Search results for: multivariate categorical data

Authors: Eman AbuKhousa, Marwan Z. Bataineh

Abstract:

The 21^st century higher education and globalization challenge new faculty members to build effective professional networks and partnership with industry in order to accelerate their growth and success. This creates the need for community of practice (CoP)-oriented development approaches that focus on cognitive apprenticeship while considering individual predisposition and future career needs. This work adopts data mining, clustering analysis, and social networking technologies to present the CoP-Network as a virtual space that connects together similar career-aspiration individuals who are socially influenced to join and engage in a process for domain-related knowledge and practice acquisitions. The CoP-Network model can be integrated into higher education to extend traditional graduate and professional development programs.

Keywords: Clustering analysis, community of practice, data mining, higher education, new faculty challenges, social networks, social influence, professional development.

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Authors: M. Capocelli, A. Caputo, M. De Falco, D. Mazzei, V. Piemonte

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This paper presents the experimental results and the related modeling of a thermal energy storage (TES) facility, ideated and realized by ENEA and realizing the thermocline with an innovative geometry. Firstly, the thermal energy exchange model of an equivalent shell & tube heat exchanger is described and tested to reproduce the performance of the spiral exchanger installed in the TES. Through the regression of the experimental data, a first-order thermocline model was also validated to provide an analytical function of the thermocline, useful for the performance evaluation and the comparison with other systems and implementation in simulations of integrated systems (e.g. power plants). The experimental data obtained from the plant start-up and the short-cut modeling of the system can be useful for the process analysis, for the scale-up of the thermal storage system and to investigate the feasibility of its implementation in actual case-studies.

Keywords: Thermocline, modelling, heat exchange, spiral, shell, tube.

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Authors: Mohammadreza Razzazi, Maryam Bayat

Abstract:

Selection of the best possible set of suppliers has a significant impact on the overall profitability and success of any business. For this reason, it is usually necessary to optimize all business processes and to make use of cost-effective alternatives for additional savings. This paper proposes a new efficient context-aware supplier selection model that takes into account possible changes of the environment while significantly reducing selection costs. The proposed model is based on data clustering techniques while inspiring certain principles of online algorithms for an optimally selection of suppliers. Unlike common selection models which re-run the selection algorithm from the scratch-line for any decision-making sub-period on the whole environment, our model considers the changes only and superimposes it to the previously defined best set of suppliers to obtain a new best set of suppliers. Therefore, any recomputation of unchanged elements of the environment is avoided and selection costs are consequently reduced significantly. A numerical evaluation confirms applicability of this model and proves that it is a more optimal solution compared with common static selection models in this field.

Keywords: Supplier Selection, Context-Awareness, OnlineAlgorithms, Data Clustering.

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Authors: Taner Ersoz, Filiz Ersoz, Seyma Ozbilge

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In this study, the clients who applied to a bank branch for loan were analyzed through data mining. The study was composed of the information such as amounts of loans received by personal and SME clients working with the bank branch, installment numbers, number of delays in loan installments, payments available in other banks and number of banks to which they are in debt between 2010 and 2013. The client risk profile was examined through Classification and Regression Tree (CART) analysis, one of the decision tree classification methods. At the end of the study, 5 different types of customers have been determined on the decision tree. The classification of these types of customers has been created with the rating of those posing a risk for the bank branch and the customers have been classified according to the risk ratings.

Keywords: Client classification, loan suitability, risk rating, CART analysis, decision tree.

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Authors: Namchul Do, Gyeongseok Chae

Abstract:

This paper proposes a framework for product development including hardware and software components. It provides separation of hardware dependent software, modifications of current product development process, and integration of software modules with existing product configuration models and assembly product structures. In order to decide the dependent software, the framework considers product configuration modules and engineering changes of associated software and hardware components. In order to support efficient integration of the two different hardware and software development, a modified product development process is proposed. The process integrates the dependent software development into product development through the interchanges of specific product information. By using existing product data models in Product Data Management (PDM), the framework represents software as modules for product configurations and software parts for product structure. The framework is applied to development of a robot system in order to show its effectiveness.

Keywords: HW and SW Development Integration, ProductDevelopment with Software.

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Authors: P. Phokharatkul, K. Sankhuangaw, S. Somkuarnpanit, S. Phaiboon, C. Kimpan

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Much research into handwritten Thai character recognition have been proposed, such as comparing heads of characters, Fuzzy logic and structure trees, etc. This paper presents a system of handwritten Thai character recognition, which is based on the Ant-minor algorithm (data mining based on Ant colony optimization). Zoning is initially used to determine each character. Then three distinct features (also called attributes) of each character in each zone are extracted. The attributes are Head zone, End point, and Feature code. All attributes are used for construct the classification rules by an Ant-miner algorithm in order to classify 112 Thai characters. For this experiment, the Ant-miner algorithm is adapted, with a small change to increase the recognition rate. The result of this experiment is a 97% recognition rate of the training set (11200 characters) and 82.7% recognition rate of unseen data test (22400 characters).

Keywords: Hand written, Thai character recognition, Ant-mineralgorithm, distinct feature.

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Authors: Y. Abdelrazig, R. Moses

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Roadway planning and design is a very complex process involving five key phases before a project is completed; planning, project development, final design, right-of-way, and construction. The planning phase for a new roadway transportation project is a very critical phase as it greatly affects all latter phases of the project. A location study is usually performed during the preliminary planning phase in a new roadway project. The objective of the location study is to develop alignment alternatives that are cost efficient considering land acquisition and construction costs. This paper describes a methodology to develop optimal preliminary roadway alignments utilizing spatial-data. Four optimization criteria are taken into consideration; roadway length, land cost, land slope, and environmental impacts. The basic concept of the methodology is to convert the proposed project area into a grid, which represents the search space for an optimal alignment. The aforementioned optimization criteria are represented in each of the grid’s cells. A spatial-data optimization technique is utilized to find the optimal alignment in the search space based on the four optimization criteria. Two case studies for new roadway projects in Duval County in the State of Florida are presented to illustrate the methodology. The optimization output alignments are compared to the proposed Florida Department of Transportation (FDOT) alignments. The comparison is based on right-of-way costs for the alignments. For both case studies, the right-of-way costs for the developed optimal alignments were found to be significantly lower than the FDOT alignments.

Keywords: Optimization, planning, roadway alignment, FDOT.

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Authors: B. Sridhar, C. Arun

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The rapid growth of multimedia technology demands the secure and efficient access to information. This fast growing lose the confidence of unauthorized duplication. Henceforth the protection of multimedia content is becoming more important. Watermarking solves the issue of unlawful copy of advanced data. In this paper, blind video watermarking technique has been proposed. A luminance layer of selected frames is interlaced into two even and odd rows of an image, further it is deinterlaced and equalizes the coefficients of the two shares. Color watermark is split into different blocks, and the pieces of block are concealed in one of the share under the wavelet transform. Stack the two images into a single image by introducing interlaced even and odd rows in the two shares. Finally, chrominance bands are concatenated with the watermarked luminance band. The safeguard level of the secret information is high, and it is undetectable. Results show that the quality of the video is not changed also yields the better PSNR values.

Keywords: Authentication, data security, deinterlaced, wavelet transform, watermarking.

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Authors: Alon Friedman, Antonio Sánchez Chinchón

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This bibliometric study takes an artistic and storytelling approach to explore the term ”Information visualization.” Analyzing over 1008 titles collected from databases that specialize in data visualization research, we examine the titles of these publications to report on the characteristics and development trends in the field. Employing a qualitative methodology, we delve into the titles of these publications, extracting leading terms and exploring the co-occurrence of these terms to gain deeper insights. By systematically analyzing the leading terms and their relationships within the titles, we shed light on the prevailing themes that shape the landscape of ”Information visualization” by employing the artist Mark Lombardi’s techniques to visualize our findings. By doing so, this study provides valuable insights into bibliometrics visualization while also opening new avenues for leveraging art and storytelling to enhance data representation.

Keywords: Bibliometrics analysis, Mark Lombardi design, information visualization, qualitative methodology.

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Authors: Stefanie Rukavina, Sascha Gruss, Steffen Walter, Holger Hoffmann, Harald C. Traue

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OPEN_EmoRec_II is an open multimodal corpus with experimentally induced emotions. In the first half of the experiment, emotions were induced with standardized picture material and in the second half during a human-computer interaction (HCI), realized with a wizard-of-oz design. The induced emotions are based on the dimensional theory of emotions (valence, arousal and dominance). These emotional sequences - recorded with multimodal data (facial reactions, speech, audio and physiological reactions) during a naturalistic-like HCI-environment one can improve classification methods on a multimodal level. This database is the result of an HCI-experiment, for which 30 subjects in total agreed to a publication of their data including the video material for research purposes*. The now available open corpus contains sensory signal of: video, audio, physiology (SCL, respiration, BVP, EMG Corrugator supercilii, EMG Zygomaticus Major) and facial reactions annotations.

Keywords: Open multimodal emotion corpus, annotated labels.

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Authors: Stefanie Rukavina, Sascha Gruss, Steffen Walter, Holger Hoffmann, Harald C. Traue

Abstract:

OPEN_EmoRec_II is an open multimodal corpus with experimentally induced emotions. In the first half of the experiment, emotions were induced with standardized picture material and in the second half during a human-computer interaction (HCI), realized with a wizard-of-oz design. The induced emotions are based on the dimensional theory of emotions (valence, arousal and dominance). These emotional sequences - recorded with multimodal data (facial reactions, speech, audio and physiological reactions) during a naturalistic-like HCI-environment one can improve classification methods on a multimodal level. This database is the result of an HCI-experiment, for which 30 subjects in total agreed to a publication of their data including the video material for research purposes*. The now available open corpus contains sensory signal of: video, audio, physiology (SCL, respiration, BVP, EMG Corrugator supercilii, EMG Zygomaticus Major) and facial reactions annotations.

Keywords: Open multimodal emotion corpus, annotated labels.

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Authors: D. Hişam, S. İkizoğlu

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Identifying the problem behind balance disorder is one of the most interesting topics in medical literature. This study has considerably enhanced the development of artificial intelligence (AI) algorithms applying multiple machine learning (ML) models to sensory data on gait collected from humans to classify between normal people and those suffering from Vestibular System (VS) problems. Although AI is widely utilized as a diagnostic tool in medicine, AI models have not been used to perform feature extraction and identify VS disorders through training on raw data. In this study, three ML models, the Random Forest Classifier (RF), Extreme Gradient Boosting (XGB), and K-Nearest Neighbor (KNN), have been trained to detect VS disorder, and the performance comparison of the algorithms has been made using accuracy, recall, precision, and f1-score. With an accuracy of 95.28 %, Random Forest (RF) Classifier was the most accurate model.

Keywords: Vestibular disorder, machine learning, random forest classifier, k-nearest neighbor, extreme gradient boosting.

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Authors: Nisar Ahmed Memon

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Embedding and extraction of a secret information as well as the restoration of the original un-watermarked image is highly desirable in sensitive applications like military, medical, and law enforcement imaging. This paper presents a novel reversible data-hiding method for digital images using integer to integer wavelet transform and companding technique which can embed and recover the secret information as well as can restore the image to its pristine state. The novel method takes advantage of block based watermarking and iterative optimization of threshold for companding which avoids histogram pre and post-processing. Consequently, it reduces the associated overhead usually required in most of the reversible watermarking techniques. As a result, it keeps the distortion small between the marked and the original images. Experimental results show that the proposed method outperforms the existing reversible data hiding schemes reported in the literature.

Keywords: Adaptive Thresholding, Companding Technique, Integer Wavelet Transform, Reversible Watermarking

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Authors: Okechukwu Odinaka Ajaegbu

Abstract:

In the recent past, discussing about sex among children and youths was frowned at by traditional norms and as such sexual discussions and behavior were approached with great respect. Things are actually falling apart with the increasing number of young people that engage in premarital sex. Due to lack of experience and sex education, many young people are becoming increasingly exposed to the risk of HIV infection. In the light of the above, this study discussed premarital sex, HIV, and use of condom among youths in Nigeria. Data for this study came from 2013 Nigeria Demographic and Health Survey and other secondary data. The survey revealed that only 18.5 percent of young women that had sex in the 12 months preceding the survey used condom. Out of 3306 never-married sexually active men and women, 1728 representing 52 percent live in urban areas and 43 percent of them did not use condom during sexual intercourse in the 12 months preceding the survey. This study concludes that for there to be reduction in prevalence of HIV/AIDS among Nigerian youths, there is need for concerted effort to be made towards educating youths on the expedient of the use of condom during sexual intercourse.

Keywords: Condom, HIV, Nigeria, Premarital sex, Youths.

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Authors: Sannikumar Patel, Brian Nolan, Markus Hofmann, Philip Owende, Kunjan Patel

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Sentiment analysis and opinion mining have become emerging topics of research in recent years but most of the work is focused on data in the English language. A comprehensive research and analysis are essential which considers multiple languages, machine translation techniques, and different classifiers. This paper presents, a comparative analysis of different approaches for multilingual sentiment analysis. These approaches are divided into two parts: one using classification of text without language translation and second using the translation of testing data to a target language, such as English, before classification. The presented research and results are useful for understanding whether machine translation should be used for multilingual sentiment analysis or building language specific sentiment classification systems is a better approach. The effects of language translation techniques, features, and accuracy of various classifiers for multilingual sentiment analysis is also discussed in this study.

Keywords: Cross-language analysis, machine learning, machine translation, sentiment analysis.

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Authors: Belkacem Laimouche

Abstract:

With the field of Artificial Intelligence (AI) experiencing exponential growth, fueled by technological advancements that pave the way for increasingly innovative and promising applications, there is an escalating need to develop rigorous methods for assessing their performance in pursuit of transparency and equity. This article proposes a metrology-inspired statistical framework for evaluating bias and explainability in AI systems. Drawing from the principles of metrology, we propose a pioneering approach, using a concrete example, to evaluate the accuracy and precision of AI models, as well as to quantify the sources of measurement uncertainty that can lead to bias in their predictions. Furthermore, we explore a statistical approach for evaluating the explainability of AI systems based on their ability to provide interpretable and transparent explanations of their predictions.

Keywords: Artificial intelligence, metrology, measurement uncertainty, prediction error, bias, machine learning algorithms, probabilistic models, inter-laboratory comparison, data analysis, data reliability, bias impact assessment, bias measurement.

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Authors: Sorayya Malek, Mogeeb Mosleh, Sharifah M. Syed

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In this study, Support Vector Machine (SVM) technique was applied to predict the dichotomized value of Dissolved oxygen (DO) from two freshwater lakes namely Chini and Bera Lake (Malaysia). Data sample contained 11 parameters for water quality features from year 2005 until 2009. All data parameters were used to predicate the dissolved oxygen concentration which was dichotomized into 3 different levels (High, Medium, and Low). The input parameters were ranked, and forward selection method was applied to determine the optimum parameters that yield the lowest errors, and highest accuracy. Initial results showed that pH, Water Temperature, and Conductivity are the most important parameters that significantly affect the predication of DO. Then, SVM model was applied using the Anova kernel with those parameters yielded 74% accuracy rate. We concluded that using SVM models to predicate the DO is feasible, and using dichotomized value of DO yields higher prediction accuracy than using precise DO value.

Keywords: Dissolved oxygen, Water quality, predication DO, Support Vector Machine.

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Authors: Xiaocong Liu, Huazhen Wang, Ting He, Xiaozheng Li, Weihan Zhang, Jian Chen

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The utilization of electronic medical record (EMR) data to establish the disease diagnosis model has become an important research content of biomedical informatics. Deep learning can automatically extract features from the massive data, which brings about breakthroughs in the study of EMR data. The challenge is that deep learning lacks semantic knowledge, which leads to impracticability in medical science. This research proposes a method of incorporating lexical-semantic knowledge from abundant entities into a convolutional neural network (CNN) framework for pediatric disease diagnosis. Firstly, medical terms are vectorized into Lexical Semantic Vectors (LSV), which are concatenated with the embedded word vectors of word2vec to enrich the feature representation. Secondly, the semantic distribution of medical terms serves as Semantic Decision Guide (SDG) for the optimization of deep learning models. The study evaluates the performance of LSV-SDG-CNN model on four kinds of Chinese EMR datasets. Additionally, CNN, LSV-CNN, and SDG-CNN are designed as baseline models for comparison. The experimental results show that LSV-SDG-CNN model outperforms baseline models on four kinds of Chinese EMR datasets. The best configuration of the model yielded an F1 score of 86.20%. The results clearly demonstrate that CNN has been effectively guided and optimized by lexical-semantic knowledge, and LSV-SDG-CNN model improves the disease classification accuracy with a clear margin.

Keywords: lexical semantics, feature representation, semantic decision, convolutional neural network, electronic medical record

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Authors: Mehmet Durnali

Abstract:

The main purpose of this study is to find out, analyze and discuss basic principles of education and training in the constitutions, including the latest amendment, of France, Indonesia, Japan, South Africa, the United States of America, and Turkey. This research specifically aims at establishing a framework in order to compare educational values such as right of education, responsibilities of states and those of people, and other issues pertaining to education in the Constitution of Turkey to others. Additionally, it emphasizes the meaning of education in constitution, the reasons for references to education in constitutions and why it is important for people, states or nations and state organs. Qualitative analysis technique is performed to accomplish the aim of this study. Maximum variation sampling is used. The main data source of the analysis is official organic laws of those countries. The data is examined by using descriptive and content analysis method.

Keywords: Education in the constitution, education law, legal principles of education, right to education.

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Authors: [email protected]

Abstract:

Breast cancer is one of the most common types in women. Early prediction of breast cancer helps physicians detect cancer in its early stages. Big cancer data need a very powerful tool to analyze and extract predictions. Machine learning and deep learning are two of the most efficient tools for predicting cancer based on textual data. In this study, we developed a fusion model of two machine learning and deep learning models. To obtain the final prediction, Long-Short Term Memory (LSTM), ensemble learning with hyper parameters optimization, and score-level fusion is used. Experiments are done on the Breast Cancer Surveillance Consortium (BCSC) dataset after balancing and grouping the class categories. Five different training scenarios are used, and the tests show that the designed fusion model improved the performance by 3.3% compared to the individual models.

Keywords: Machine learning, Deep learning, cancer prediction, breast cancer, LSTM, Score-Level Fusion.

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Authors: Muhammad Kamil Abdullah, Khazaimatol S Subari, Justin Leo Cheang Loong, Nurul Nadia Ahmad

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This paper discusses the effectiveness of the EEG signal for human identification using four or less of channels of two different types of EEG recordings. Studies have shown that the EEG signal has biometric potential because signal varies from person to person and impossible to replicate and steal. Data were collected from 10 male subjects while resting with eyes open and eyes closed in 5 separate sessions conducted over a course of two weeks. Features were extracted using the wavelet packet decomposition and analyzed to obtain the feature vectors. Subsequently, the neural networks algorithm was used to classify the feature vectors. Results show that, whether or not the subjects- eyes were open are insignificant for a 4– channel biometrics system with a classification rate of 81%. However, for a 2–channel system, the P4 channel should not be included if data is acquired with the subjects- eyes open. It was observed that for 2– channel system using only the C3 and C4 channels, a classification rate of 71% was achieved.

Keywords: Biometric, EEG, Wavelet Packet Decomposition, NeuralNetworks

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Authors: Aline Zeitunlian, Ramzi A. Haraty

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Caching was suggested as a solution for reducing bandwidth utilization and minimizing query latency in mobile environments. Over the years, different caching approaches have been proposed, some relying on the server to broadcast reports periodically informing of the updated data while others allowed the clients to request for the data whenever needed. Until recently a hybrid cache consistency scheme Scalable Asynchronous Cache Consistency Scheme SACCS was proposed, which combined the two different approaches benefits- and is proved to be more efficient and scalable. Nevertheless, caching has its limitations too, due to the limited cache size and the limited bandwidth, which makes the implementation of cache replacement strategy an important aspect for improving the cache consistency algorithms. In this thesis, we proposed a new cache replacement strategy, the Least Unified Value strategy (LUV) to replace the Least Recently Used (LRU) that SACCS was based on. This paper studies the advantages and the drawbacks of the new proposed strategy, comparing it with different categories of cache replacement strategies.

Keywords: Cache consistency, hybrid algorithm, and mobileenvironments

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Authors: Payam Haghparast, Mikhail V. Sorin, Hakim Nesreddine

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The complex oblique shock phenomenon can be simply assumed as a normal shock at the constant area section to simulate a sharp pressure increase and velocity decrease in 1-D thermodynamic models. The assumed normal shock location is one of the greatest sources of error in ejector thermodynamic models. Most researchers consider an arbitrary location without justifying it. Our study compares the effect of normal shock place on ejector dimensions in 1-D models. To this aim, two different ejector experimental test benches, a constant area-mixing ejector (CAM) and a constant pressure-mixing (CPM) are considered, with different known geometries, operating conditions and working fluids (R245fa, R141b). In the first step, in order to evaluate the real value of the efficiencies in the different ejector parts and critical back pressure, a CFD model was built and validated by experimental data for two types of ejectors. These reference data are then used as input to the 1D model to calculate the lengths and the diameters of the ejectors. Afterwards, the design output geometry calculated by the 1D model is compared directly with the corresponding experimental geometry. It was found that there is a good agreement between the ejector dimensions obtained by the 1D model, for both CAM and CPM, with experimental ejector data. Furthermore, it is shown that normal shock place affects only the constant area length as it is proven that the inlet normal shock assumption results in more accurate length. Taking into account previous 1D models, the results suggest the use of the assumed normal shock location at the inlet of the constant area duct to design the supersonic ejectors.

Keywords: 1D model, constant area-mixing, constant pressure-mixing, normal shock location, ejector dimensions.

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Authors: Souvik Bhattacharyya, Gautam Sanyal

Abstract:

Recent years have witnessed the rapid development of the Internet and telecommunication techniques. Information security is becoming more and more important. Applications such as covert communication, copyright protection, etc, stimulate the research of information hiding techniques. Traditionally, encryption is used to realize the communication security. However, important information is not protected once decoded. Steganography is the art and science of communicating in a way which hides the existence of the communication. Important information is firstly hidden in a host data, such as digital image, video or audio, etc, and then transmitted secretly to the receiver.In this paper a data hiding model with high security features combining both cryptography using finite state sequential machine and image based steganography technique for communicating information more securely between two locations is proposed. The authors incorporated the idea of secret key for authentication at both ends in order to achieve high level of security. Before the embedding operation the secret information has been encrypted with the help of finite-state sequential machine and segmented in different parts. The cover image is also segmented in different objects through normalized cut.Each part of the encoded secret information has been embedded with the help of a novel image steganographic method (PMM) on different cuts of the cover image to form different stego objects. Finally stego image is formed by combining different stego objects and transmit to the receiver side. At the receiving end different opposite processes should run to get the back the original secret message.

Keywords: Cover Image, Finite state sequential machine, Melaymachine, Pixel Mapping Method (PMM), Stego Image, NCUT.

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Authors: A. Rajagopal, S. Somasundaram, B. Sowmya, T. Suguna

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Wireless Sensor Networks (WSNs) enable new applications and need non-conventional paradigms for the protocol because of energy and bandwidth constraints, In WSN, sensor node’s life is a critical parameter. Research on life extension is based on Low-Energy Adaptive Clustering Hierarchy (LEACH) scheme, which rotates Cluster Head (CH) among sensor nodes to distribute energy consumption over all network nodes. CH selection in WSN affects network energy efficiency greatly. This study proposes an improved CH selection for efficient data aggregation in sensor networks. This new algorithm is based on Bacterial Foraging Optimization (BFO) incorporated in LEACH.

Keywords: Bacterial Foraging Optimization (BFO), Cluster Head (CH), Data-aggregation protocols, Low-Energy Adaptive Clustering Hierarchy (LEACH).

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Authors: Marios M. Hadjiandreou, Raul Conejeros, Ian Wilson

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This study presents a mathematical modeling approach to the planning of HIV therapies on an individual basis. The model replicates clinical data from typical-progressors to AIDS for all stages of the disease with good agreement. Clinical data from rapid-progressors and long-term non-progressors is also matched by estimation of immune system parameters only. The ability of the model to reproduce these phenomena validates the formulation, a fact which is exploited in the investigation of effective therapies. The therapy investigation suggests that, unlike continuous therapy, structured treatment interruptions (STIs) are able to control the increase in both the drug-sensitive and drug-resistant virus population and, hence, prevent the ultimate progression from HIV to AIDS. The optimization results further suggest that even patients characterised by the same progression type can respond very differently to the same treatment and that the latter should be designed on a case-by-case basis. Such a methodology is presented here.

Keywords: AIDS, chemotherapy, mathematical modeling, optimal control, progression.

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Authors: Yun Won Chung

Abstract:

The energy consumption of home femto base stations (BSs) can be reduced, by turning off the Wi-Fi radio interface when there is no mobile station (MS) under the coverage of the BSs or MSs do not transmit or receive data packet for long time, especially in late night. In the energy-efficient home femto BSs, if MSs have any data packet to transmit and the Wi-Fi radio interface in off state, MSs wake up the Wi-Fi radio interface of home femto BSs by using additional low power radio interface. In this paper, the performance of the energy-efficient home femto BSs from the aspect of energy consumption and cumulative average delay, and show the effect of various parameters on energy consumption and cumulative average delay. From the results, the tradeoff relationship between energy consumption and cumulative average delay is shown and thus, appropriate operation should be needed to balance the tradeoff.

Keywords: energy consumption, power saving, femto base station.

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Authors: Shahin Mirshekari, Mohammadreza Moradi, Hossein Jafari, Mehdi Jafari, Mohammad Ensaf

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This research employs Gaussian Process Regression (GPR) with an ensemble kernel, integrating Exponential Squared, Revised Matérn, and Rational Quadratic kernels to analyze pharmaceutical sales data. Bayesian optimization was used to identify optimal kernel weights: 0.76 for Exponential Squared, 0.21 for Revised Matérn, and 0.13 for Rational Quadratic. The ensemble kernel demonstrated superior performance in predictive accuracy, achieving an R² score near 1.0, and significantly lower values in MSE, MAE, and RMSE. These findings highlight the efficacy of ensemble kernels in GPR for predictive analytics in complex pharmaceutical sales datasets.

Keywords: Gaussian Process Regression, Ensemble Kernels, Bayesian Optimization, Pharmaceutical Sales Analysis, Time Series Forecasting, Data Analysis.

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Authors: H.Mohammadi Majd, M.Jalali Azizpour

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In this paper back-propagation artificial neural network (BPANN )with Levenberg–Marquardt algorithm is employed to predict the deformation of the upsetting process. To prepare a training set for BPANN, some finite element simulations were carried out. The input data for the artificial neural network are a set of parameters generated randomly (aspect ratio d/h, material properties, temperature and coefficient of friction). The output data are the coefficient of polynomial that fitted on barreling curves. Neural network was trained using barreling curves generated by finite element simulations of the upsetting and the corresponding material parameters. This technique was tested for three different specimens and can be successfully employed to predict the deformation of the upsetting process

Keywords: Back-propagation artificial neural network(BPANN), prediction, upsetting

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Authors: V. Sacca, A. Sarica, F. Novellino, S. Barone, T. Tallarico, E. Filippelli, A. Granata, P. Valentino, A. Quattrone

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In the last few years, resting-state functional MRI (rs-fMRI) was widely used to investigate the architecture of brain networks by investigating the Blood Oxygenation Level Dependent response. This technique represented an interesting, robust and reliable approach to compare pathologic and healthy subjects in order to investigate neurodegenerative diseases evolution. On the other hand, the elaboration of rs-fMRI data resulted to be very prone to noise due to confounding factors especially the head motion. Head motion has long been known to be a source of artefacts in task-based functional MRI studies, but it has become a particularly challenging problem in recent studies using rs-fMRI. The aim of this work was to evaluate in MS patients a well-known motion correction algorithm from the FMRIB's Software Library - MCFLIRT - that could be applied to minimize the head motion distortions, allowing to correctly interpret rs-fMRI results.

Keywords: Head motion correction, MCFLIRT algorithm, multiple sclerosis, resting state fMRI.

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