Search results for: gravitational search algorithm

Authors: Farshideh Einsele, Hassan Foroosh

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In this paper, we present a robust algorithm to recognize extracted text from grocery product images captured by mobile phone cameras. Recognition of such text is challenging since text in grocery product images varies in its size, orientation, style, illumination, and can suffer from perspective distortion. Pre-processing is performed to make the characters scale and rotation invariant. Since text degradations can not be appropriately defined using wellknown geometric transformations such as translation, rotation, affine transformation and shearing, we use the whole character black pixels as our feature vector. Classification is performed with minimum distance classifier using the maximum likelihood criterion, which delivers very promising Character Recognition Rate (CRR) of 89%. We achieve considerably higher Word Recognition Rate (WRR) of 99% when using lower level linguistic knowledge about product words during the recognition process.

Keywords: camera-based OCR, feature extraction, document, image processing, grocery products

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Authors: Muganyizi Jonas Bisheko, G. Rejikumar

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Reducing post-harvest losses could be a sustainable solution to enhance the food and income security of smallholder farmers in developing countries. While various research institutions have come up with a number of innovative post-harvest technologies for reducing post-harvest losses, most of them have not been extensively adopted by smallholder farmers. Despite this gap, the synthesized information about the major constraints of post-harvest technology is scarce. This study has been conducted to fill this gap and show the implications of the findings for future post-harvest research. The developed search strategy retrieved 2201 studies. However, after excluding duplicates, title, abstract and full article screening, a total of 41 documents were identified. The major findings are: (i) there is an outstanding deficiency of systematic evidence of the effect of climate change, off-farm income and sources of post-harvest information on the adoption of improved post-harvest technologies; (ii) there is very limited information on adoption constraints pertaining to matters of policy, rules and regulations; (iii) there is very thin literature on behavioral constraints associated with limited adoption of improved post-harvest technologies; (iv) most of the studies focused on post-harvest storage technologies (47%) followed by overall post-harvest management practices (25%), processing technologies (19%) and packaging technologies (3%). Much of the information was found on Cereals (58%), especially maize (44%); (v) geographically, Sub-Saharan Africa accounted for 79% of the reviewed interventions, while South Asia occupied only 21%. The findings of this review are intended to guide various post-harvest technologists and decision-makers in addressing the challenge of huge post-harvest losses.

Keywords: constraints, post-harvest loss, post-harvest technology , smallholder farmer

Procedia PDF Downloads 237

Authors: Emily Adams, Eddie Donaghy, David Henderson, Lauren Ng, Caroline Sanders, Rowena Stewart, Maria Wolters, Stewart Mercer

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Ongoing review abstract: Older People Experiencing Homelessness (OPEH) can have mental and physical indicators of aging 10–20 years earlier than the general population and experience premature mortality due to age-related chronic conditions. Emerging literature suggests digital interventions could positively impact PEH’s well-being. However, the increased reliance on digital delivery may also perpetuate digital inequalities for socially excluded groups, including PEH. The potential triple disadvantage of being older, homeless, and digitally excluded creates a uniquely problematic situation that warrants further research. This scoping review aims to investigate and synthesise the range and type of digital interventions available to OPEH and the organisations that support OPEH. The following databases were searched on 28th July 2023: Medline, Scopus, International Bibliography of the Social Sciences (IBSS)‎, Applied Social Sciences Index & Abstracts (ASSIA)‎, Association for Computing Machinery Digital Library (ACMDL) and Policy commons. A search strategy was developed in collaboration with an academic librarian. The presentation will include: An introduction to OPEH and digital exclusion Overview of the results of this review: OPEH usage of digital platforms Current digital interventions available The role of support organisations Current gaps in the evidence, future research and recommendations for policy and practice

Keywords: homeless, digital exclusion, aging, technology

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Authors: Laidi Maamar, Hanini Salah

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The present work investigates the potential of artificial neural network (ANN) model to predict the horizontal global solar radiation (HGSR). The ANN is developed and optimized using three years meteorological database from 2011 to 2013 available at the meteorological station of Blida (Blida 1 university, Algeria, Latitude 36.5°, Longitude 2.81° and 163 m above mean sea level). Optimal configuration of the ANN model has been determined by minimizing the Root Means Square Error (RMSE) and maximizing the correlation coefficient (R2) between observed and predicted data with the ANN model. To select the best ANN architecture, we have conducted several tests by using different combinations of parameters. A two-layer ANN model with six hidden neurons has been found as an optimal topology with (RMSE=4.036 W/m²) and (R²=0.999). A graphical user interface (GUI), was designed based on the best network structure and training algorithm, to enhance the users’ friendliness application of the model.

Keywords: artificial neural network, global solar radiation, solar energy, prediction, Algeria

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Authors: Aagesson, Ekstrand, Persson, Sallander

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A description of how a team of librarians at Linnaeus University Library in Sweden utilizes a pedagogical approach to deliver engaging digital workshops on information retrieval. The team consists of four librarians supporting three different faculties. The paper discusses the challenges faced in engaging students who may perceive information retrieval as a boring and difficult subject. The paper emphasizes the importance of motivation, inclusivity, constructive feedback, and collaborative learning in enhancing student engagement. By employing a two-librarian teaching model, maintaining a lighthearted approach, and relating information retrieval to everyday experiences, the team aimed to create an enjoyable and meaningful learning experience. The authors describe their approach to increase student engagement and learning outcomes through a three-phase workshop structure: before, during, and after the workshops. The "flipped classroom" method was used, where students were provided with pre-workshop materials, including a short film on information search and encouraged to reflect on the topic using a digital collaboration tool. During the workshops, interactive elements such as quizzes, live demonstrations, and practical training were incorporated, along with opportunities for students to ask questions and provide feedback. The paper concludes by highlighting the benefits of the flipped classroom approach and the extended learning opportunities provided by the before and after workshop phases. The authors believe that their approach offers a sustainable alternative for enhancing information retrieval knowledge among students at Linnaeus University.

Keywords: digital workshop, flipped classroom, information retrieval, interactivity, LIS practitioner, student engagement

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Authors: Manan’Iarivo Rasolonjanahary, Chris Bingham, Nigel Schofield, Masoud Bazargan

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This paper presents the research and application of model predictive scheduled charging of electric vehicles (EV) subject to limited available power resource. To focus on algorithm and operational characteristics, the EV interface to the source is modelled as a battery state equation during the charging operation. The researched methods allow for the priority scheduling of EV charging in a multi-vehicle regime and when subject to limited source power availability. Priority attribution for each connected EV is described. The validity of the developed methodology is shown through the simulation of different scenarios of charging operation of multiple connected EVs including non-scheduled and scheduled operation with various numbers of vehicles. Performance of the developed algorithms is also reported with the recommendation of the choice of suitable parameters.

Keywords: model predictive control, non-scheduled, power limited sources, scheduled and stop-start battery charging

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Authors: Kenji Gwee

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In Singapore, the death penalty remains in active use for murder and drug trafficking of controlled drugs such as heroin. As such, the psychological assessment of defendants can often be of high stakes. The Test of Memory Malingering (TOMM) is employed by government psychologists to determine the degree of effort invested by defendants, which in turn inform on the veracity of overall psychological findings that can invariably determine the life and death of defendants. The purpose of this study was to find out if defendants facing the death penalty were more likely to invest less effort during psychological assessment (to fake bad in hopes of escaping the death sentence) compared to defendants facing lesser penalties. An archival search of all forensic cases assessed in 2012-2013 by Singapore’s designated forensic psychiatric facility yielded 186 defendants’ TOMM scores. Offence severity, coded into 6 rank-ordered categories, was analyzed in a one-way ANOVA with TOMM score as the dependent variable. There was a statistically significant difference (F(5,87) = 2.473, p = 0.038). A Tukey post-hoc test with Bonferroni correction revealed that defendants facing lower charges (Theft, shoplifting, criminal breach of trust) invested less test-taking effort (TOMM = 37.4±12.3, p = 0.033) compared to those facing the death penalty (TOMM = 46.2±8.1). The surprising finding that those facing death penalties actually invested more test taking effort than those facing relatively minor charges could be due to higher levels of cooperation when faced with death. Alternatively, other legal avenues to escape the death sentence may have been preferred over the mitigatory chance of a psychiatric defence.

Keywords: capital sentencing, offence severity, Singapore, Test of Memory Malingering

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Authors: Safaa M. Hassan; Safwat S. Gabr, Mohamed F. Sadek

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This study is proposed for the lithological and iron oxides detection in the old mine areas of El-Bahariya Depression, Western Desert, using ASTER and Landsat-8 remote sensing data. Four old iron ore occurrences, namely; El-Gedida, El-Haraa, Ghurabi, and Nasir mine areas found in the El-Bahariya area. This study aims to find new high potential areas for iron mineralization around El-Baharyia depression. Image processing methods such as principle component analysis (PCA) and band ratios (b4/b5, b5/b6, b6/b7, and 4/2, 6/7, band 6) images were used for lithological identification/mapping that includes the iron content in the investigated area. ASTER and Landsat-8 visible and short-wave infrared data found to help mapping the ferruginous sandstones, iron oxides as well as the clay minerals in and around the old mines area of El-Bahariya depression. Landsat-8 band ratio and the principle component of this study showed well distribution of the lithological units, especially ferruginous sandstones and iron zones (hematite and limonite) along with detection of probable high potential areas for iron mineralization which can be used in the future and proved the ability of Landsat-8 and ASTER data in mapping these features. Minimum Noise Fraction (MNF), Mixture Tuned Matched Filtering (MTMF), pixel purity index methods as well as Spectral Ange Mapper classifier algorithm have been successfully discriminated the hematite and limonite content within the iron zones in the study area. Various ASTER image spectra and ASD field spectra of hematite and limonite and the surrounding rocks are compared and found to be consistent in terms of the presence of absorption features at range from 1.95 to 2.3 μm for hematite and limonite. Pixel purity index algorithm and two sub-pixel spectral methods, namely Mixture Tuned Matched Filtering (MTMF) and matched filtering (MF) methods, are applied to ASTER bands to delineate iron oxides (hematite and limonite) rich zones within the rock units. The results are validated in the field by comparing image spectra of spectrally anomalous zone with the USGS resampled laboratory spectra of hematite and limonite samples using ASD measurements. A number of iron oxides rich zones in addition to the main surface exposures of the El-Gadidah Mine, are confirmed in the field. The proposed method is a successful application of spectral mapping of iron oxides deposits in the exposed rock units (i.e., ferruginous sandstone) and present approach of both ASTER and ASD hyperspectral data processing can be used to delineate iron-rich zones occurring within similar geological provinces in any parts of the world.

Keywords: Landsat-8, ASTER, lithological mapping, iron exploration, western desert

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Authors: Minh Quang Le, Dane Taylor

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Convection is a well-studied topic in fluid dynamics, yet it is less understood in the context of networks flows. Here, we incorporate techniques from topological data analysis (namely, persistent homology) to automate the detection and characterization of convective/cyclic/chiral flows over networks, particularly those that arise for irreversible Markov chains (MCs). As two applications, we study convection cycles arising under the PageRank algorithm, and we investigate chiral edges flows for a stochastic model of a bi-monomer's configuration dynamics. Our experiments highlight how system parameters---e.g., the teleportation rate for PageRank and the transition rates of external and internal state changes for a monomer---can act as homology regularizers of convection, which we summarize with persistence barcodes and homological bifurcation diagrams. Our approach establishes a new connection between the study of convection cycles and homology, the branch of mathematics that formally studies cycles, which has diverse potential applications throughout the sciences and engineering.

Keywords: homology, persistent homolgy, markov chains, convection cycles, filtration

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Authors: Radhia Toujani, Jalel Akaichi

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In recent years, the study of community detection in social networks has received great attention. The hierarchical structure of the network leads to the emergence of the convergence to a locally optimal community structure. In this paper, we aim to avoid this local optimum in the introduced hybrid hierarchical method. To achieve this purpose, we present an objective function where we incorporate the value of structural and semantic similarity based modularity and a metaheuristic namely bees colonies algorithm to optimize our objective function on both hierarchical level divisive and agglomerative. In order to assess the efficiency and the accuracy of the introduced hybrid bee colony model, we perform an extensive experimental evaluation on both synthetic and real networks.

Keywords: social network, community detection, agglomerative hierarchical clustering, divisive hierarchical clustering, similarity, modularity, metaheuristic, bee colony

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Authors: Christopher G. Harris, Guy D. Eslick

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Purpose: Lobular carcinoma in situ (LCIS) is a known risk factor for breast cancer of unclear significance when detected in association with invasive carcinoma. This meta-analysis aims to determine the impact of LCIS on local recurrence risk for individuals with breast cancer treated with breast conservation therapy to help guide appropriate treatment strategies. Methods: We identified relevant studies from five electronic databases. Studies were deemed suitable for inclusion where they compared patients with invasive breast cancer and concurrent LCIS to those with breast cancer alone, all patients underwent breast conservation therapy (lumpectomy with adjuvant radiation therapy), and local recurrence was evaluated. Recurrence data were pooled by use of a random effects model. Results: From 1488 citations screened by our search, 8 studies were deemed suitable for inclusion. These studies comprised of 908 cases and 10638 controls. Median follow-up time was 90 months. There was a significantly increased overall risk of local breast cancer recurrence for individuals with LCIS in association with breast cancer following breast conservation therapy [pOR 1.87; 95% CI 1.14-3.04; p = 0.012]. The risk of local recurrence was non-significantly increased at 5 [pOR 1.09; 95% CI 0.48-2.48; p = 0.828] and 10 years [pOR 1.90; 95% CI 0.89-4.06; p = 0.096]. Conclusions: Individuals with LCIS in association with invasive breast cancer have an increased risk of local recurrence following breast conservation therapy. This supports consideration of aggressive local control of LCIS by way of completion mastectomy or re-excision for certain high-risk patients.

Keywords: breast cancer, breast conservation therapy, lobular carcinoma in situ, lobular neoplasia, local recurrence, meta-analysis

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Authors: N. P. Yadav, Deepti Verma

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This paper focuses on the mathematical modeling for solidification of Al alloy in a cube mould cavity to study the solidification behavior of casting process. The parametric investigation of solidification process inside the cavity was performed by using computational solidification/melting model coupled with Volume of fluid (VOF) model. The implicit filling algorithm is used in this study to understand the overall process from the filling stage to solidification in a model metal casting process. The model is validated with past studied at same conditions. The solidification process are analyzed by including the effect of pouring velocity and temperature of liquid metal, effect of wall temperature as well natural convection from the wall and geometry of the cavity. These studies show the possibility of various defects during solidification process.

Keywords: buoyancy driven flow, natural convection driven flow, residual flow, secondary flow, volume of fluid

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Authors: Alexander Guzman Urbina, Atsushi Aoyama

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The sustainability of traditional technologies employed in energy and chemical infrastructure brings a big challenge for our society. Making decisions related with safety of industrial infrastructure, the values of accidental risk are becoming relevant points for discussion. However, the challenge is the reliability of the models employed to get the risk data. Such models usually involve large number of variables and with large amounts of uncertainty. The most efficient techniques to overcome those problems are built using Artificial Intelligence (AI), and more specifically using hybrid systems such as Neuro-Fuzzy algorithms. Therefore, this paper aims to introduce a hybrid algorithm for risk assessment trained using near-miss accident data. As mentioned above the sustainability of traditional technologies related with energy and chemical infrastructure constitutes one of the major challenges that today’s societies and firms are facing. Besides that, the adaptation of those technologies to the effects of the climate change in sensible environments represents a critical concern for safety and risk management. Regarding this issue argue that social consequences of catastrophic risks are increasing rapidly, due mainly to the concentration of people and energy infrastructure in hazard-prone areas, aggravated by the lack of knowledge about the risks. Additional to the social consequences described above, and considering the industrial sector as critical infrastructure due to its large impact to the economy in case of a failure the relevance of industrial safety has become a critical issue for the current society. Then, regarding the safety concern, pipeline operators and regulators have been performing risk assessments in attempts to evaluate accurately probabilities of failure of the infrastructure, and consequences associated with those failures. However, estimating accidental risks in critical infrastructure involves a substantial effort and costs due to number of variables involved, complexity and lack of information. Therefore, this paper aims to introduce a well trained algorithm for risk assessment using deep learning, which could be capable to deal efficiently with the complexity and uncertainty. The advantage point of the deep learning using near-miss accidents data is that it could be employed in risk assessment as an efficient engineering tool to treat the uncertainty of the risk values in complex environments. The basic idea of using a Near-Miss Deep Learning Approach for Neuro-Fuzzy Risk Assessment in Pipelines is focused in the objective of improve the validity of the risk values learning from near-miss accidents and imitating the human expertise scoring risks and setting tolerance levels. In summary, the method of Deep Learning for Neuro-Fuzzy Risk Assessment involves a regression analysis called group method of data handling (GMDH), which consists in the determination of the optimal configuration of the risk assessment model and its parameters employing polynomial theory.

Keywords: deep learning, risk assessment, neuro fuzzy, pipelines

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Authors: Margarita Mayoral-Villa, J. Klapp, L. Di G. Sigalotti, J. E. V. Guzmán

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Automated learning techniques are widely applied in the energy sector to address challenging problems from a practical point of view. To this end, we discuss the implementation of a Message Passing algorithm (MPNN)within a Graph Neural Network(GNN)to leverage the neighborhood of a set of nodes during the aggregation process. This approach enables the characterization of multiphase diffusion processes in the reservoir, such that the flow paths underlying the interconnections between multiple wells may be inferred from previously available data on flow rates and bottomhole pressures. The results thus obtained compare favorably with the predictions produced by the Reduced Order Capacitance-Resistance Models (CRM) and suggest the potential of MPNNs to enhance the robustness of the forecasts while improving the computational efficiency.

Keywords: multiphase diffusion, message passing neural network, well interconnection, interwell connectivity, graph neural network, capacitance-resistance models

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Authors: Premjit K. Sanjram, Gagan Jakhotiya, Apoorv Goyal, Shanu Shukla

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In psychological research, laboratory experiments often face the trade-off issue between experimental control and mundane realism. With the advent of Immersive Virtual Environment Technology (IVET), this issue seems to be at bay. However there is a growing challenge within the IVET itself to design and develop system or software that captures the psychological phenomenon of everyday lives. One such phenomena that is of growing interest is ‘media-multitasking’ To aid laboratory researches in media-multitasking this paper introduces Query Task Modulator (QTM), a computerized experimentation system to study media-multitasking behavior in a controlled laboratory environment. The system provides a computerized platform in conducting an experiment for experimenters to study media-multitasking in which participants will be involved in a query task. The system has Instant Messaging, E-mail, and Voice Call features. The answers to queries are provided on the left hand side information panel where participants have to search for it and feed the information in the respective communication media blocks as fast as possible. On the whole the system will collect multitasking behavioral data. To analyze performance there is a separate output table that records the reaction times and responses of the participants individually. Information panel and all the media blocks will appear on a single window in order to ensure multi-modality feature in media-multitasking and equal emphasis on all the tasks (thus avoiding prioritization to a particular task). The paper discusses the development of QTM in the light of current techniques of studying media-multitasking.

Keywords: experimentation system, human performance, media-multitasking, query-task

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Authors: Neda Navidi, Rene Jr. Landry

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Vehicle tracking and accident recognizing are considered by many industries like insurance and vehicle rental companies. The main goal of this paper is to detect the location of a car accident by combining different methods. The methods, which are considered in this paper, are Global Navigation Satellite Systems/Inertial Measurement Units (GNSS/IMU)-based navigation and vehicle accident detection algorithms. They are expressed by a set of raw measurements, which are obtained from a designed integrator black box using GNSS and inertial sensors. Another concern of this paper is the definition of accident detection algorithm based on its jerk to identify the position of that accident. In fact, the results convinced us that, even in GNSS blockage areas, the position of the accident could be detected by GNSS/INS integration with 50% improvement compared to GNSS stand alone.

Keywords: driver behavior monitoring, integration, IMU, GNSS, monitoring, tracking

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Authors: Paul Okonkwo, Howard Smith

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A study to estimate the size of the cabin and major aircraft components as well as detect and avoid interference between internally placed components and the external surface, during the conceptual design synthesis and optimisation to explore the design space of a BWB, was conducted. Sizing of components follows the Bradley cabin sizing and rubber engine scaling procedures to size the cabin and engine respectively. The interference detection and avoidance algorithm relies on the ability of the Class Shape Transform parameterisation technique to generate polynomial functions of the surfaces of a BWB aircraft configuration from the sizes of the cabin and internal objects using few variables. Interference detection is essential in packaging of non-conventional configuration like the BWB because of the non-uniform airfoil-shaped sections and resultant varying internal space. The unique configuration increases the need for a methodology to prevent objects from being placed in locations that do not sufficiently enclose them within the geometry.

Keywords: packaging, optimisation, BWB, parameterisation, aircraft conceptual design

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Authors: Mercedes Bertotto, Marcelo Bello, Hector Goicoechea, Veronica Fusca

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The National Service of Agri-Food Health and Quality (SENASA), controls honey to detect contamination by synthetic or natural chemical substances and establishes and controls the traceability of the product. The utility of near-infrared spectroscopy for the detection of adulteration of honey with high fructose corn syrup (HFCS) was investigated. First of all, a mixture of different authentic artisanal Argentinian honey was prepared to cover as much heterogeneity as possible. Then, mixtures were prepared by adding different concentrations of high fructose corn syrup (HFCS) to samples of the honey pool. 237 samples were used, 108 of them were authentic honey and 129 samples corresponded to honey adulterated with HFCS between 1 and 10%. They were stored unrefrigerated from time of production until scanning and were not filtered after receipt in the laboratory. Immediately prior to spectral collection, honey was incubated at 40°C overnight to dissolve any crystalline material, manually stirred to achieve homogeneity and adjusted to a standard solids content (70° Brix) with distilled water. Adulterant solutions were also adjusted to 70° Brix. Samples were measured by NIR spectroscopy in the range of 650 to 7000 cm⁻¹. The technique of specular reflectance was used, with a lens aperture range of 150 mm. Pretreatment of the spectra was performed by Standard Normal Variate (SNV). The ant colony optimization genetic algorithm sample selection (ACOGASS) graphical interface was used, using MATLAB version 5.3, to select the variables with the greatest discriminating power. The data set was divided into a validation set and a calibration set, using the Kennard-Stone (KS) algorithm. A combined method of Potential Functions (PF) was chosen together with Partial Least Square Linear Discriminant Analysis (PLS-DA). Different estimators of the predictive capacity of the model were compared, which were obtained using a decreasing number of groups, which implies more demanding validation conditions. The optimal number of latent variables was selected as the number associated with the minimum error and the smallest number of unassigned samples. Once the optimal number of latent variables was defined, we proceeded to apply the model to the training samples. With the calibrated model for the training samples, we proceeded to study the validation samples. The calibrated model that combines the potential function methods and PLSDA can be considered reliable and stable since its performance in future samples is expected to be comparable to that achieved for the training samples. By use of Potential Functions (PF) and Partial Least Square Linear Discriminant Analysis (PLS-DA) classification, authentic honey and honey adulterated with HFCS could be identified with a correct classification rate of 97.9%. The results showed that NIR in combination with the PT and PLS-DS methods can be a simple, fast and low-cost technique for the detection of HFCS in honey with high sensitivity and power of discrimination.

Keywords: adulteration, multivariate analysis, potential functions, regression

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Authors: S. A. Naeini, A. Khalili

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Nowadays, tunnels with different applications are developed, and most of them are related to subway tunnels. The excavation of shallow tunnels that pass under municipal utilities is very important, and the surface settlement control is an important factor in the design. The study sought to analyze the settlement and also to find an appropriate model in order to predict the behavior of the tunnel in Tehran subway line-3. The displacement in these sections is also determined by using numerical analyses and numerical modeling. In addition, the Adaptive Neuro-Fuzzy Inference System (ANFIS) method is utilized by Hybrid training algorithm. The database pertinent to the optimum network was obtained from 46 subway tunnels in Iran and Turkey which have been constructed by the new Austrian tunneling method (NATM) with similar parameters based on type of their soil. The surface settlement was measured, and the acquired results were compared to the predicted values. The results disclosed that computing intelligence is a good substitute for numerical modeling.

Keywords: settlement, Subway Line, FLAC3D, ANFIS Method

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Authors: Waziri Victor Onomza, John K. Alhassan, Idris Ismaila, Noel Dogonyaro Moses

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This paper describes the problem of building secure computational services for encrypted information in the Cloud Computing without decrypting the encrypted data; therefore, it meets the yearning of computational encryption algorithmic aspiration model that could enhance the security of big data for privacy, confidentiality, availability of the users. The cryptographic model applied for the computational process of the encrypted data is the Fully Homomorphic Encryption Scheme. We contribute theoretical presentations in high-level computational processes that are based on number theory and algebra that can easily be integrated and leveraged in the Cloud computing with detail theoretic mathematical concepts to the fully homomorphic encryption models. This contribution enhances the full implementation of big data analytics based cryptographic security algorithm.

Keywords: big data analytics, security, privacy, bootstrapping, homomorphic, homomorphic encryption scheme

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Authors: Sondos M. Hammad, Sherin M. ElGokhy, Mahmoud M. Fahmy, Elsayed A. Sallam

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In this paper, an ensemble-based Support Vector Machine (SVM) classification approach is proposed. It is used for miRNA prediction. Three problems, commonly associated with previous approaches, are alleviated. These problems arise due to impose assumptions on the secondary structural of premiRNA, imbalance between the numbers of the laboratory checked miRNAs and the pseudo-hairpins, and finally using a training data set that does not consider all the varieties of samples in different species. We aggregate the predicted outputs of three well-known SVM classifiers; namely, Triplet-SVM, Virgo and Mirident, weighted by their variant features without any structural assumptions. An additional SVM layer is used in aggregating the final output. The proposed approach is trained and then tested with balanced data sets. The results of the proposed approach outperform the three base classifiers. Improved values for the metrics of 88.88% f-score, 92.73% accuracy, 90.64% precision, 96.64% specificity, 87.2% sensitivity, and the area under the ROC curve is 0.91 are achieved.

Keywords: MiRNAs, SVM classification, ensemble algorithm, assumption problem, imbalance data

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Authors: Abdul Basit Kiani, Maryam Kiani

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Content Management Systems (CMS) have transformed the landscape of web development by providing an accessible and efficient platform for creating and managing digital content. This abstract explores the key features and benefits of CMS in web development, highlighting its impact on website creation and maintenance. CMS offers a user-friendly interface that empowers individuals to create, edit, and publish content without requiring extensive technical knowledge. With customizable templates and themes, users can personalize the design and layout of their websites, ensuring a visually appealing online presence. Furthermore, CMS facilitates efficient content organization through categorization and tagging, enabling visitors to navigate and search for information effortlessly. It also supports version control, allowing users to track and manage revisions effectively. Scalability is a notable advantage of CMS, as it offers a wide range of plugins and extensions to integrate additional features into websites. From e-commerce functionality to social media integration, CMS adapts to evolving business needs. Additionally, CMS enhances collaborative workflows by allowing multiple user roles and permissions. This enables teams to collaborate effectively on content creation and management, streamlining processes and ensuring smooth coordination. In conclusion, CMS serves as a powerful tool in web development, simplifying content creation, customization, organization, scalability, and collaboration. With CMS, individuals and businesses can create dynamic and engaging websites, establishing a strong online presence with ease.

Keywords: web development, content management systems, information technology, programming

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Authors: Andrej Kocjan

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Introduction: Mobility has been shown to play an important role of health and quality of life among older adults. Falls, which are often related to decreased mobility, as well as to neuromuscular deficits, represent the most common injury among older adults. Fall risk has been shown to increase with reduced lower extremity flexibility. The aim of the paper is to assess the importance of flexibility training on joint range of motion and functional performance among elderly population. Methods: We performed literature research on PubMed and evaluated articles published until 2000. The articles found in the search strategy were also added. The population of interest included older adults (≥ 65 years of age). Results: Flexibility training programs still represent an important part of several rehabilitation programs. Static stretching and proprioceptive neuromuscular facilitation are the most frequently used techniques to improve the length of the muscle-tendon complex. Although the effectiveness of type of stretching seems to be related to age and gender, static stretching is a more appropriate technique to enhance shoulder, hip, and ankle range of motion in older adults. Stretching should be performed in multiple sets with holds of more than 60 seconds for a single muscle group. Conclusion: The literature suggests that flexibility training is an effective method to increase joint range of motion in older adults. In the light of increased functional outcome, activities such as strengthening, balance, and aerobic exercises should be incorporated into a training program for older people. Due to relatively little published literature, it is still not possible to prescribe detailed recommendations regarding flexibility training for older adults.

Keywords: elderly, exercise, flexibility, falls

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Authors: Po-Jung Huang, Chi-Ching Lee, Bertrand Chin-Ming Tan, Yuan-Ming Yeh, Julie Lichieh Chu, Tin-Wen Chen, Cheng-Yang Lee, Ruei-Chi Gan, Hsuan Liu, Petrus Tang

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Whole-exome sequencing focuses on the protein coding regions of disease/cancer associated genes based on a priori knowledge is the most cost-effective method to study the association between genetic alterations and disease. Recent advances in high throughput sequencing technologies and proteomic techniques has provided an opportunity to integrate genomics and proteomics, allowing readily detectable mutated peptides corresponding to mutated genes. Since sequence database search is the most widely used method for protein identification using Mass spectrometry (MS)-based proteomics technology, a mutant proteome database is required to better approximate the real protein pool to improve disease-associated mutated protein identification. Large-scale whole exome/genome sequencing studies were launched by National Cancer Institute (NCI), Broad Institute, and The Cancer Genome Atlas (TCGA), which provide not only a comprehensive report on the analysis of coding variants in diverse samples cell lines but a invaluable resource for extensive research community. No existing database is available for the collection of mutant protein sequences related to the identified variants in these studies. CMPD is designed to address this issue, serving as a bridge between genomic data and proteomic studies and focusing on protein sequence-altering variations originated from both germline and cancer-associated somatic variations.

Keywords: TCGA, cancer, mutant, proteome

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Authors: Justin Bin

Abstract:

Artificial intelligence (AI) has been rapidly rising in usage recently, already active in the daily lives of millions, from distinguished AIs like the popular ChatGPT or Siri to more obscure, inconspicuous AIs like those used in social media or internet search engines. As upcoming generations grow immersed in emerging technology, AI will play a vital role in their development. Namely, the education sector, an influential portion of a person’s early life as a student, faces a vast ocean of possibilities concerning the implementation of AI. The main purpose of this study is to analyze the effect that AI will have on the future of the educational field. More particularly, this study delves deeper into the following three categories: school admissions, the productivity of students, and ethical concerns (role of human teachers, purpose of schooling itself, and significance of diplomas). This study synthesizes research and data on the current effects of AI on education from various published literature sources and journals, as well as estimates on further AI potential, in order to determine the main, overarching effects it will have on the future of education. For this study, a systematic organization of data in terms of type (quantitative vs. qualitative), the magnitude of effect implicated, and other similar factors were implemented within each area of significance. The results of the study suggest that AI stands to change all the beforementioned subgroups. However, its specific effects vary in magnitude and favorability (beneficial or harmful) and will be further discussed. The results discussed will reveal to those affiliated with the education field, such as teachers, counselors, or even parents of students, valuable information on not just the projected possibilities of AI in education but the effects of those changes moving forward.

Keywords: artificial intelligence, education, schools, teachers

Procedia PDF Downloads 523

Authors: M. H. Korayem, Sh. Ameri, N. Yousefi Lademakhi

Abstract:

To ensure the stability of closed-loop nonlinear model predictive control (NMPC) within a finite horizon, there is a need for appropriate design terminal ingredients, which can be a time-consuming and challenging effort. Otherwise, in order to ensure the stability of the control system, it is necessary to consider an infinite predictive horizon. Increasing the prediction horizon increases computational demand and slows down the implementation of the method. In this study, a new technique has been proposed to ensure system stability without terminal ingredients. This technique has been employed in the design of the NMPC algorithm, leading to a reduction in the computational complexity of designing terminal ingredients and computational burden. The studied system is a wheeled mobile robot (WMR) subjected to non-holonomic constraints. Simulation has been investigated for two problems: trajectory tracking and adjustment mode.

Keywords: wheeled mobile robot, nonlinear model predictive control, stability, without terminal ingredients

Procedia PDF Downloads 92

Authors: I. Hosni, L. Bennaceur Farah, N. Saber, R Bennaceur

Abstract:

Soil moisture content is a key variable in many environmental sciences. Even though it represents a small proportion of the liquid freshwater on Earth, it modulates interactions between the land surface and the atmosphere, thereby influencing climate and weather. Accurate modeling of the above processes depends on the ability to provide a proper spatial characterization of soil moisture. The measurement of soil moisture content allows assessment of soil water resources in the field of hydrology and agronomy. The second parameter in interaction with the radar signal is the geometric structure of the soil. Most traditional electromagnetic models consider natural surfaces as single scale zero mean stationary Gaussian random processes. Roughness behavior is characterized by statistical parameters like the Root Mean Square (RMS) height and the correlation length. Then, the main problem is that the agreement between experimental measurements and theoretical values is usually poor due to the large variability of the correlation function, and as a consequence, backscattering models have often failed to predict correctly backscattering. In this study, surfaces are considered as band-limited fractal random processes corresponding to a superposition of a finite number of one-dimensional Gaussian process each one having a spatial scale. Multiscale roughness is characterized by two parameters, the first one is proportional to the RMS height, and the other one is related to the fractal dimension. Soil moisture is related to the complex dielectric constant. This multiscale description has been adapted to two-dimensional profiles using the bi-dimensional wavelet transform and the Mallat algorithm to describe more correctly natural surfaces. We characterize the soil surfaces and sub-surfaces by a three layers geo-electrical model. The upper layer is described by its dielectric constant, thickness, a multiscale bi-dimensional surface roughness model by using the wavelet transform and the Mallat algorithm, and volume scattering parameters. The lower layer is divided into three fictive layers separated by an assumed plane interface. These three layers were modeled by an effective medium characterized by an apparent effective dielectric constant taking into account the presence of air pockets in the soil. We have adopted the 2D multiscale three layers small perturbations model including, firstly air pockets in the soil sub-structure, and then a vegetable canopy in the soil surface structure, that is to simulate the radar backscattering. A sensitivity analysis of backscattering coefficient dependence on multiscale roughness and new soil moisture has been performed. Later, we proposed to change the dielectric constant of the multilayer medium because it takes into account the different moisture values of each layer in the soil. A sensitivity analysis of the backscattering coefficient, including the air pockets in the volume structure with respect to the multiscale roughness parameters and the apparent dielectric constant, was carried out. Finally, we proposed to study the behavior of the backscattering coefficient of the radar on a soil having a vegetable layer in its surface structure.

Keywords: multiscale, bidimensional, wavelets, backscattering, multilayer, SPM, air pockets

Procedia PDF Downloads 126

Authors: Alina Trifan, António J. R. Neves

Abstract:

Although most digital cameras acquire images in a raw format, based on a Color Filter Array that arranges RGB color filters on a square grid of photosensors, most image compression techniques do not use the raw data; instead, they use the rgb result of an interpolation algorithm of the raw data. This approach is inefficient and by performing a lossless compression of the raw data, followed by pixel interpolation, digital cameras could be more power efficient and provide images with increased resolution given that the interpolation step could be shifted to an external processing unit. In this paper, we conduct a survey on the use of lossless compression algorithms with raw Bayer images. Moreover, in order to reduce the effect of the transition between colors that increase the entropy of the raw Bayer image, we split the image into three new images corresponding to each channel (red, green and blue) and we study the same compression algorithms applied to each one individually. This simple pre-processing stage allows an improvement of more than 15% in predictive based methods.

Keywords: bayer image, CFA, lossless compression, image coding standards

Procedia PDF Downloads 322

Authors: Lakmali Anthony, Madeline Gillies

Abstract:

Purpose: Decreasing mortality has led to increased focus on patient-reported outcomes such as quality of life (QoL) in breast cancer. Breast cancer patients often have decreased QoL even after treatment is complete. This systematic review of the literature aims to identify psychosocial factors associated with decreased QoL in post-treatment breast cancer patients. Methodology: This systematic review was performed in accordance with the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses recommendations. The search was conducted in MEDLINE, EMBASE, and PsychINFO using MeSH headings. The two authors screened studies for relevance and extracted data. Results: Seventeen studies were identified, including 3,150 total participants (mean = 197) with a mean age of 51.9 years. There was substantial heterogeneity in measures of QoL. The most common was the European Organisation for Research and Treatment of Cancer QLQ-C30 (n=7, 41.1%). Most studies (n=12, 70.5%) found that emotional distress correlated with poor QoL, while 3 found no significant association. The most common measure of emotional distress was the Hospital Anxiety and Depression Scale (n=12, 70.5%). Other psychosocial factors associated with QoL were unmet needs, problematic social support, and negative affect. Clinicopathologic determinants included mastectomy without reconstruction, stage IV disease, and adjuvant chemotherapy. Conclusion: This systematic review provides a summary of the psychosocial determinants of poor QoL in post-treatment breast cancer patients, as well as the most commonly reported measures of these. An understanding of these potentially modifiable determinants of poor outcome is pivotal to the provision of quality, patient-centred care in surgical oncology.

Keywords: breast cancer, quality of life, psychosocial determinants, cancer surgery

Procedia PDF Downloads 78

Authors: Musa H. Arslan, Murat Ceylan, Tayfun Koyuncu

Abstract:

In this study, an artificial intelligence-based (ANN based) analytical method has been developed for analyzing earthquake performances of the reinforced concrete (RC) buildings. 66 RC buildings with four to ten storeys were subjected to performance analysis according to the parameters which are the existing material, loading and geometrical characteristics of the buildings. The selected parameters have been thought to be effective on the performance of RC buildings. In the performance analyses stage of the study, level of performance possible to be shown by these buildings in case of an earthquake was determined on the basis of the 4-grade performance levels specified in Turkish Earthquake Code- 2007 (TEC-2007). After obtaining the 4-grade performance level, selected 23 parameters of each building have been matched with the performance level. In this stage, ANN-based fast evaluation algorithm mentioned above made an economic and rapid evaluation of four to ten storey RC buildings. According to the study, the prediction accuracy of ANN has been found about 74%.

Keywords: artificial intelligence, earthquake, performance, reinforced concrete

Procedia PDF Downloads 463