Project MATCH Research Articles

Optimal investment of distribution energy resources via energy performance contracts: An evolutionary game approach

Investment in distributed energy resources (DERs) is crucial for the expansion of distribution networks. However, traditional methods often lack comprehensive project management throughout their entire lifecycle and precise matching between distribution network operator (DNO) and energy consumer (EC) at the investment level. To address these issues, this paper proposes a novel investment model based on energy performance contracts (EPCs). Three EPC mechanisms are introduced: energy shared savings model (ESSM), energy guaranteed saving model (EGSM), and energy cost trust model (ECTM). Additionally, DERs investment strategies under EPC mechanisms are proposed to satisfy full-process management of project and precise matching between EC and DNO. The investment and operation problem is solved by an evolutionary game theory that ensures bounded rational agent to achieve satisfactory expected returns through repeated dynamic games. Results from the case study of an actual urban 37-bus system demonstrate that different EPC mechanisms are suitable for various scenarios depending on the EC's profit demand and project scale. The proposed model provides a comprehensive framework for DERs investment, considering the interests of both DNO and EC, and promotes the optimal expansion of distribution networks in a more practical and effective manner.

Role of relative-reinforcement value of alcohol-free activities during recovery from alcohol use disorder in an adult clinical sample.

Behavioral economic theory has been extensively applied to understand alcohol use disorder (AUD). Applications of behavioral economic theory conceptualize AUD as a pattern of harmful alcohol use over extended periods of time in which choices between drinking or engaging in alcohol-free activities favor drinking. Recovery, in contrast, entails a sustained shift toward a pattern of selecting rewarding alcohol-free activities. The present study examined whether alcohol-free activity engagement and the relative-reinforcement value (RRV) of engaging in those activities predicted AUD treatment outcomes via secondary analysis of data from Project MATCH, a multisite randomized clinical trial examining behavioral treatments for AUD (N = 1,279, 75.8% male, 81.0% non-Hispanic White, 9.5% Black, 7.7% Hispanic/Latino, 1.5% American Indian/Alaska Native, < 1% Asian American, and other race/ethnicity). Regression analyses indicated that every additional alcohol-free activity reported at 6 months posttreatment was associated with 7% fewer drinking days, 5% fewer heavy drinking days, and approximately one less drink per drinking day, as well as with significant improvements in depression, purpose in life, and psychosocial functioning at 12 months following treatment. Consistent with behavioral economic theory, higher RRV of alcohol-free activities also predicted significant reductions in drinking and improvements in functioning, and these associations were stronger compared to results for alcohol-free activity frequency only. The findings highlight the importance of understanding environmental contexts conducive to recovery and support the value of increasing alcohol-free activity engagement and the RRV of engaging in such activities to facilitate reductions in drinking and improved functioning among individuals with AUD. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Do improvements in motivational language predict alcohol use in motivational interviewing? Ambivalence matters.

Motivational Interviewing (MI) is described as a method for improving clinical outcomes by reducing client ambivalence. If this is true, MI's focus on improving clients' motivational language should be most useful for clients with ambivalence about change and less valuable for those who are ready to implement new behaviors or are opposed to change. To address this hypothesis and potentially add precision to MI delivery in clinical settings, we tested whether the relationship between clients' in-session motivational language and posttreatment alcohol use depended on their baseline motivation to change. Client speech from 149 sessions from Project MATCH were analyzed. A cluster analysis of the percent change talk during the first decile of the session identified three motivational groups: opposed, ambivalent, and ready. The change in percent change talk (C-PCT) across the session was calculated for each group. Zero-inflated negative binomial analysis was used to test whether the effect of C-PCT on end-of-treatment drinking varied between motivational groups. The count part of the model revealed a significant interaction between C-PCT and membership in the ambivalent group (b = -17.710, 95% CI [-25.775, -9.645], p < .001), only for those who received MI. Favorable C-PCT was associated with less drinking (b = -15.735, p = .004). Only baseline drinking was a significant predictor of abstinence at follow-up (b = .032, 95% CI [0.012, 0.051], p = .001). A putative MI mechanism-improved client motivational language-appears most important for clients who express ambivalence in the opening minutes of the session, with minimal value for those who do not. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Enhanced Freelance Matching: Integrated Data Analysis and Machine Learning Techniques

The objective of this research is to devise a personalized recommendation system for a freelancing platform to optimize the freelancer project matching process. This enhancement is intended to improve user experience and increase the success rate of projects. The system will recommend projects to freelancers based on their skills and preferences by employing data analysis and machine learning methodologies. The research methodology adheres to the Cross Industry Standard Process for Data Mining (CRISP-DM), incorporating six stages: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. The proposed project employs a hybrid recommendation strategy, integrating Content-Based Filtering through KNearest Neighbors (K-NN) and Cosine Similarity, Collaborative Filtering via Singular Value Decomposition (SVD), and recommendations derived from Word2vec. Evaluation metrics such as precision, recall, F1 score, MAP, and MRR are utilized to assess model performance. The results, including precision scores of 0.80 for KNN and 0.728 for SVD, recall scores of 0.60 for KNN and 0.623 for SVD, and F1 scores of 0.69 for KNN and 0.671 for SVD, as well as a MAP of 0.75 and MRR of 0.80 for Word2vec, demonstrate the efficacy of the hybrid recommendation system in delivering accurate and varied project suggestions to freelancers, with a weighted average ensemble learning model emerging as the most effective solution.

Rapid authenticity assessment of PGI Hongyuan yak milk based on SICRIT-QTOF MS

Hongyuan (HY) yaks live in a pollution-free environment, making HY yak milk a green food, but their short milk production period and low milk yield make yak milk precious and expensive. The phenomenon of counterfeiting HY yak milk with ordinary milk from other origins has already occurred, so the authenticity assessment of HY yak milk is necessary. This study developed a rapid soft ionisation by chemical reaction in transfer quadrupole time-of-flight mass spectrometry (SICRIT-QTOF MS) for HY yak milk differences assessment. Principal component analysis and orthogonal least squares discriminant analysis showed differences between HY milk and the other three origins. Twenty-eight differential compounds were screened out by variable importance in projection, fold change, P-value, and database matching. Furthermore, six characteristic compounds (proline, 2-hydroxy-3-methylbutyric acid, and l-isoleucine, etc.) of HY samples were putatively identified. The study demonstrated that SICRIT-QTOF MS has great potential for rapidly distinguishing the milk origin.

Patterns of drinking behavior around a treatment episode for alcohol use disorder: Predictions from pre-treatment measures.

Alcohol use disorder (AUD) has been described as a chronic disease given the high rates that affected individuals have in returning to drinking after a change attempt. Many studies have characterized predictors of aggregated alcohol use (e.g., percent heavy drinking days) following treatment for AUD. However, to inform future research on predicting drinking as an AUD outcome measure, a better understanding is needed of the patterns of drinking that surround a treatment episode and which clinical measures predict patterns of drinking. We analyzed data from the Project MATCH and COMBINE studies (MATCH: n = 1726; 24.3% female, 20.0% non-White; COMBINE: n = 1383; 30.9% female, 23.2% non-White). Daily drinking was measured in the 90 days prior to treatment, 90 days (MATCH) and 120 days (COMBINE) during treatment, and 365 days following treatment. Gradient boosting machine learning methods were used to explore baseline predictors of drinking patterns. Drinking patterns during a prior time period were the most consistent predictors of future drinking patterns. Social network drinking, AUD severity, mental health symptoms, and constructs based on the addiction cycle (incentive salience, negative emotionality, and executive function) were associated with patterns of drinking prior to treatment. Addiction cycle constructs, AUD severity, purpose in life, social network, legal history, craving, and motivation were associated with drinking during the treatment period and following treatment. There is heterogeneity in drinking patterns around an AUD treatment episode. This study provides novel information about variables that may be important to measure to improve the prediction of drinking patterns during and following treatment. Future research should consider which patterns of drinking they aim to predict and which period of drinking is most important to predict. The current findings could guide the selection of predictor variables and generate hypotheses for those predictors.

A Variable Photo-Model Method for Object Pose and Size Estimation with Stereo Vision in a Complex Home Scene.

Model-based stereo vision methods can estimate the 6D poses of rigid objects. They can help robots to achieve a target grip in complex home environments. This study presents a novel approach, called the variable photo-model method, to estimate the pose and size of an unknown object using a single photo of the same category. By employing a pre-trained You Only Look Once (YOLO) v4 weight for object detection and 2D model generation in the photo, the method converts the segmented 2D photo-model into 3D flat photo-models assuming different sizes and poses. Through perspective projection and model matching, the method finds the best match between the model and the actual object in the captured stereo images. The matching fitness function is optimized using a genetic algorithm (GA). Unlike data-driven approaches, this approach does not require multiple photos or pre-training time for single object pose recognition, making it more versatile. Indoor experiments demonstrate the effectiveness of the variable photo-model method in estimating the pose and size of the target objects within the same class. The findings of this study have practical implications for object detection prior to robotic grasping, particularly due to its ease of application and the limited data required.

Model-Based Needle Identification Using Image Analysis and Needle Library Matching for Ultrasound-Guided Kidney Biopsy: A Feasibility Study.

The aim of the work described here was to determine the feasibility of using a novel biopsy needle detection technique that achieves high sensitivity and specificity in a trade-off of resolution, detectability and depth of imaging. The proposed needle detection method consists of a model-based image analysis, temporal needle projection and needle library matching: (i) Image analysis was formulated under the signal decomposition framework; (ii) temporal projection converted the time-resolved needle dynamics into a single image of the desired needle; and (iii) the enhanced needle structure was spatially refined by matching a long, straight linear object in the needle library. The efficacy was examined with respect to different needle visibility. Our method effectively eliminated confounding effects of the background tissue artifacts more robustly than conventional methods, thus improving needle visibility even with the low contrast between the needle and tissue. The improvement in needle structure further resulted in an improvement in estimation performance for the trajectory angle and tip position. Our three-step needle detection method can reliably detect needle position without the need for external devices, increasing the needle conspicuity and reducing motion sensitivity.

NIR-IIb Fluorescence Molecular Tomography of Glioblastomas Based on Heterogeneous Mouse Models and Adaptive Projection Match Pursuit Method.

Fluorescence molecular tomography (FMT) using the second near-infrared window (NIR-II) fluorescence has been proved to outperform conventional FMT using the first near-infrared window (NIR-I) fluorescence. However, it was still a challenge to achieve a satisfactory reconstructed light source using NIR-II FMT as the NIR-IIa (1300-1400 nm) fluorescence in the NIR-II spectrum used in the previous NIR-II FMT study was still suffering from prominent absorption and scattering of tissue. A novel NIR-IIb (1500-1700 nm) FMT method was proposed and applied in the reconstruction of glioblastomas in animal models. Optical parameters that describe the effect of different tissue on the NIR-IIb photons were calculated to construct a light propagation model of NIR-IIb light to form the forward model. Besides, a novel adaptive projection matching pursuit (APMP) method was further adopted to accurately solve the inverse problem. Location error and Dice coefficient were used to evaluate the accuracy of reconstruction. Simulation experiments using single-source and dual-source and in vivo experiments were conducted to evaluate the reconstructed light source. Both simulation and in vivo experiments demonstrated that NIR-IIb FMT achieved reduced location error and improved shape similarity compared with NIR-IIa. NIR-IIb FMT of orthotopic mouse models further achieved a location error of 0.65 mm and a Dice coefficient of 0.56, which both outperformed NIR-IIa. The results demonstrated that NIR-IIb has better reconstruction performance for positioning accuracy and shape recovery. The inspiring results in this study demonstrate the effectiveness and advantages of NIR-IIb FMT in precise tumor positioning.

Achieving a shared understanding in the creative industries: freelancers’ use of boundary objects in collaborative innovation projects

This paper examines how freelancers in the field of the creative industries capture and distil their clients’ needs and wants in collaborative innovation projects. Our empirical data from sixteen collaborative product-development processes led by freelancers in Finland and Spain allowed us to identify that boundary objects were critical for the achievement of a shared understanding at the beginning of the development process. In the later stages, the use of these objects decreased drastically and dialogue became the focus. The existence of a project match – whether the project was relevant and interesting for the freelancer – and a personal match – perceived similarity with the client in terms of thought and sense-making processes – were crucial to ensure engagement. From a managerial perspective, this paper stresses the importance of finding a personal and project match, highlights the role of boundary objects to create a shared understanding and reinforces the importance of dialogue in development processes and collaborative innovation.

Constructs derived from the addiction cycle predict alcohol use disorder treatment outcomes and recovery 3 years following treatment.

The addiction cycle has been proposed as a framework for understanding the progression of alcohol use disorder (AUD) in terms of psychological and biological domains, including reward drinking/incentive salience, relief drinking/negative emotionality, and loss of control/executive functioning impairment. To have utility in clinical practice, self-report measures of these domains that are applicable across sociodemographic groups and associated with clinical outcomes are needed. This study sought to validate domains from self-report measures and to test whether domains are measurement invariant across sociodemographic groups and associated with treatment outcomes. Secondary analysis of individuals with AUD (n = 3,092) who participated in two alcohol clinical trials, Project Matching Alcohol Treatment to Client Heterogeneity (MATCH) and COMBINE. Factor analytic methods were used to derive addiction cycle domains at baseline. These domains were then examined as predictors of outcomes. Fifteen self-report items were used as indicators of the addiction cycle domains, with sociodemographic differences in measurement by sex, age, race, education, and AUD symptoms. Relief/negative emotionality and reward/incentive salience were significantly associated with outcomes at 1 and 3 years following treatment, and executive functioning also predicted nonabstinent recovery at 3 years. The results support the utility of domains relevant to the addiction cycle in predicting AUD treatment outcomes and recovery among individuals who sought treatment for AUD. The addiction cycle domains were more strongly associated with outcomes than other measures clinicians might use to predict outcomes (e.g., AUD symptoms). Future research should continue to develop and refine the items and test whether the addiction cycle domains can inform treatment planning. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Objectives and curriculum for a graduate business analytics capstone: Reflections from practice

AbstractMany higher education institutions have responded to the significant shortage of professionals with strong analytical expertise by offering graduate programs in business analytics and data science. These programs are typically designed to be practitioner focused and many of them offer a Capstone course that gives students the opportunity to conduct a real‐world analytics project. In this article, we describe the innovative design of a Capstone course offered to cohorts of approximately 110 graduate students in the business school at the University of Texas, Austin. This course is designed to deliver self‐directed and experiential learning from interacting with business stakeholders and successfully completing team‐based, business analytics projects within a commercial firm using the firm's data. The course design includes student and sponsor engagement, the formation of diverse balanced project teams, matching of teams with projects, and scaffolding, which includes consistent structured mentoring, team reflections, interim deliverables and in‐class learning for practical skills not covered elsewhere in the curriculum. This course has now been offered successfully to two cohorts. In this article, we will also describe changes made for the second cohort related to team formation, project matching, mentoring and in‐class learning. These changes were based on feedback from the first cohort.

Measurement invariance of the University of Rhode Island Change Assessment Scale in Project MATCH: An exploratory structural equation modeling approach.

Progression through the stages of change is a proposed mechanism underlying the effects of treatment for alcohol use disorder (AUD). However, examining stages of change as a mechanism of treatment effects requires that the measure be invariant across patient subgroups, treatment conditions, and time. In this study, we examined measurement invariance of the University of Rhode Island Change Assessment Scale (URICA) in Project MATCH using an exploratory structural equation modeling (ESEM) approach. We conducted a secondary analysis of data from Project MATCH (N=1726; Mage =40.2, SD=10.9; 75.7% male; 80% non-Hispanic white), a multisite randomized clinical trial that tested three AUD treatments: Motivational Enhancement Therapy, Cognitive-Behavioral Therapy, or Twelve-Step Facilitation. Participants completed the 24-item URICA for assessing the stages of change in relation to drinking at baseline and post-treatment (3months after baseline). A 4-factor ESEM provided a good fit to the data and a better fit to the data than a conventional 4-factor confirmatory factor analysis model. Further, the URICA demonstrated scalar invariance across each patient subgroup at baseline (sex, ethnicity, marital status, education, and parental history of AUD) and treatment condition at follow-up. However, the URICA was not longitudinally invariant as the metric model resulted in a significant decrement in model fit. Measurement invariance of the URICA over time was not supported. Longitudinally invariant measures of the stages of change are needed to test the proposal that progression through the stages explains treatment effects.

Limitations of cross-lagged panel models in addiction research and alternative models: An empirical example using project MATCH.

Models of addiction often posit bidirectional and dynamic associations between constructs relevant to the etiology and maintenance of addictive behaviors. The cross-lagged panel model (CLPM) is commonly used in addiction research but has been critiqued for not appropriately adjusting for between-person variance. Alternatives to the CLPM have been suggested but remain underutilized. The primary purpose of this article is to highlight interpretational limitations of the CLPM and to provide examples of alternative models. We specified CLPM, Random-Intercept CLPM, and a Latent Curve Model with Structured Residuals using four waves of data from Project MATCH (n = 1,201). We modeled prospective relations among depression symptoms and temptation to drink. Substantive inferences and assumptions across models were compared. The CLPM provided the most evidence of significant cross-lagged paths but the poorest fit to the data compared to other models. Alternative models found little evidence of prospective within-person associations, and more evidence for between-person associations and wave-specific within-person relations between depression symptoms and temptation to drink. This study highlights shortcomings of the CLPM and details alternative models to consider. Addiction researchers should consider alternatives to the CLPM to more optimally delineate relations among constructs across time. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Substantiation of location image classification model using projective template matching and convolutional neural network

This study first attempts to observe the action of the CNN and then compares it to test Projective Template Matching and Object Detection as new approaches. In the final model selection, the accuracy of the prediction model and the computational processing time was mainly compared. At last, the combination of the Object Detection model and CNN was selected as a final location classification model with a prediction accuracy of 61%. This final model shows the optimal prediction result by first attempting to detect the common feature regions of the location image and then analyzing the overall feature characteristic. The fact is that CNN is good for training image data with common overall features for classification. This being so, we expect that training several fundamental ROIs can more efficiently train the CNN model than training the pure location images.

Examining replicability in addictions research: How to assess and ways forward.

The high rate of statistically significant findings in the sciences that do not replicate in a new sample has been described as a "replication crisis." Few replication attempts have been conducted in studies of alcohol use disorder (AUD), and the best method for determining whether a finding replicates has not been explored. The goal of the current study was to conduct direct replications within a multisite AUD-randomized controlled trial and to test a range of replication metrics. We used data from a large AUD clinical trial (Project Matching Alcoholism Treatments and Client Heterogeneity [Project MATCH], n = 1,726) to simulate direct replication attempts. We examined associations between drinking intensity and negative alcohol-related consequences (Model 1), sex differences in drinking intensity (Model 2), and reductions in drinking following treatment (Model 3). We treated each of the 11 data collection sites as unique studies such that each subsample was treated as an "original" study, and the remaining 10 subsamples were viewed as "replication" studies. Replicability metrics included the consistency of statistical significance, overlapping confidence intervals, and consistency of the direction of the effect. We also tested effect replication and heterogeneity using meta-analysis. We observed between 0% and 100% replicability across the replicability metrics depending on which subsample was treated as the "original" study. Meta-analyses indicated results were more similar across subsamples with no significant heterogeneity for Models 1 and 2. We recommend researchers focus on effect sizes and use meta-analysis to evaluate the level of replicability. We also encourage direct replication attempts and sharing of data and code to facilitate direct replication. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Spatiotemporal Matching Cost Function Based on Differential Evolutionary Algorithm for Random Speckle 3D Reconstruction

Random speckle structured light can increase the texture information of the object surface, so it is added in the binocular stereo vision system to solve the matching ambiguity problem caused by the surface with repetitive pattern or no texture. To improve the reconstruction quality, many current researches utilize multiple speckle patterns for projection and use stereo matching methods based on spatiotemporal correlation. This paper presents a novel random speckle 3D reconstruction scheme, in which multiple speckle patterns are used and a weighted-fusion-based spatiotemporal matching cost function (STMCF) is proposed to find the corresponding points in speckle stereo image pairs. Furthermore, a parameter optimization method based on differential evolutionary (DE) algorithm is designed for automatically determining the values of all parameters included in STMCF. In this method, since there is no suitable training data with ground truth, we explore a training strategy where a passive stereo vision dataset with ground truth is used as training data and then apply the learned parameter value to the stereo matching of speckle stereo image pairs. Various experimental results verify that our scheme can realize accurate and high-quality 3D reconstruction efficiently and the proposed STMCF exhibits superior performance in terms of accuracy, computation time and reconstruction quality than the state-of-the-art method based on spatiotemporal correlation.

ClusterAlign: A fiducial tracking and tilt series alignment tool for thick sample tomography.

Thick specimens, as encountered in cryo-scanning transmission electron tomography, offer special challenges to conventional reconstruction workflows. The visibility of features, including gold nanoparticles introduced as fiducial markers, varies strongly through the tilt series. As a result, tedious manual refinement may be required in order to produce a successful alignment. Information from highly tilted views must often be excluded to the detriment of axial resolution in the reconstruction. We introduce here an approach to tilt series alignment based on identification of fiducial particle clusters that transform coherently in rotation, essentially those that lie at similar depth. Clusters are identified by comparison of tilted views with a single untilted reference, rather than with adjacent tilts. The software, called ClusterAlign, proves robust to poor signal to noise ratio and varying visibility of the individual fiducials and is successful in carrying the alignment to the ends of the tilt series where other methods tend to fail. ClusterAlign may be used to generate a list of tracked fiducials, to align a tilt series, or to perform a complete 3D reconstruction. Tools to evaluate alignment error by projection matching are included. Execution involves no manual intervention, and adherence to standard file formats facilitates an interface with other software, particularly IMOD/etomo, tomo3d, and tomoalign.

Symposium

BackgroundAbout 4% Alcohol dependence syndrome (ADS) suffer complicated withdrawal. Nearly all evidence based treatment for ADS originated as individual interventions, and most major treatment research studies have been limited to individual-based treatments e.g., COMBINE and Project MATCH studies. However this is a challenge to implement Individual therapy in our country where treatment gap is 70-90%. It has been found that group-based interventions have strong experimental support. Department of psychiatry KIMS hubli explored the utility of group therapy in general hospital psychiatry unit (GHPU) setting for patients with ADS.Aims1. Need of resource efficient psychosocial intervention in ADS in relapse prevention.2. Utility of group therapy in ADSMethods92 consenting individuals along with their primary care giver were enrolled in group therapy. A total of 6 sessions of structured group therapy, one session monthly for 6 months. There were 9 closed groups and each group consisted of less than males. Individuals were assessed for relapse and care givers knowledge attitude and practices (KAP) towards ADS were assessed at baseline, end of 6 sessions and after 6 months of follow-up. Under supervision of senior psychiatrist post graduates students in MD Psychiatry conducted the Group therapy sessions.Results and Conclusion18 individuals dropped out. Attrition rate of 19.5%. Relapse rate was 40%. Early identification and intervention of relapses. Most patients and their care givers were retained in the treatment loop. KAP were favourable in the caregivers. It is a challenge to provide Individual psychotherapy for ADS where mental health care services are scare. However group interventions are promising as they are effective and economical relapse prevention strategy. It is acceptable by the caregivers and feasible in GHPU. Prospective research and controlled trials to be considered in the future.

Target Projection Feature Matching Based Deep ANN with LSTM for Lung Cancer Prediction

Prediction of lung cancer at early stages is essential for diagnosing and prescribing the correct treatment. With the continuous development of medical data in healthcare services, Lung cancer prediction is the most concerning area of interest. Therefore, early prediction of cancer helps in reducing the mortality rate of humans. The existing techniques are time-consuming and have very low accuracy. The proposed work introduces a novel technique called Target Projection Feature Matched Deep Artificial Neural Network with LSTM (TPFMDANN-LSTM) for accurate lung cancer prediction with minimum time consumption. The proposed deep learning model consists of multiple layers to learn the given input patient data. Different processes are carried out at each layer to predict lung cancer at an earlier stage. The input layer of the deep neural network receives the data and associated features and sends them to the hidden layer. The first hidden layer performs the feature selection process using Target Projection matching to identify the relevant features for accurate disease prediction with minimum time consumption. Hidden layer 2 performs the patient Data Classification based on Czekanowski's dice similarity coefficient with the selected relevant features from the previous layer to predict lung cancer. The factors considered for performance evaluation of the proposed technique with the existing state of the art approaches include prediction accuracy, false-positive rate and prediction time. Lunar 16 Lung Cancer dataset consisting of patient data is used for evaluation. The obtained results show that the proposed TPFMDANN-LSTM technique achieves higher prediction accuracy with minimum time consumption and less false positive rate than the state-of-the-art methods. The experimental results reveal that the TPFMDANN-LSTM technique performs better with a 6% improvement in prediction accuracy, 36% reduction of false positives, and 16% faster prediction time for lung cancer detection compared to existing works.