Search results for: independent learning

Authors: Mahdi Bazarganigilani

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Computer-aided diagnosis (CAD) systems can play crucial roles in diagnosing crucial diseases such as breast cancer at the earliest. In this paper, a CAD system for the diagnosis of breast cancer was introduced and evaluated. This CAD system was developed by using spatio-temporal analysis of data on a set of consecutive thermographic images by employing wavelet transformation. By using this analysis, a very accurate machine learning model using random forest was obtained. The final results showed a promising accuracy of 91% in terms of the F1 measure indicator among 200 patients' sample data. The CAD system was further extended to obtain a detailed analysis of the effect of smaller sub-areas of each breast on the occurrence of cancer.

Keywords: computer-aided diagnosis systems, thermographic analysis, spatio-temporal analysis, image processing, machine learning

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Authors: Ibrahim Arslan

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Since 1984, Turkey has experienced a significant transformation in legal and economic matters. The most consequential examples of this transformation in recent years are the renewal of the Commercial Code and the Check Act. Nowadays, the commercial activity is not limited within the boundaries of the country; on the contrary, as required by the global economy, it has an international dimension. For this reason, unlike some other legal principles, the rules regulating the commercial life should be compatible with the international standards as much as possible. Otherwise the development possibility in the global markets will be limited. The Check Act has been adopted in 2009 and the Commercial Code has been adopted in 2011. The Commercial Code has been entered into force on 1 July 2012. The international dimension of check is in-disputable for it is based on the Geneva Convention. However, the Turkish business life has created a unique application of this legal tool. This application is called “post-date” checks. Indeed the majority of the checks being used in the market are post-dated checks. The holders of these checks have waited the date written on the check for presentation and collection. Thus, the actual situation has occurred. This actual situation has been legitimized via Check Act No. 5941 and post dated checks have gained a legal status. In the preparation of the new the Turkish Commercial Code one of the goals is "to ensure that the Turkish commercial law becomes a part of the international market". To achieve this goal, significant changes have been made especially concerning the independent external audition of the corporations, the board structure and public disclosure regulations. These changes aim to facilitate the internationalization of Turkish corporations as well as intensification of foreign direct investments through foreign capital. Although the target has been determined this way, after the adoption but five days before the entry into force of the Turkish Commercial Code No. 6102, a law made backward going alterations concerning independent external audition and public disclosure regulations. Turkish Commercial Code has been currently in force with its altered status. Both the regulations in the Check Act as well as the changes in the Commercial Code are not compatible with the goals introduced by rationale “to ensure Turkish commercial law to be a part of the international market” as such.

Keywords: Turkish Commercial Code No. 6102, Turkish Check Act, “post-date” checks, legal changes

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Authors: Assel Jaxylykova, Alexnder Pak

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This study utilized a Self-Organizing Map (SOM) for unsupervised learning on the CONLL-2003 dataset for Named Entity Recognition (NER). The process involved encoding words into 300-dimensional vectors using FastText. These vectors were input into a SOM grid, where training adjusted node weights to minimize distances. The SOM provided a topological representation for identifying and clustering named entities, demonstrating its efficacy without labeled examples. Results showed an F1-measure of 0.86, highlighting SOM's viability. Although some methods achieve higher F1 measures, SOM eliminates the need for labeled data, offering a scalable and efficient alternative. The SOM's ability to uncover hidden patterns provides insights that could enhance existing supervised methods. Further investigation into potential limitations and optimization strategies is suggested to maximize benefits.

Keywords: named entity recognition, natural language processing, self-organizing map, CONLL-2003, semantics

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Authors: Varun Agarwal

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Introduction: With the advent of personalized medicine, histopathological review of whole slide images (WSIs) for cancer diagnosis presents an exceedingly time-consuming, complex task. Specifically, detecting metastatic regions in WSIs of sentinel lymph node biopsies necessitates a full-scanned, holistic evaluation of the image. Thus, digital pathology, low-level image manipulation algorithms, and machine learning provide significant advancements in improving the efficiency and accuracy of WSI analysis. Using Camelyon16 data, this paper proposes a deep learning pipeline to automate and ameliorate breast cancer metastasis localization and WSI classification. Methodology: The model broadly follows five stages -region of interest detection, WSI partitioning into image tiles, convolutional neural network (CNN) image-segment classifications, probabilistic mapping of tumor localizations, and further processing for whole WSI classification. Transfer learning is applied to the task, with the implementation of Inception-ResNetV2 - an effective CNN classifier that uses residual connections to enhance feature representation, adding convolved outputs in the inception unit to the proceeding input data. Moreover, in order to augment the performance of the transfer learning CNN, a stack of convolutional denoising autoencoders (CDAE) is applied to produce embeddings that enrich image representation. Through a saliency-detection algorithm, visual training segments are generated, which are then processed through a denoising autoencoder -primarily consisting of convolutional, leaky rectified linear unit, and batch normalization layers- and subsequently a contrast-normalization function. A spatial pyramid pooling algorithm extracts the key features from the processed image, creating a viable feature map for the CNN that minimizes spatial resolution and noise. Results and Conclusion: The simplified and effective architecture of the fine-tuned transfer learning Inception-ResNetV2 network enhanced with the CDAE stack yields state of the art performance in WSI classification and tumor localization, achieving AUC scores of 0.947 and 0.753, respectively. The convolutional feature retention and compilation with the residual connections to inception units synergized with the input denoising algorithm enable the pipeline to serve as an effective, efficient tool in the histopathological review of WSIs.

Keywords: breast cancer, convolutional neural networks, metastasis mapping, whole slide images

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Authors: Deeman Yousif Mahmood, Mohammed Abdullah Hussein

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The goal of a network-based intrusion detection system is to classify activities of network traffics into two major categories: normal and attack (intrusive) activities. Nowadays, data mining and machine learning plays an important role in many sciences; including intrusion detection system (IDS) using both supervised and unsupervised techniques. However, one of the essential steps of data mining is feature selection that helps in improving the efficiency, performance and prediction rate of proposed approach. This paper applies unsupervised K-means clustering algorithm with information gain (IG) for feature selection and reduction to build a network intrusion detection system. For our experimental analysis, we have used the new NSL-KDD dataset, which is a modified dataset for KDDCup 1999 intrusion detection benchmark dataset. With a split of 60.0% for the training set and the remainder for the testing set, a 2 class classifications have been implemented (Normal, Attack). Weka framework which is a java based open source software consists of a collection of machine learning algorithms for data mining tasks has been used in the testing process. The experimental results show that the proposed approach is very accurate with low false positive rate and high true positive rate and it takes less learning time in comparison with using the full features of the dataset with the same algorithm.

Keywords: information gain (IG), intrusion detection system (IDS), k-means clustering, Weka

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Authors: Usama Shahid

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Be it written documents, news columns, or students' essays, verifying the content can be a time-consuming task. Apart from the spelling and grammar mistakes, the proofreader is also supposed to verify whether the content included in the essay or document is relevant or not. The irrelevant content in any document or essay is referred to as off-topic text and in this paper, we will address the problem of off-topic text detection from a document using machine learning techniques. Our study aims to identify the off-topic content from a document using Echo state network model and we will also compare data with other models. The previous study uses Convolutional Neural Networks and TFIDF to detect off-topic text. We will rearrange the existing datasets and take new classifiers along with new word embeddings and implement them on existing and new datasets in order to compare the results with the previously existing CNN model.

Keywords: off topic, text detection, eco state network, machine learning

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Authors: Tatyana Gavrilova, Vadim Onufriev

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Knowledge-based systems’ design requires the developer’s owning the advanced analytical skills. The efficient development of that skills within university courses needs a deep understanding of main pitfalls and drawbacks, which students usually make during their analytical work in form of visual modeling. Thus, it was necessary to hold an analysis of 5-th year students’ learning exercises within courses of 'Intelligent systems' and 'Knowledge engineering' in Saint-Petersburg Polytechnic University. The analysis shows that both lack of system thinking skills and methodological mistakes in course design cause the errors that are discussed in the paper. The conclusion contains an exploration of the issues and topics necessary and sufficient for the implementation of the improved practices in educational design for future curricula of teaching programs.

Keywords: knowledge based systems, knowledge engineering, students’ errors, visual modeling

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Authors: Rezvan Ravanfar Haghighi, V. C. Vani, Suresh Perumal, Sabyasachi Chatterjee, Pratik Kumar

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X-ray attenuation coefficient [µ(E)] of any substance, for energy (E), is a sum of the contributions from the Compton scattering [ μCom(E)] and photoelectric effect [µPh(E)]. In terms of the, electron density (ρe) and the effective atomic number (Zeff) we have µCom(E) is proportional to [(ρe)fKN(E)] while µPh(E) is proportional to [(ρeZeffx)/Ey] with fKN(E) being the Klein-Nishina formula, with x and y being the exponents for photoelectric effect. By taking the sample's HU at two different excitation voltages (V=V1, V2) of the CT machine, we can solve for X=ρe, Y=ρeZeffx from these two independent equations, as is attempted in DECT inversion. Since µCom(E) and µPh(E) are both energy dependent, the coefficients of inversion are also dependent on (a) the source spectrum S(E,V) and (b) the detector efficiency D(E) of the CT machine. In the present paper we tabulate these coefficients of inversion for different practical manifestations of S(E,V) and D(E). The HU(V) values from the CT follow: <µ(V)>=<µw(V)>[1+HU(V)/1000] where the subscript 'w' refers to water and the averaging process <….> accounts for the source spectrum S(E,V) and the detector efficiency D(E). Linearity of μ(E) with respect to X and Y implies that (a) <µ(V)> is a linear combination of X and Y and (b) for inversion, X and Y can be written as linear combinations of two independent observations <µ(V1)>, <µ(V2)> with V1≠V2. These coefficients of inversion would naturally depend upon S(E, V) and D(E). We numerically investigate this dependence for some practical cases, by taking V = 100 , 140 kVp, as are used for cardiological investigations. The S(E,V) are generated by using the Boone-Seibert source spectrum, being superposed on aluminium filters of different thickness lAl with 7mm≤lAl≤12mm and the D(E) is considered to be that of a typical Si[Li] solid state and GdOS scintilator detector. In the values of X and Y, found by using the calculated inversion coefficients, errors are below 2% for data with solutions of glycerol, sucrose and glucose. For low Zeff materials like propionic acid, Zeffx is overestimated by 20% with X being within1%. For high Zeffx materials like KOH the value of Zeffx is underestimated by 22% while the error in X is + 15%. These imply that the source may have additional filtering than the aluminium filter specified by the manufacturer. Also it is found that the difference in the values of the inversion coefficients for the two types of detectors is negligible. The type of the detector does not affect on the DECT inversion algorithm to find the unknown chemical characteristic of the scanned materials. The effect of the source should be considered as an important factor to calculate the coefficients of inversion.

Keywords: attenuation coefficient, computed tomography, photoelectric effect, source spectrum

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Authors: Di Luo, Maria Buchberger, Anja Pickhard

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Objectives: The purpose of this study was to assess and compare the diagnostic accuracy of four common morphological approaches, including sonography, computed tomography (CT), positron emission tomography/computed tomography (PET/CT), and magnetic resonance imaging (MRI) for the evaluation of cervical lymph node metastases in head and neck squamous cell carcinoma (HNSCC) patients. Material and Methods: Included in this retrospective study were 26 patients diagnosed with HNSCC between 2010 and 2011 who all underwent sonography, CT, PET/CT, and MRI imaging before neck dissection. Morphological data were compared to the corresponding histopathological results. Statistical analysis was performed with SPSS statistic software (version 26.0), calculating sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy for detection of cervical lymph node metastases. Results: The 5-year survival rate of the patient collective was 55.5%.Risk factors for survival included initial primary tumor stage, initial lymph node stage, initial metastasis status, and therapeutic approaches. Cox regression showed initial metastasis status(HR 8.671, 95%CI 1.316-57.123, p=0.025) and therapeutic approaches(HR 6.699, 95%CI 1.746-25.700, p=0.006)to be independent predictive risk factors for survival. Sensitivity was highest for MRI (96% compared to 85% for sonography and 89% for CT and PET/CT). Specificity was comparable with 95 % for CT and 98 % for sonography and PET/CT, but only 68% for MRI. While the MRI showed the least PPV (34%) compared to all other methods (85% for sonography,75% for CT, and 86% for PET/CT), the NPV was comparable in all methods(98-99%). The overall accuracy of cervical lymph node metastases detection was comparable for sonography, CT, and PET/CT with 96%,97%,94%, respectively, while MRI had only 72% accuracy. Conclusion: Since the initial status of metastasis is an independent predictive risk factor for patients’ survival, efficient detection is crucial to plan adequate therapeutic approaches. Sonography, CT, and PET/CT have better diagnostic accuracy than MRI for the evaluation of cervical lymph node metastases in HNSCC patients.

Keywords: cervical lymph node metastases, diagnostic accuracy, head and neck squamous carcinoma, risk factors, survival

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Authors: Dr. Rebecca Kimelman

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Teaching students to self-advocate has become a central topic in special education literature and practice. However, many special education programs do not address this important skill area. To this end, I created and implemented the Self Advocacy Made Easy (S.A.M.E.) program of instruction, intended to enhance the self-advocacy skills of young adults with mild to moderate disabilities. The effectiveness of S.A.M.E., the degree to which self-advocacy skills were acquired and demonstrated by the students, the level of parental support, and the impact of culture on the process, and teachers’ beliefs and attitudes about the role of self-advocacy skills for their students were measured using action research that employed mixed methodology. Conducted at an overseas American International School, this action research study sought answers to these questions by providing an in-depth portrayal of the S.A.M.E. program, as well as the attitudes and perceptions of the stakeholders involved in the study (thirteen students, their parents, teachers and counsellors). The findings of this study were very positive. The S.A.M.E. program was found to be a valid and valuable instructional tool for teaching self-advocacy skills to students with learning disabilities and ADHD. The study showed participation in the S.A.M.E. program led to an increased understanding of the important elements of self-advocacy, an increase in students’ skills and abilities to self-advocate, and a positive increase in students’ feelings about themselves. Inclusion in the Student-Led IEP meetings, an authentic student assessment within the S.A.M.E. program, also yielded encouraging results, including a higher level of ownership of one’s profile and learning needs, a higher level of student engagement and participation in the IEP meeting, and a growing student awareness of the relevance of the document and the IEP process to their lives. Without exception, every parent believed that participating in the Student-Led IEP led to a growth in confidence in their children, including that it taught them how to ‘own’ their disability and an improvement in their communication skills. Teachers and counsellors that participated in the study felt the program was worthwhile, and led to an increase in the students’ ability to acknowledge their learning profile and to identify and request the accommodations (such as extended time or use of a calculator) they need to overcome or work around their disability. The implications for further research are many, and include an examination of the degree to which participation in S.A.M.E. fosters student achievement, the long-term effects of participation in the program, and the degree to which student participation in the Student-Led IEP meeting increases parents’ level of understanding and involvement.

Keywords: self-advocacy, learning disabilities, ADHD, student-led IEP process

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Authors: Amah Nnachi Lofty, Oyefeso Olufemi, Ibiam Udu Ama

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New and more powerful communications technologies continue to emerge at a rapid pace and their uses in education are widespread and the impact remarkable in the developing societies. This study investigates Mobile Collaboration Learning Technique (MCLT) on learners’ outcome among students in tertiary institutions of developing nations (a case of Nigeria students). It examines the significance of retention achievement scores of students taught using mobile collaboration and conventional method. The sample consisted of 120 students using Stratified random sampling method. Three research questions and hypotheses were formulated, and tested at a 0.05 level of significance. A student achievement test (SAT) was made of 40 items of multiple-choice objective type, developed and validated for data collection by professionals. The SAT was administered to students as pre-test and post-test. The data were analyzed using t-test statistic to test the hypotheses. The result indicated that students taught using MCLT performed significantly better than their counterparts using the conventional method of instruction. Also, there was no significant difference in the post-test performance scores of male and female students taught using MCLT. Based on the findings, the following recommendations was made that: Mobile collaboration system be encouraged in the institutions to boost knowledge sharing among learners, workshop and trainings should be organized to train teachers on the use of this technique and that schools and government should formulate policies and procedures towards responsible use of MCLT.

Keywords: education, communication, learning, mobile collaboration, technology

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Authors: Bhaskar Basu

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Universities and higher education institutes are finding it increasingly difficult to engage students fruitfully through traditional pedagogic tools. Web 2.0 technologies comprising social networking sites (SNSs) offer a platform for students to collaborate and share information, thereby enhancing their learning experience. Despite the potential and reach of SNSs, its use has been limited in academic settings promoting higher education. The purpose of this paper is to assess the perception of social networking sites among business school students in India and analyze its role in enhancing quality of student experiences in a business school leading to the proposal of an agenda for future research. In this study, more than 300 students of a reputed business school were involved in a survey of their preferences of different social networking sites and their perceptions and attitudes towards these sites. A questionnaire with three major sections was designed, validated and distributed among  a sample of students, the research method being descriptive in nature. Crucial questions were addressed to the students concerning time commitment, reasons for usage, nature of interaction on these sites, and the propensity to share information leading to direct and indirect modes of learning. It was further supplemented with focus group discussion to analyze the findings. The paper notes the resistance in the adoption of new technology by a section of business school faculty, who are staunch supporters of the classical “face-to-face” instruction. In conclusion, social networking sites like Facebook and LinkedIn provide new avenues for students to express themselves and to interact with one another. Universities could take advantage of the new ways  in which students are communicating with one another. Although interactive educational options such as Moodle exist, social networking sites are rarely used for academic purposes. Using this medium opens new ways of academically-oriented interactions where faculty could discover more about students' interests, and students, in turn, might express and develop more intellectual facets of their lives. hitherto unknown intellectual facets.  This study also throws up the enormous potential of mobile phones as a tool for “blended learning” in business schools going forward.

Keywords: business school, India, learning, social media, social networking, university

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Authors: Krisztina Bohacs, Klaudia Markus

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To author’s best knowledge we are in absence of studies on cognitive program evaluation and we are certainly short of programs that prove to have high effect sizes with strong retention results. The purpose of our study was to investigate the effectiveness of a comprehensive cognitive training program, namely BrainRx. This cognitive rehabilitation program target and remediate seven core cognitive skills and related systems of sub-skills through repeated engagement in game-like mental procedures delivered one-on-one by a clinician, supplemented by digital training. A larger sample of children with learning disability were given pretest and post-test cognitive assessments. The experimental group completed a twenty-week cognitive training program in a BrainRx center. A matched control group received another twenty-week intervention with Feuerstein’s Instrumental Enrichment programs. A second matched control group did not receive training. As for pre- and post-test, we used a general intelligence test to assess IQ and a computer-based test battery for assessing cognition across the lifespan. Multiple regression analyses indicated that the experimental BrainRx treatment group had statistically significant higher outcomes in attention, working memory, processing speed, logic and reasoning, auditory processing, visual processing and long-term memory compared to the non-treatment control group with very large effect sizes. With the exception of logic and reasoning, the BrainRx treatment group realized significantly greater gains in six of the above given seven cognitive measures compared to the Feuerstein control group. Our one-year retention measures showed that all the cognitive training gains were above ninety percent with the greatest retention skills in visual processing, auditory processing, logic, and reasoning. The BrainRx program may be an effective tool to establish long-term cognitive changes in case of students with learning disabilities. Recommendations are made for treatment centers and special education institutions on the cognitive training of students with special needs. The importance of our study is that targeted, systematic, progressively loaded and intensive brain training approach may significantly change learning disabilities.

Keywords: cognitive rehabilitation training, cognitive skills, learning disability, permanent structural cognitive changes

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Authors: C. B. Le, V. N. Pham

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In modern data analysis, multi-source data appears more and more in real applications. Multi-source data clustering has emerged as a important issue in the data mining and machine learning community. Different data sources provide information about different data. Therefore, multi-source data linking is essential to improve clustering performance. However, in practice multi-source data is often heterogeneous, uncertain, and large. This issue is considered a major challenge from multi-source data. Ensemble is a versatile machine learning model in which learning techniques can work in parallel, with big data. Clustering ensemble has been shown to outperform any standard clustering algorithm in terms of accuracy and robustness. However, most of the traditional clustering ensemble approaches are based on single-objective function and single-source data. This paper proposes a new clustering ensemble method for multi-source data analysis. The fuzzy optimized multi-objective clustering ensemble method is called FOMOCE. Firstly, a clustering ensemble mathematical model based on the structure of multi-objective clustering function, multi-source data, and dark knowledge is introduced. Then, rules for extracting dark knowledge from the input data, clustering algorithms, and base clusterings are designed and applied. Finally, a clustering ensemble algorithm is proposed for multi-source data analysis. The experiments were performed on the standard sample data set. The experimental results demonstrate the superior performance of the FOMOCE method compared to the existing clustering ensemble methods and multi-source clustering methods.

Keywords: clustering ensemble, multi-source, multi-objective, fuzzy clustering

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Authors: Helen Zhang

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Accurate segmentation of the airways from CT scans is crucial for early diagnosis of lung cancer. However, the existing airway segmentation algorithms often rely on thin-slice CT scans, which can be inconvenient and costly. This paper presents a set of machine learning-based 3D super-resolution algorithms along heterogeneous dimensions to improve the resolution of thicker CT scans to reduce the reliance on thin-slice scans. To evaluate the efficacy of the super-resolution algorithms, quantitative assessments using PSNR (Peak Signal to Noise Ratio) and SSIM (Structural SIMilarity index) were performed. The impact of super-resolution on airway segmentation accuracy is also studied. The proposed approach has the potential to make airway segmentation more accessible and affordable, thereby facilitating early diagnosis and treatment of lung cancer.

Keywords: 3D super-resolution, airway segmentation, thin-slice CT scans, machine learning

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Authors: RAM PAL SINGH, VIKASH CHAUDHARY, MONIKA VERMA

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In this paper, we proposed the implementation of optimization based Extreme Learning Machine (ELM) for watermarking of B-channel of color image in discrete wavelet transform (DWT) domain. ELM, a regularization algorithm, works based on generalized single-hidden-layer feed-forward neural networks (SLFNs). However, hidden layer parameters, generally called feature mapping in context of ELM need not to be tuned every time. This paper shows the embedding and extraction processes of watermark with the help of ELM and results are compared with already used machine learning models for watermarking.Here, a cover image is divide into suitable numbers of non-overlapping blocks of required size and DWT is applied to each block to be transformed in low frequency sub-band domain. Basically, ELM gives a unified leaning platform with a feature mapping, that is, mapping between hidden layer and output layer of SLFNs, is tried for watermark embedding and extraction purpose in a cover image. Although ELM has widespread application right from binary classification, multiclass classification to regression and function estimation etc. Unlike SVM based algorithm which achieve suboptimal solution with high computational complexity, ELM can provide better generalization performance results with very small complexity. Efficacy of optimization method based ELM algorithm is measured by using quantitative and qualitative parameters on a watermarked image even though image is subjected to different types of geometrical and conventional attacks.

Keywords: BER, DWT, extreme leaning machine (ELM), PSNR

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Authors: Wagneur Edouard

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Tropical algebra is the algebra constructed over an idempotent semifield S. We show here that every m-dimensional tropical module M over S with strongly independent basis can be embedded into Sm, and provide an algebraic invariant -the Γ-matrix of M- which characterises the isomorphy class of M. The strong independence condition also yields a significant improvement to the Whitney embedding for tropical torsion modules published earlier We also show that the strong independence of the basis of M is equivalent to the unique representation of elements of M. Numerous examples illustrate our results.

Keywords: classification, idempotent semi-modules, strong independence, tropical algebra

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Authors: Chhavi Rana, Sanjay K Jain

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All sides of every education debate agree that quality learning happens when knowledgeable, caring teachers use sound pedagogy. Many deliberations of pedagogy make the fault of considering it as principally being about teaching. There has been lot of research about how to build a positive climate for learning, improve student curiosity, and enhance classroom association. However, these things can only be facilitated when teachers are equipped with better teaching techniques that use sound and accurate pedagogy. Pedagogy is the science and art of education. Its aims range from the full development of the human being to skills acquisition. In India, a project named Mission 10 x has been started by an esteemed IT Corporation Wipro as a faculty development programme (FDP) that particularly focus on elements that facilitated teachers in developing curriculum and new pedagogies that can lead to improvement in student engagement. This paper presents a study of these FDPs and examines (1) the parameters that help teachers in building new pedagogies (2) the extent to which appropriate usage of pedagogy is improved after the conduct of Mission 10 x FDPs, and (3) whether institutions differ in terms of their ability to convert usage of improved pedagogy into academic performance via these FDPs. The sample consisted of 2,236 students at 6 four-year engineering colleges and universities that completed several FDPs during 2012-2014. Many measures of usage of better pedagogy were linked positively with such FDPs, although some of the relationships were weak in strength. The results suggest that the usage of pedagogy were more benefited after conducting these FDPs and application of novel approaches in conducting classes.

Keywords: student engagement, critical thinking; achievement, student learning, pedagogy

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Authors: Brandon Foggo, Nanpeng Yu

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Power distribution circuits undergo frequent network topology changes that are often left undocumented. As a result, the documentation of a circuit’s connectivity becomes inaccurate with time. The lack of reliable circuit connectivity information is one of the biggest obstacles to model, monitor, and control modern distribution systems. To enhance the reliability and efficiency of electric power distribution systems, the circuit’s connectivity information must be updated periodically. This paper focuses on one critical component of a distribution circuit’s topology - the secondary transformer to phase association. This topology component describes the set of phase lines that feed power to a given secondary transformer (and therefore a given group of power consumers). Finding the documentation of this component is call Phase Identification, and is typically performed with physical measurements. These measurements can take time lengths on the order of several months, but with supervised learning, the time length can be reduced significantly. This paper compares several such methods applied to Phase Identification for a large range of real distribution circuits, describes a method of training data selection, describes preprocessing steps unique to the Phase Identification problem, and ultimately describes a method which obtains high accuracy (> 96% in most cases, > 92% in the worst case) using only 5% of the measurements typically used for Phase Identification.

Keywords: distribution network, machine learning, network topology, phase identification, smart grid

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Authors: Ali Almadan, Anoop Krishnan, Ajita Rattani

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Facial recognition is a widely adopted technology in surveillance, border control, healthcare, banking services, and lately, in mobile user authentication with Apple introducing “Face ID” moniker with iPhone X. A lot of research has been conducted in the area of face recognition on datasets captured by surveillance cameras, DSLR, and mobile devices. Recently, face recognition technology has also been deployed on body-worn cameras to keep officers safe, enabling situational awareness and providing evidence for trial. However, limited academic research has been conducted on this topic so far, without the availability of any publicly available datasets with a sufficient sample size. This paper aims to advance research in the area of face recognition using body-worn cameras. To this aim, the contribution of this work is two-fold: (1) collection of a dataset consisting of a total of 136,939 facial images of 102 subjects captured using body-worn cameras in in-door and daylight conditions and (2) evaluation of various deep-learning architectures for face identification on the collected dataset. Experimental results suggest a maximum True Positive Rate(TPR) of 99.86% at False Positive Rate(FPR) of 0.000 obtained by SphereFace based deep learning architecture in daylight condition. The collected dataset and the baseline algorithms will promote further research and development. A downloadable link of the dataset and the algorithms is available by contacting the authors.

Keywords: face recognition, body-worn cameras, deep learning, person identification

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Authors: Theresia El Khoury, Georges Badr, Amir Hajjam El Hassani, Stéphane N’Guyen Van Ky

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The widespread adoption of machine learning and artificial intelligence across different domains can be attributed to the digitization of data over several decades, resulting in vast amounts of data, types, and structures. Thus, data processing and preparation turn out to be a crucial stage. However, applying these techniques to S1000D standard-based data poses a challenge due to its complexity and the need to preserve logical information. This paper describes SALERNO, an S1000d AppLicability dEpended pRocessiNg mOdel. This python-based model analyzes and converts the XML S1000D-based files into an easier data format that can be used in machine learning techniques while preserving the different logic and relationships in files. The model parses the files in the given folder, filters them, and extracts the required information to be saved in appropriate data frames and Excel sheets. Its main idea is to group the extracted information by applicability. In addition, it extracts the full text by replacing internal and external references while maintaining the relationships between files, as well as the necessary requirements. The resulting files can then be saved in databases and used in different models. Documents in both English and French languages were tested, and special characters were decoded. Updates on the technical manuals were taken into consideration as well. The model was tested on different versions of the S1000D, and the results demonstrated its ability to effectively handle the applicability, requirements, references, and relationships across all files and on different levels.

Keywords: aeronautics, big data, data processing, machine learning, S1000D

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Authors: M. M. Essa, S. Subash, M. Akbar, S. Al-Adawi, A. Al-Asmi, G. J. Guillemein

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Objective: Transgenic (tg) mice which contain an amyloid precursor protein (APP) gene mutation, develop extracellular amyloid beta (Aβ) deposition in the brain, and severe memory and behavioural deficits with age. These mice serve as an important animal model for testing the efficacy of novel drug candidates for the treatment and management of symptoms of Alzheimer's disease (AD). Several reports have suggested that oxidative stress is the underlying cause of Aβ neurotoxicity in AD. Pomegranates contain very high levels of antioxidants and several medicinal properties that may be useful for improving the quality of life in AD patients. In this study, we investigated the effect of dietary supplementation of Omani pomegranate extract on the memory, anxiety and learning skills along with inflammation in an AD mouse model containing the double Swedish APP mutation (APPsw/Tg2576). Methods: The experimental groups of APP-transgenic mice from the age of 4 months were fed custom-mix diets (pellets) containing 4% pomegranate. We assessed spatial memory and learning ability, psychomotor coordination, and anxiety-related behavior in Tg and wild-type mice at the age of 4-5 months and 18-19 months using the Morris water maze test, rota rod test, elevated plus maze test, and open field test. Further, inflammatory parameters also analysed. Results: APPsw/Tg2576 mice that were fed a standard chow diet without pomegranates showed significant memory deficits, increased anxiety-related behavior, and severe impairment in spatial learning ability, position discrimination learning ability and motor coordination along with increased inflammation compared to the wild type mice on the same diet, at the age of 18-19 months In contrast, APPsw/Tg2576 mice that were fed a diet containing 4% pomegranates showed a significant improvements in memory, learning, locomotor function, and anxiety with reduced inflammatory markers compared to APPsw/Tg2576 mice fed the standard chow diet. Conclusion: Our results suggest that dietary supplementation with pomegranates may slow the progression of cognitive and behavioural impairments in AD. The exact mechanism is still unclear and further extensive research needed.

Keywords: Alzheimer's disease, pomegranates, oman, cognitive decline, memory loss, anxiety, inflammation

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Authors: Sıdıka Kaya, Gülay Sain Güven, Seda Karsavuran, Onur Toka

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This study is part of an ongoing research project supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under Project Number 114K404, and participation to this conference was supported by Hacettepe University Scientific Research Coordination Unit under Project Number 10243. Evaluation of hospital readmissions is gaining importance in terms of quality and cost, and is becoming the target of national policies. In Turkey, the topic of hospital readmission is relatively new on agenda and very few studies have been conducted on this topic. The aim of this study was to determine 30-day readmission rates and risk factors for readmission. Whether readmission was planned, related to the prior admission and avoidable or not was also assessed. The study was designed as a ‘prospective cohort study.’ 472 patients hospitalized in internal medicine departments of a university hospital in Turkey between February 1, 2015 and April 30, 2015 were followed up. Analyses were conducted using IBM SPSS Statistics version 22.0 and SPSS Modeler 16.0. Average age of the patients was 56 and 56% of the patients were female. Among these patients 95 were readmitted. Overall readmission rate was calculated as 20% (95/472). However, only 31 readmissions were unplanned. Unplanned readmission rate was 6.5% (31/472). Out of 31 unplanned readmission, 24 was related to the prior admission. Only 6 related readmission was avoidable. To determine risk factors for readmission we constructed Chi-square automatic interaction detector (CHAID) decision tree algorithm. CHAID decision trees are nonparametric procedures that make no assumptions of the underlying data. This algorithm determines how independent variables best combine to predict a binary outcome based on ‘if-then’ logic by portioning each independent variable into mutually exclusive subsets based on homogeneity of the data. Independent variables we included in the analysis were: clinic of the department, occupied beds/total number of beds in the clinic at the time of discharge, age, gender, marital status, educational level, distance to residence (km), number of people living with the patient, any person to help his/her care at home after discharge (yes/no), regular source (physician) of care (yes/no), day of discharge, length of stay, ICU utilization (yes/no), total comorbidity score, means for each 3 dimensions of Readiness for Hospital Discharge Scale (patient’s personal status, patient’s knowledge, and patient’s coping ability) and number of daycare admissions within 30 days of discharge. In the analysis, we included all 95 readmitted patients (46.12%), but only 111 (53.88%) non-readmitted patients, although we had 377 non-readmitted patients, to balance data. The risk factors for readmission were found as total comorbidity score, gender, patient’s coping ability, and patient’s knowledge. The strongest identifying factor for readmission was comorbidity score. If patients’ comorbidity score was higher than 1, the risk for readmission increased. The results of this study needs to be validated by other data–sets with more patients. However, we believe that this study will guide further studies of readmission and CHAID is a useful tool for identifying risk factors for readmission.

Keywords: decision tree, hospital, internal medicine, readmission

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Authors: Serena Salvi

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Ageistic stereotypes and practices have become a normal and therefore pervasive phenomenon in various aspects of everyday life. Over the past years, renewed awareness towards self-directed age stereotyping in older adults has given rise to a line of research focused on the potential role of attitudes towards ageing on seniors’ health and functioning. This set of studies has showed how a negative internalisation of ageistic stereotypes would discourage older adults in seeking medical advice, in addition to be associated to negative subjective health evaluation. An important dimension of mental health that is often affected in older adults is represented by sleep quality. Self-reported sleep quality among older adults has shown to be often unreliable when compared to their objective sleep measures. Investigations focused on self-reported sleep quality among older adults have suggested how this portion of the population would tend to accept disrupted sleep if believed to be up to standard for their age. On the other hand, unrealistic expectations, and dysfunctional beliefs towards sleep in ageing, might prompt older adults to report sleep disruption even in the absence of objective disrupted sleep. Objective of this study is to examine an association between personal attitudes towards ageing in adults aged 60+ and dysfunctional sleep related cognition. More in detail, this study aims to investigate a potential association between personal attitudes towards ageing, sleep locus of control and dysfunctional beliefs towards sleep among this portion of the population. Data in this study were statistically analysed in SPSS software. Participants were recruited through the online participants recruitment system Prolific. Inclusion of attention check questions throughout the questionnaire and consistency of responses were looked at. Prior to the commencement of this study, Ethical Approval was granted (ref. 39396). Descriptive statistics were used to determine the frequency, mean, and SDs of the variables. Pearson coefficient was used for interval variables, independent T-test for comparing means between two independent groups, analysis of variance (ANOVA) test for comparing the means in several independent groups, and hierarchical linear regression models for predicting criterion variables based on predictor variables. In this study self-perceptions of ageing were assessed using APQ-B’s subscales, while dysfunctional sleep related cognition was operationalised using the SLOC and the DBAS16 scales. Of the final subscales taken in consideration in the brief version of the APQ questionnaire, Emotional Representations (ER), Control Positive (PC) and Control and Consequences Negative (NC) have shown to be of particularly relevance for the remits of this study. Regression analysis show how an increase in the APQ-B subscale Emotional Representations (ER) predicts an increase in dysfunctional beliefs and attitudes towards sleep in this sample, after controlling for subjective sleep quality, level of depression and chronological age. A second regression analysis showed that APQ-B subscales Control Positive (PC) and Control and Consequences Negative (NC) were significant predictors in the change of variance of SLOC, after controlling for subjective sleep quality, level of depression and dysfunctional beliefs about sleep.

Keywords: sleep-related cognition, perceptions of aging, older adults, sleep quality

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Authors: Jamison Duckworth, Shankarachary Ragi

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Detection and segmentation of wildfire smoke plumes from remote sensing imagery are being pursued as a solution for early fire detection and response. Smoke plume detection can be automated and made robust by the application of artificial intelligence methods. Specifically, in this study, the deep learning approach Mask Region-based Convolutional Neural Network (RCNN) is being proposed to learn smoke patterns across different spectral bands. This method is proposed to separate the smoke regions from the background and return masks placed over the smoke plumes. Multispectral data was acquired using NASA’s Earthdata and WorldView and services and satellite imagery. Due to the use of multispectral bands along with the three visual bands, we show that Mask R-CNN can be applied to distinguish smoke plumes from clouds and other landscape features that resemble smoke.

Keywords: deep learning, mask-RCNN, smoke plumes, spectral bands

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Authors: Sampo Tukiainen, Jukka Mattila, Niina Erkama, Erkki Ormala

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During the past decade, calls for more holistic and integrative organizational innovation research have been increasingly voiced. On the one hand, from the theoretical perspective, the reason for this has been the tendency in contemporary innovation studies to focus on disciplinary subfields, often leading to challenges in integrating theories in meaningful ways. For example, we find that during the past three decades the innovation research has evolved into an academic field consisting of several independent research streams, such as studies on organizational learning, project management, and top management teams, to name but a few. The innovation research has also proliferated according to different dimensions of innovation, such as sources, drivers, forms, and the nature of innovation. On the other hand, from the practical perspective the rationale has been the need to develop understanding of the solving of complex, interdisciplinary issues and problems in contemporary and future societies and organizations. Therefore, for advancing theorizing, as well as the practical applicability of organizational innovation research, we acknowledge the need for more integrative and holistic perspectives and approaches. We contribute to addressing this challenge by developing a ‘box transcendent’ perspective to examine interlinkages in and across four key dimensions of organizational innovation processes, which traditionally have been studied in separate research streams. Building on an in-depth, qualitative analysis of 123 interviews of CTOs (or equivalent) and CEOs in top innovative Finnish companies as well as three in-depth case studies, both as part of an EU-level interview study of more than 700 companies, we specify interlinkages in and between i) strategic management, ii) innovation management, iii) implementation and organization, and iv) commercialization, in innovation processes. We contribute to the existing innovation research in multiple ways. Firstly, we develop a cross-dimensional, ‘box transcendent’ conceptual model at the level of organizational innovation process. Secondly, this modeling enables us to extend existing theorizing by allowing us to distinguish specific cross-dimensional innovation ‘profiles’ in two different company categories: large multinational corporations and SMEs. Finally, from the more practical perspective, we consider the implications of such innovation ‘profiles’ for the societal and institutional, policy-making development.

Keywords: holistic research, innovation management, innovation studies, organizational innovation

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Authors: Yanjie Zhu, André Jesus, Irwanda Laory

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Structural Health Monitoring (SHM), involving data acquisition, data interpretation and decision-making system aim to continuously monitor the structural performance of civil infrastructures under various in-service circumstances. The main value and purpose of SHM is identifying damages through data interpretation system. Research on SHM has been expanded in the last decades and a large volume of data is recorded every day owing to the dramatic development in sensor techniques and certain progress in signal processing techniques. However, efficient and reliable data interpretation for damage detection under environmental variations is still a big challenge. Structural damages might be masked because variations in measured data can be the result of environmental variations. This research reports a novel method based on single-channel Blind Signal Separation (BSS), which extracts environmental effects from measured data directly without any prior knowledge of the structure loading and environmental conditions. Despite the successful application in audio processing and bio-medical research fields, BSS has never been used to detect damage under varying environmental conditions. This proposed method optimizes and combines Ensemble Empirical Mode Decomposition (EEMD), Principal Component Analysis (PCA) and Independent Component Analysis (ICA) together to separate structural responses due to different loading conditions respectively from a single channel input signal. The ICA is applying on dimension-reduced output of EEMD. Numerical simulation of a truss bridge, inspired from New Joban Line Arakawa Railway Bridge, is used to validate this method. All results demonstrate that the single-channel BSS-based method can recover temperature effects from mixed structural response recorded by a single sensor with a convincing accuracy. This will be the foundation of further research on direct damage detection under varying environment.

Keywords: damage detection, ensemble empirical mode decomposition (EEMD), environmental variations, independent component analysis (ICA), principal component analysis (PCA), structural health monitoring (SHM)

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Authors: A. Bouabid, B. Bielenberg, S. Ainane, N. Pasha

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In this paper, a team of faculty members of the Petroleum Institute in Abu Dhabi, UAE representing six different courses across General Engineering (ENGR), Communication (COMM), and Design (STPS) worked together to establish a clear developmental progression of learning outcomes and performance indicators for targeted knowledge, areas of competency, and skills for the first three semesters of the Bachelor of Sciences in Engineering curriculum. The sequences of courses studied in this project were ENGR/COMM, COMM/STPS, and ENGR/STPS. For each course’s nine areas of knowledge, competency, and skills, the research team reviewed the existing learning outcomes and related performance indicators with a focus on identifying linkages across disciplines as well as within the courses of a discipline. The team reviewed existing performance indicators for developmental progression from semester to semester for same discipline related courses (vertical alignment) and for different discipline courses within the same semester (horizontal alignment). The results of this work have led to recommendations for modifications of the initial indicators when incoherence was identified, and/or for new indicators based on best practices (identified through literature searches) when gaps were identified. It also led to recommendations for modifications of the level of emphasis within each course to ensure developmental progression. The exercise has led to a revised Sequence Performance Indicator Mapping for the knowledge, skills, and competencies across the six core courses.

Keywords: curriculum alignment, horizontal and vertical progression, performance indicators, skill level

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Authors: Bander Mohayya Alotaibi

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The valuable partnerships between parents and teachers may develop positive and effective interactions between home and school. This will help these stakeholders share information and resources regarding student academics during ongoing interactions. Thus, partnerships will build a solid foundation for both families and schools to help children succeed in school. Parental involvement can be seen as an effective tool that can change homes and communities and not just schools’ systems. Seeking parents and stakeholders’ attitudes toward learning and learners can help schools design a curriculum. Subsequently, this information can be used to find ways to help improve the academic performance of students, especially in low performing schools. There may be some conflicts when designing curriculum. In addition, designing curriculum might bring more educational expectations to all the sides. There is a lack of research that targets the specific attitude of parents toward specific concepts on curriculum contents. More research is needed to study the perspective that parents of children with learning disabilities (LD) have regarding early reading curriculum. Parents and stakeholders’ perspectives on early reading intervention implemented as a curriculum for children with LD was studied through an advanced quantitative research. The purpose of this study seeks to understand stakeholders and parents’ perspectives of key concepts and essential early reading skills that impact the design of curriculum that will serve as an intervention for early struggler readers who have LD. Those concepts or stages include phonics, phonological awareness, and reading fluency as well as strategies used in house by parents. A survey instrument was used to gather the data. Participants were recruited through 29 schools and districts of the metropolitan area of the northern part of Saudi Arabia. Participants were stakeholders including parents of children with learning disability. Data were collected using distribution of paper and pen survey to schools. Psychometric properties of the instrument were evaluated for the validity and reliability of the survey; face validity, content validity, and construct validity including an Exploratory Factor Analysis were used to shape and reevaluate the structure of the instrument. Multivariate analysis of variance (MANOVA) used to find differences between the variables. The study reported the results of the perspectives of stakeholders toward reading strategies, phonics, phonological awareness, and reading fluency. Also, suggestions and limitations are discussed.

Keywords: stakeholders, learning disability, early reading, perspectives, parents, intervention, curriculum

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Authors: Mais Nijim, Rama Devi Chennuboyina, Waseem Al Aqqad

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Advances in spatial and spectral resolution of satellite images have led to tremendous growth in large image databases. The data we acquire through satellites, radars and sensors consists of important geographical information that can be used for remote sensing applications such as region planning, disaster management. Spatial data classification and object recognition are important tasks for many applications. However, classifying objects and identifying them manually from images is a difficult task. Object recognition is often considered as a classification problem, this task can be performed using machine-learning techniques. Despite of many machine-learning algorithms, the classification is done using supervised classifiers such as Support Vector Machines (SVM) as the area of interest is known. We proposed a classification method, which considers neighboring pixels in a region for feature extraction and it evaluates classifications precisely according to neighboring classes for semantic interpretation of region of interest (ROI). A dataset has been created for training and testing purpose; we generated the attributes by considering pixel intensity values and mean values of reflectance. We demonstrated the benefits of using knowledge discovery and data-mining techniques, which can be on image data for accurate information extraction and classification from high spatial resolution remote sensing imagery.

Keywords: remote sensing, object recognition, classification, data mining, waterbody identification, feature extraction

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