental results undoubtedly show the supremacy, reliability, and robustness of the proposed approach in determining the AQI effectively.

Efficient modelling of infectious diseases in wildlife: A case study of bovine tuberculosis in wild badgers.

Bovine tuberculosis (bTB) has significant socio-economic and welfare impacts on the cattle industry in parts of the world. In the United Kingdom and Ireland, disease control is complicated by the presence of infection in wildlife, principally the European badger. Control strategies tend to be applied to whole populations, but better identification of key sources of transmission, whether individuals or groups, could help inform more efficient approaches. Mechanistic transmission models can be used to better understand key epidemiological drivers of disease spread and identify high-risk individuals and groups if they can be adequately fitted to observed data. However, this is a significant challenge, especially within wildlife populations, because monitoring relies on imperfect diagnostic test information, and even under systematic surveillance efforts (such as capture-mark-recapture sampling) epidemiological events are only partially observed. To this end we develop a stochastic compartmental model of bTB transmission, and fit this to individual-level data from a unique > 40-year longitudinal study of 2,391 badgers using a recently developed individual forward filtering backward sampling algorithm. Modelling challenges are further compounded by spatio-temporal meta-population structures and age-dependent mortality. We develop a novel estimator for the individual effective reproduction number that provides quantitative evidence for the presence of superspreader badgers, despite the population-level effective reproduction number being less than one. We also infer measures of the hidden burden of infection in the host population through time; the relative likelihoods of competing routes of transmission; effective and realised infectious periods; and longitudinal measures of diagnostic test performance. This modelling framework provides an efficient and generalisable way to fit state-space models to individual-level data in wildlife populations, which allows identification of high-risk individuals and exploration of important epidemiological questions about bTB and other wildlife diseases.

Power Loss Minimization and Voltage Profile Improvement on Nigeria Distribution Network Using Whale Optimization Algorithm

The power grid has significant power losses, unstable voltage, and large voltage drops during high demand due to its high resistance. One way to reduce power loss is by strategically placing and sizing Distributed Generation (DG) within the distribution network. However, improper installation of DG can increase power loss. To address this issue, various optimization techniques have been used, but finding the best solution and dealing with complex issues has been challenging. It is important to make efforts to optimally place and size DG in the distribution network to minimize power loss. To this end, a research study aimed to minimize power loss and improve the voltage profile on the DG network using the Whale Optimization Algorithm (WOA) was done. Objective functions were formulated and integrated into WOA, and power flow analysis on the standard IEEE 33-bus and 33-bus Ilorin industrial distribution feeder with and without DG was conducted using the forward and backward sweep distribution load flow algorithm. The optimal size and location of DG for power network loss reduction using sensitivity analysis (SA) and the whale optimization algorithm were determined. The results of the power flow analysis indicated that the total active losses and reactive power losses were reduced to varying extents with the integration of DG using both SA and WOA. The validation results showed that WOA is more efficient and provides high-quality solutions in terms of system loss reduction and best placement for DG in power systems compared to the application of SA. Additionally, WOA was found to offer accurate and high-quality solutions for power loss reduction compared to other existing techniques such as Loss Sensitivity Analysis (LSA), Grey Wolf Optimizer (GWO), Adaptive Shuffled Frogs Leaping Algorithm (ASFLA), and One Rank Cuckoo Search Algorithm (ORCSA). Keywords: Optimization, Distributed Generation (DG), Whale Optimization Algorithm (WOA), Sensitivity Analysis (SA)

Three-State Hidden Markov Model for Spectrum Prediction in Cognitive Radio Networks

The exponential growth and proliferation of wireless devices for different wireless applications have led to the emergence of cognitive radio network (CRN) for optimal utilization of scarce spectrum resources. However, these resources have grossly been under-utilized due to the inaccurate spectrum predictions. Existing spectrum occupancy and prediction techniques which rely on 2-state hidden Markov model (HMM) results in false alarm or missed detection caused by noisy or incomplete observable effects. In this paper, a 3-state HMM spectrum occupancy and prediction technique in CRNs is proposed. The transmission, emission and initial state probabilities of the proposed 3-state HMM parameters were derived based on the three canonical problems associated with HMM. The evaluation, decoding and learning problems were solved using Forward algorithm, Viterbi algorithm and the Baum-Welch algorithm, respectively. The performance of the proposed 3-state HMM spectrum prediction technique was evaluated using prediction accuracy, probability of detection and spectrum utilization efficiency. The simulation results obtained revealed that the 3-state HMM outperformed the 2-state HMM spectrum prediction technique by 24.1% in prediction accuracy.

The development of a bio-inspired vibration source localization algorithm

Vibration source localization is an interdisciplinary topic that has applications in both engineering and biology. To develop bio-inspired technologies that can be applied in engineering, a vibration source localization algorithm is developed based on a spider dynamic model. The model includes: (i) a point source; (ii) a spider model with 8 legs resting on a non-dispersive substrate that introduces correlated leg inputs. The proposed algorithm first calculates the leg inputs using a backward filter forward smoother algorithm with given leg responses to the excitation from the vibration source; and then estimates the source angle, source distance, wave speed and decay rate through optimization. The optimization utilizes the time and amplitude relationships between the leg inputs and the leg-substrate contact locations. The effectiveness of the algorithm is validated by comparing the algorithm estimations with the actual parameters. Results show that the algorithm gives accurate estimation of the source angle and wave speed usually with errors within 3 deg and 1 m/s, respectively, but cannot estimate the source distance. Two spatially-separated spider-like models are needed for estimating the source distance. The algorithm works well even under low signal-to-noise ratio down to 11 dB.

Three-Dimensional Marine Magnetotelluric Parallel Forward Modeling in Conductive and Magnetic Anisotropic Medium Using Finite-Element Method Based on Secondary Field

The marine magnetotelluric (MMT) method is a significant tool extensively utilized in offshore studies, including the understanding of the Earth’s tectonics and hydrocarbon exploration. Conductive anisotropy and non-zero magnetic susceptibility are common phenomena observed in the Earth’s subsurface, and MMT forward modeling is the basis of practical inversion. However, numerical modeling that incorporates both conductive anisotropy and magnetic susceptibility has received limited attention. Moreover, both accuracy and efficiency are crucial in developing a 3D MMT modeling algorithm. Therefore, we developed a multi-level parallel MMT forward modeling algorithm that is capable of simultaneously modeling conductive and magnetic arbitrary anisotropic models using the vector finite element method based on the secondary field formula. The algorithm’s accuracy was validated through comparisons with previously published results for an arbitrary anisotropic model. The results show that the maximum relative error is below 2%, and the speedup reaches an impressive value of 552.41 when running with 2048 cores. Furthermore, the MMT responses of conductive anisotropy and magnetic susceptibility were comprehensively analyzed by several typical models. Our findings highlight the importance of considering magnetic susceptibility in magnetite-rich regions, particularly as the MMT responses may exhibit opposite responses for anomalies with lower resistivity and higher magnetic susceptibility compared with the surrounding rocks.

Linear Monotonic Inter-electrode Associations as Quantitative EEG for Alcoholism Diagnosis

Alcohol use disorders (AUDs) are associated with alterations in EEG patterns that reflect underlying neural dysfunctions, such as impaired cognitive processing, reduced neural connectivity, and disrupted brain rhythms. These EEG alterations can be indicative of several brain disorders, including cognitive deficits, mood disorders, and neurodegenerative diseases, which are often exacerbated by chronic alcohol abuse. Cognitive impairment in alcoholic subjects is caused due to altered functional connectivity between brain regions that can be quantified using statistical measures such as correlation. This paper proposes novel Quantitative EEG (QEEG) features comprising the band-wise absolute value of inter-electrode correlations as a measure of brain functional connectivity to classify alcoholic and non-alcoholic EEG. The EEG signal is first decomposed into five frequency sub-bands. In each sub-band, the absolute value of the linear Pearson product-moment correlation coefficient is computed between time-series EEG recorded by electrode pairs placed over different brain regions. To reduce the dimensionality of the feature vector, an ensemble feature selection approach is adopted, utilizing ANOVA and Chi-square, in conjunction with greedy forward feature selection wrapper algorithm. Four classifiers, namely SVM, KNN, ANN and RF are utilized for the classification. Among them, the SVM classifier achieves the best classification accuracies of 100% on validation with test data and 99.58% on cross-validation with train data, respectively.

A Lovász-Simonovits Theorem for Hypergraphs with Application to Local Clustering

We present the first analysis of diffusion on hypergraphs based on the Lovász-Simonovits theory. We demonstrate that an averaging-based diffusion operator is the appropriate generalization of the lazy random walk diffusion on 2-graphs because the diffusion rapidly converges to its stationary state from any initial state. By proving a Lovász-Simonovits-like theorem for this diffusion, we show that the diffusion rate depends on the hypergraph's conductance. To use averaging-based diffusion for clustering, we define a generalization of personalized page rank for hypergraphs, which we call ''Averaging-based Personalized Page Rank for Hypergraphs'' (APPRH). The fact that averaging-based diffusion is linear, unlike previous hypergraph diffusions used for clustering in the literature, allows us to use the Forward Push algorithm to compute APPRH efficiently. Using this method, we obtain theoretical bounds for the conductance of our clustering that are at least a constant times better than the best-known bounds in the literature. We compare our algorithm A- HyperCut against baselines on million-scale hypergraphs and find that our method is an order of magnitude faster while being competitive regarding the conductance of the local clusters produced.

Maximizing influence via link prediction in evolving networks

Influence Maximization (IM), targeting the optimal selection of k seed nodes to maximize potential information dissemination in prospectively social networks, garners pivotal interest in diverse realms like viral marketing and political discourse dissemination. Despite receiving substantial scholarly attention, prevailing research predominantly addresses the IM problem within the confines of existing networks, thereby neglecting the dynamic evolutionary character of social networks. An inevitable requisite arises to explore the IM problem in social networks of future contexts, which is imperative for certain application scenarios. In this light, we introduce a novel problem, Influence Maximization in Future Networks (IMFN), aimed at resolving the IM problem within an anticipated future network framework. We establish that the IMFN problem is NP-hard and advocate a prospective solution framework, employing judiciously selected link prediction methods to forecast the future network, and subsequently applying a greedy algorithm to select the k most influential nodes. Moreover, we present SCOL (Sketch-based Cost-effective lazy forward selection algorithm Optimized with Labeling technique), a well-designed algorithm to accelerate the query of our IMFN problem. Extensive experimental results, rooted in five real-world datasets, are provided, affirming the efficacy and efficiency of the proffered solution and algorithms.

Spherical Magnetic Vector Forwarding of Isoparametric DGGS Cells with Natural Superconvergent Points

With the rapid advancement of satellite remote sensing technology, many scientists and organizations, including NASA, ESA, NAOC, and Roscosmos, observe and study significant changes in the geomagnetic field, which has greatly promoted research on the geomagnetic field and made it an important research direction in Earth system science. In traditional geomagnetic field research, tesseroid cells face degradation issues in high-latitude regions and accuracy limitations. To overcome these limitations, this paper introduces the Discrete Global Grid System (DGGS) to construct a geophysical model, achieving seamless global coverage through multi-level grid subdivision, significantly enhancing the processing capability of multi-source and multi-temporal spatial data. Addressing the challenges of the lack of analytical solutions and clear integration limits for DGGS cells, a method for constructing shape functions of arbitrary isoparametric elements is proposed based on the principle of isoparametric transformation, and the shape functions of isoparametric DGGS cells are successfully derived. In magnetic vector forwarding, considering the potential error amplification caused by Poisson’s formula, the DGGS grid is divided into six regular triangular sub-units. The triangular superconvergent point technique is adopted, and the positions of integration points and their weight coefficients are accurately determined according to symmetry rules, thereby significantly improving the calculation accuracy without increasing the computational complexity. Finally, through the forward modeling algorithm based on tiny tesseroid cells, this study comprehensively compares and analyzes the computational accuracy of the DGGS-based magnetic vector forwarding algorithm, verifying the effectiveness and superiority of the proposed method and providing new theoretical support and technical means for geophysical research.

A forwarding spoofing detection algorithm for Beidou navigation satellite system vulnerability

With the Beidou navigation system's fast expansion in China, it is popular in military and civilian aspects. However, since the satellite orbit operates at an extremely high position and there is energy loss during the propagation process, the receiver only picks up a very faint signal, which makes the Beidou receiver very vulnerable to interference. The interference of the receiver is divided into natural interference and human interference, of which the human interference is particularly serious. Deception is commonly used in human interference. The deception interference detection technology in Beidou navigation system is studied in this research. Firstly, the signal in the signal capture stage is detected by multi-peak detection algorithm to determine the signal type. If it cannot be determined, the signal is detected by the half-peak full-width algorithm, so as to determine the signal type. In the stage of signal tracking, the Doppler shift of the spoofing signal is applied to determine whether the signal is spoofed or not. When the spoofing signal forwarding delay is set to 0.5 and 1 chip respectively, the full width of half peak is 8.56 and 11.35 after fitting the main peak. If the half-peak full width exceeds the normal navigation signal, it indicates spoofing interference. The constructed model can effectively track downspoofing signals and improve the Beidou navigation system’s detection performance.

Optimal initial donor selection for the synthetic control method

This paper argues that the performance of the Synthetic Control Method (SCM) can depend on the initial set of control units (donors) due to a bias–variance trade-off. A forward stepwise selection algorithm, validated via cross-validation, may reduce prediction error by suggesting to use fewer donors.

Classifying Plant Communities in the North American Coastal Plain With PRISMA Spaceborne Hyperspectral Imagery and the Spectral Mixture Residual

AbstractThe effort to map terrestrial biodiversity, in recent years limited mostly to the use of broadband multispectral remote sensing at decameter scales, can be greatly enhanced by harnessing hyperspectral imagery. Interpretation of hyperspectral imagery may be aided by the Mixture Residual (MR) spectral preprocessing transformation. MR integrates the benefits of spectral mixture analysis with the absorption peak‐enhancing characteristics of continuum removal. MR characterizes each pixel as a linear combination of generic end‐members estimating the spectral continuum, from which the residual of each wavelength is computed and treated as a source of additional information. Using Hyperspectral Precursor of the Application Mission (PRISMA) imagery, we tested the ability of MR‐transformed reflectance as compared to untransformed surface reflectance (SR) to map plant associations and land cover using ground truthing and random forest classifications across four landscapes within the North American Coastal Plain. We used a forward stepwise selection algorithm to choose bands for each classification and subsequently compared these between SR and MR. Our MR classifications distinguished land cover with 5% greater balanced accuracy on average than the SR‐based classifications across all four landscapes. The MR‐based classification that integrated data from all landscapes into a unified model encompassing all 21 land cover types achieved a 76% average balanced accuracy over three iterations. Generally, MR utilized the near‐infrared region to a greater degree than SR while deemphasizing the green peak. Based on our results, MR improves the accuracy of mapping terrestrial biodiversity, likely extending to other current and planned satellite hyperspectral missions.

Motion transformation solutions based on Euler angle perturbation model

This paper presents two novel solutions to compute motion transformation by effectively addressing the nonlinear constraints formed by observational data. These solutions have broad applicability in algorithms involving rotation calculation, such as autonomous positioning systems. Instead of the commonly used Lie algebra-based solutions, the two novel solutions are based on the Euler angle perturbation model, which circumvent the problem arising from the singularity of Euler angles. The first solution is a forward compositional algorithm, which emphasizes accelerated iterative processes. The second solution is an inverse compositional algorithm that aims to minimize computational complexity. Both algorithms offer accelerated iteration by employing efficient Jacobian computation and parameter updates. Furthermore, we have developed an odometry algorithm and a point cloud alignment program to assess the performance of these different solutions. The experimental results demonstrate that the two novel solutions achieve an equivalent global minimum while also reducing computational load.

Advancing Animal Health: A Web-Based Expert System Utilizing Forward Chaining for Disease Diagnosis

The increasing prevalence of animal diseases, along with the increasing need for animal products, highlights the urgent need for efficient diagnostic tools in veterinary medicine. The aim of this research is to create an expert system that uses a forward chaining algorithm to diagnose animal diseases. The forward chaining algorithm is a deductive reasoning approach that starts with existing facts and uses expert tree rules for hypotheses. This process continues until the desired goal is achieved or no additional conclusions can be drawn. Even though there are developments in expert systems, there are still shortcomings in implementing the forward chain for rapid and precise diagnosis of livestock diseases. This work aims to fill this gap by developing an expert system that improves the accuracy and efficiency of disease diagnosis in the livestock industry. A database of animal diseases and symptoms was created by observing and interacting directly with farmers. The system architecture is specifically intended to optimize data processing and user engagement, enabling rapid diagnosis and treatment recommendations. This test shows a level of accuracy and precision, thereby reducing the possibility of misdiagnosis. The capacity of expert systems to provide fast and reliable diagnoses has the potential to improve livestock health management, thereby helping farmers maintain animal welfare and productivity. The results of this work advance the field of veterinary diagnostics and propose other uses of expert systems in animal health management.

IMPLEMENTASI METODE FORWARD CHAINING PADA DIAGNOSA PENYAKIT MALARIA

Malaria is a serious disease caused by the Plasmodium parasite, transmitted through the bite of Anopheles mosquitoes. This disease can cause various symptoms, ranging from fever, headache, and chills to more severe complications such as severe anemia, organ damage, and even death if not treated properly. Therefore, rapid and accurate diagnosis of malaria is crucial for effective treatment and can save lives. This article discusses the implementation of the forward chaining method in a Python-based program for the diagnosis of malaria. Forward chaining is an inference technique in expert systems used to draw conclusions from a series of available facts or data. In this context, the forward chaining method enables the identification of symptoms associated with malaria based on input data provided by the user. By using the forward chaining algorithm, the system automatically processes this data to provide initial diagnosis recommendations and appropriate treatment. This implementation is expected to assist medical personnel in making faster and more accurate decisions, reducing workload, and improving efficiency in handling malaria cases, especially in endemic areas. This article also evaluates the performance of the developed system and discusses challenges and further development prospects in expert system-based diagnostic applications for malaria. The evaluation shows that this system has great potential in supporting medical personnel and speeding up the diagnosis process, which is crucial in managing malaria

A Resource-Based Dynamic Pricing and Forced Forwarding Incentive Algorithm in Socially Aware Networking

In socially aware networking, nodes typically behave selfishly due to resource constraints and social correlations, resulting in low network performance. To incentivize selfish nodes to actively participate in message forwarding, this paper proposes a resource-based dynamic pricing and forced forwarding incentive algorithm (DFIA). Firstly, the algorithm introduces virtual currency as a transaction medium and then designs a pricing function based on factors such as the node’s resource status, participation contribution, location relevance, and social connectivity. It ensures that the forwarding service is transacted at a reasonable price through bargaining rules. Secondly, a forced forwarding strategy is implemented to compel selfish nodes, which are unwilling to participate in other nodes’ message forwarding, to forward a certain number of non-local messages. Meanwhile, in order to prevent nodes from discarding messages and to ensure successful forwarding to the destination, specific rules are used to allocate contribution values to nodes that successfully participate in message forwarding. Lastly, to avoid false quotation behavior, blockchain technology is employed. Transaction information is packaged into blocks and added to the blockchain after consensus validation by other nodes in the network, ensuring the transparency and immutability of transaction data. Simulation results indicate that compared with the existing incentive algorithms, this algorithm not only enhances message delivery probability but also effectively reduces average latency.

An efficient intrusion detection systems in fog computing using forward selection and BiLSTM

Intrusion detection systems (IDS) play a pivotal role in network security and anomaly detection and are significantly impacted by the feature selection (FS) process. As a significant task in machine learning and data analysis, FS is directed toward pinpointing a subset of pertinent features that primarily influence the target variable. This paper proposes an innovative approach to FS, leveraging the forward selection search algorithm with hybrid objective/fitness functions such as correlation, entropy, and variance. The approach is evaluated using the BoT-IoT and TON_IoT datasets. By employing the proposed methodology, our bidirectional long-short term memory (BiLSTM) model achieved an accuracy of 98.42% on the TON_IoT dataset and 98.7% on the BoT-IoT dataset. This superior classification accuracy underscores the efficacy of the synergized BiLSTM deep learning model and the innovative FS approach. The study accentuates the potency of the proposed hybrid approach in FS for IDS and highlights its substantial contribution to achieving high classification performance in internet of things (IoT) network traffic analysis.