Search results for: deep feed forward neural network

Authors: V. Viliene, V. Sasyte, A. Raceviciute-Stupeliene, R. Gruzauskas

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In horse nutrition, the most suitable cereal for their rations composition could be defined as oats and barley. Oats have high nutritive value because it provides more protein, fiber, iron and zinc than other whole grains, has good taste, and an activity of stimulating metabolic changes in the body. Another cereal – barley is very similar to oats as a feed except for some characteristics that affect how it is used; however, barley is lower in fiber than oats and is classified as a "heavy" feed. The value of oats and barley grain, first of all is dependent on its composition. Near-infrared spectroscopy (NIRS) has long been considered and used as a significant method in component and quality analysis and as an emerging technology for authenticity applications for cereal quality control. This paper presents the chemical and amino acid composition of different varieties of barley and oats, also digestible energy of different cereals for horses. Ten different spring barley (n = 5) and oats (n = 5) varieties, grown in one location in Lithuania, were assayed for their chemical composition (dry matter, crude protein, crude fat, crude ash, crude fiber, starch) and amino acids content, digestible amino acids and amino acids digestibility. Also, the grains digestible energy for horses was calculated. The oats and barley samples reflectance spectra were measured by means of NIRS using Foss-Tecator DS2500 equipment. The chemical components: fat, crude protein, starch and fiber differed statistically (P<0.05) between the oats and barley varieties. The highest total amino acid content between oats was determined in variety Flamingsprofi (4.56 g/kg) and the lowest – variety Circle (3.57 g/kg), and between barley - respectively in varieties Publican (3.50 g/kg) and Sebastian (3.11 g/kg). The different varieties of oats digestible amino acid content varied from 3.11 g/kg to 4.07 g/kg; barley different varieties varied from 2.59 g/kg to 2.94 g/kg. The average amino acids digestibility of oats varied from 74.4% (Liz) to 95.6% (Fen) and in barley - from 75.8 % (Tre) to 89.6% (Fen). The amount of digestible energy in the analyzed varieties of oats and barley was an average compound 13.74 MJ/kg DM and 14.85 MJ/kg DM, respectively. An analysis of the results showed that different varieties of oats compared with barley are preferable for horse nutrition according to the crude fat, crude fiber, ash and separate amino acids content, but the analyzed barley varieties dominated the higher amounts of crude protein, the digestible Liz amount and higher DE content, and thus, could be recommended for making feed formulation for horses combining oats and barley, taking into account the chemical composition of using cereal varieties.

Keywords: barley, digestive energy, horses, nutritional value, oats

Procedia PDF Downloads 205

Authors: Nureni Asafe Yekini, Olawale Nasiru Lawal, Bola Dada, Gabriel Adeyemi Okunlola

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This study is focused on the strengths and challenges associated with using the online social network as a rapidly evolving medium in marketing tourism services and businesses among the youths in Nigeria. The paper examines the Nigerian tourists’ attitude, mainly towards three aspects: application of Internet for travel and tourism; usage of online social networks in sharing travel and tourism experiences; and trust in electronic-media for marketing tourism businesses and services. The aim of this research is to determine the level of application of internet tools in marketing tourism businesses and services in Nigeria. This study reports an empirical analysis based on data obtained from a survey among 1004 Nigerian tourists. The outcome confirms the research hypothesis and points to crucial importance of introducing online social network site for marketing tourism businesses and services in Nigeria, and increasing the awareness for Nigeria as a tourist destination. Moreover, the paper strongly recommends the use of online social network as a tool for marketing tourism businesses and services, and the need for identifying effective framework for application of ICT tools in marketing tourism businesses and services in Nigeria at large.

Keywords: tourism business, internet, online social networks, tourism services, ICT

Procedia PDF Downloads 356

Authors: A. Rabin, G. Mazor, I. Ladizhenski, R. Shneck, Z.

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The hardness and hardenability of AISI D2 cold work tool steel with conventional quenching (CQ), deep cryogenic quenching (DCQ) and rapid deep cryogenic quenching heat treatments caused by temporary porous coating based on magnesium sulfate was investigated. Each of the cooling processes was examined from the perspective of the full process efficiency, heat flux in the austenite-martensite transformation range followed by characterization of the temporary porous layer made of magnesium sulfate using confocal laser scanning microscopy (CLSM), surface and core hardness and hardenability using Vickr’s hardness technique. The results show that the cooling rate (CR) at the austenite-martensite transformation range have a high influence on the hardness of the studied steel.

Keywords: AISI D2, controllable cooling, magnesium sulfate coating, rapid cryogenic heat treatment, temporary porous layer

Procedia PDF Downloads 137

Authors: Namrata Mishra

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Health of a person plays a vital role in the collective health of his community and hence the well-being of the society as a whole. But, in today’s fast paced technology driven world, health issues are increasingly being associated with human behaviors – their lifestyle. Social networks have tremendous impact on the health behavior of individuals. Many researchers have used social network analysis to understand human behavior that implicates their social and economic environments. It would be interesting to use a similar analysis to understand human behaviors that have health implications. This paper focuses on concepts of those behavioural analyses that have health implications using social networks analysis and provides possible algorithmic approaches. The results of these approaches can be used by the governing authorities for rolling out health plans, benefits and take preventive measures, while the pharmaceutical companies can target specific markets, helping health insurance companies to better model their insurance plans.

Keywords: breadth first search, directed graph, health behaviors, social network analysis

Procedia PDF Downloads 471

Authors: Shin-Pin Tseng

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Nowadays, numerous spectral amplitude coding (SAC) fiber-optic code-division-multiple-access (FO-CDMA) techniques were appealing due to their capable of providing moderate security and relieving the effects of multiuser interference (MUI). Nonetheless, the performance of the previous network is degraded due to fixed in-phase cross-correlation (IPCC) value. Based on the above problems, a new SAC FO-CDMA network using partial M-sequence (PMS) code is presented in this study. Because the proposed PMS code is originated from M-sequence code, the system using the PMS code could effectively suppress the effects of MUI. In addition, two-code keying (TCK) scheme can applied in the proposed SAC FO-CDMA network and enhance the whole network performance. According to the consideration of system flexibility, simple optical encoders/decoders (codecs) using fiber Bragg gratings (FBGs) were also developed. First, we constructed a diagram of the SAC FO-CDMA network, including (N/2-1) optical transmitters, (N/2-1) optical receivers, and one N×N star coupler for broadcasting transmitted optical signals to arrive at the input port of each optical receiver. Note that the parameter N for the PMS code was the code length. In addition, the proposed SAC network was using superluminescent diodes (SLDs) as light sources, which then can save a lot of system cost compared with the other FO-CDMA methods. For the design of each optical transmitter, it is composed of an SLD, one optical switch, and two optical encoders according to assigned PMS codewords. On the other hand, each optical receivers includes a 1 × 2 splitter, two optical decoders, and one balanced photodiode for mitigating the effect of MUI. In order to simplify the next analysis, the some assumptions were used. First, the unipolarized SLD has flat power spectral density (PSD). Second, the received optical power at the input port of each optical receiver is the same. Third, all photodiodes in the proposed network have the same electrical properties. Fourth, transmitting '1' and '0' has an equal probability. Subsequently, by taking the factors of phase‐induced intensity noise (PIIN) and thermal noise, the corresponding performance was displayed and compared with the performance of the previous SAC FO-CDMA networks. From the numerical result, it shows that the proposed network improved about 25% performance than that using other codes at BER=10-9. This is because the effect of PIIN was effectively mitigated and the received power was enhanced by two times. As a result, the SAC FO-CDMA network using PMS codes has an opportunity to apply in applications of the next-generation optical network.

Keywords: spectral amplitude coding, SAC, fiber-optic code-division multiple-access, FO-CDMA, partial M-sequence, PMS code, fiber Bragg grating, FBG

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Authors: Raed Hanandeh

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This empirical study examines how knowledge sharing behaviors mediate the effect Technology-driven strategy on virtual organization success in Jordanian tourism sector firms. The results reveal that Social network ties are positively related to web knowledge seeking, web knowledge contributing and interactive system, but negatively related to accidental knowledge leakage. Furthermore, all types of knowledge sharing behavior are positively related to virtual organization success. Data collected from 23 firms. The total number of questionnaires mailed, 250 questionnaires were delivered. 214 were considered valid out of 241 Responses were received. The findings provide evidence that knowledge sharing behavior play a mediating role between Social network ties and virtual organization success and show that, web knowledge seeking, web knowledge contributing and interactive system playing an important impact on virtual organization success through knowledge sharing behaviors.

Keywords: social network ties, virtual organization success, knowledge sharing behaviors, web knowledge

Procedia PDF Downloads 273

Authors: Mihai-Ionut Firescu, Mark A. P. Carson

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We present a case of inferior vena cava agenesis (IVCA) associated with bilateral deep venous thrombosis (DVT) in a patient with Beckwith-Wiedemann syndrome (BWS). In adult patients with BWS presenting with bilateral lower limb oedema, specific aetiological factors should be considered. These include cardiomyopathy and intraabdominal tumours. Congenital malformations of the IVC, through causing relative venous stasis, can lead to lower limb oedema either directly or indirectly by favouring lower limb venous thromboembolism; however, they are yet to be reported as an associated feature of BWS. Given its life-threatening potential, the prompt initiation of treatment for bilateral DVT is paramount. In BWS patients, however, this can prove more complicated. Due to overgrowth, the above-average birth weight can continue throughout childhood. In this case, the patient’s weight reached 170 kg, impacting on anticoagulation choice, as direct oral anticoagulants have a limited evidence base in patients with a body mass above 120 kg. Furthermore, the presence of IVCA leads to a long-term increased venous thrombosis risk. Therefore, patients with IVCA and bilateral DVT warrant specialist consideration and may benefit from multidisciplinary team management, with hematology and vascular surgery input. Conclusion: Here, we showcased a rare cause for bilateral lower limb oedema, respectively bilateral deep venous thrombosis complicating IVCA in a patient with Beckwith-Wiedemann syndrome. The importance of this case lies in its novelty, as the association between IVC agenesis and BWS has not yet been described. Furthermore, the treatment of DVT in such situations requires special consideration, taking into account the patient’s weight and the presence of a significant, predisposing vascular abnormality.

Keywords: Beckwith-Wiedemann syndrome, bilateral deep venous thrombosis, inferior vena cava agenesis, venous thromboembolism

Procedia PDF Downloads 235

Authors: Shayan Mohajer Hamidi

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Federated learning has emerged as a promising approach for distributed model training while preserving data privacy. However, the challenges of communication overhead, limited network resources, and slow convergence hinder its widespread adoption. On the other hand, knowledge distillation has shown great potential in compressing large models into smaller ones without significant loss in performance. In this paper, we propose an innovative framework that combines federated learning and knowledge distillation to address these challenges and enhance the efficiency of distributed learning. Our approach, called Federated Knowledge Distillation (FKD), enables multiple clients in a federated learning setting to collaboratively distill knowledge from a teacher model. By leveraging the collaborative nature of federated learning, FKD aims to improve model compression while maintaining privacy. The proposed framework utilizes a coded teacher model that acts as a reference for distilling knowledge to the client models. To demonstrate the effectiveness of FKD, we conduct extensive experiments on various datasets and models. We compare FKD with baseline federated learning methods and standalone knowledge distillation techniques. The results show that FKD achieves superior model compression, faster convergence, and improved performance compared to traditional federated learning approaches. Furthermore, FKD effectively preserves privacy by ensuring that sensitive data remains on the client devices and only distilled knowledge is shared during the training process. In our experiments, we explore different knowledge transfer methods within the FKD framework, including Fine-Tuning (FT), FitNet, Correlation Congruence (CC), Similarity-Preserving (SP), and Relational Knowledge Distillation (RKD). We analyze the impact of these methods on model compression and convergence speed, shedding light on the trade-offs between size reduction and performance. Moreover, we address the challenges of communication efficiency and network resource utilization in federated learning by leveraging the knowledge distillation process. FKD reduces the amount of data transmitted across the network, minimizing communication overhead and improving resource utilization. This makes FKD particularly suitable for resource-constrained environments such as edge computing and IoT devices. The proposed FKD framework opens up new avenues for collaborative and privacy-preserving distributed learning. By combining the strengths of federated learning and knowledge distillation, it offers an efficient solution for model compression and convergence speed enhancement. Future research can explore further extensions and optimizations of FKD, as well as its applications in domains such as healthcare, finance, and smart cities, where privacy and distributed learning are of paramount importance.

Keywords: federated learning, knowledge distillation, knowledge transfer, deep learning

Procedia PDF Downloads 75

Authors: Noga Bregman

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Accurate and efficient earthquake detection and phase picking are crucial for seismic hazard assessment and emergency response. This study introduces EQMamba, a deep-learning method that combines the strengths of the Earthquake Transformer and the Mamba model for simultaneous earthquake detection and phase picking. EQMamba leverages the computational efficiency of Mamba layers to process longer seismic sequences while maintaining a manageable model size. The proposed architecture integrates convolutional neural networks (CNNs), bidirectional long short-term memory (BiLSTM) networks, and Mamba blocks. The model employs an encoder composed of convolutional layers and max pooling operations, followed by residual CNN blocks for feature extraction. Mamba blocks are applied to the outputs of BiLSTM blocks, efficiently capturing long-range dependencies in seismic data. Separate decoders are used for earthquake detection, P-wave picking, and S-wave picking. We trained and evaluated EQMamba using a subset of the STEAD dataset, a comprehensive collection of labeled seismic waveforms. The model was trained using a weighted combination of binary cross-entropy loss functions for each task, with the Adam optimizer and a scheduled learning rate. Data augmentation techniques were employed to enhance the model's robustness. Performance comparisons were conducted between EQMamba and the EQTransformer over 20 epochs on this modest-sized STEAD subset. Results demonstrate that EQMamba achieves superior performance, with higher F1 scores and faster convergence compared to EQTransformer. EQMamba reached F1 scores of 0.8 by epoch 5 and maintained higher scores throughout training. The model also exhibited more stable validation performance, indicating good generalization capabilities. While both models showed lower accuracy in phase-picking tasks compared to detection, EQMamba's overall performance suggests significant potential for improving seismic data analysis. The rapid convergence and superior F1 scores of EQMamba, even on a modest-sized dataset, indicate promising scalability for larger datasets. This study contributes to the field of earthquake engineering by presenting a computationally efficient and accurate method for simultaneous earthquake detection and phase picking. Future work will focus on incorporating Mamba layers into the P and S pickers and further optimizing the architecture for seismic data specifics. The EQMamba method holds the potential for enhancing real-time earthquake monitoring systems and improving our understanding of seismic events.

Keywords: earthquake, detection, phase picking, s waves, p waves, transformer, deep learning, seismic waves

Procedia PDF Downloads 52

Authors: Atanu K Samanta, Asim Ali Khan

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Due to the acquisition of huge amounts of brain tumor magnetic resonance images (MRI) in clinics, it is very difficult for radiologists to manually interpret and segment these images within a reasonable span of time. Computer-aided diagnosis (CAD) systems can enhance the diagnostic capabilities of radiologists and reduce the time required for accurate diagnosis. An intelligent computer-aided technique for automatic detection of a brain tumor through MRI is presented in this paper. The technique uses the following computational methods; the Level Set for segmentation of a brain tumor from other brain parts, extraction of features from this segmented tumor portion using gray level co-occurrence Matrix (GLCM), and the Artificial Neural Network (ANN) to classify brain tumor images according to their respective types. The entire work is carried out on 50 images having five types of brain tumor. The overall classification accuracy using this method is found to be 98% which is significantly good.

Keywords: brain tumor, computer-aided diagnostic (CAD) system, gray-level co-occurrence matrix (GLCM), tumor segmentation, level set method

Procedia PDF Downloads 512

Authors: Elias Nemer, Greg Vines

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Time-frequency transformation and spectral representations of audio signals are commonly used in various machine learning applications. Training networks on frequency features such as the Mel-Spectrogram or Cochleogram have been proven more effective and convenient than training on-time samples. In practical realizations, these features are created on a different processor and/or pre-computed and stored on disk, requiring additional efforts and making it difficult to experiment with different features. In this paper, we provide a PyTorch framework for creating various spectral features as well as time-frequency transformation and time-domain filter-banks using the built-in trainable conv1d() layer. This allows computing these features on the fly as part of a larger network and enabling easier experimentation with various combinations and parameters. Our work extends the work in the literature developed for that end: First, by adding more of these features and also by allowing the possibility of either starting from initialized kernels or training them from random values. The code is written as a template of classes and scripts that users may integrate into their own PyTorch classes or simply use as is and add more layers for various applications.

Keywords: neural networks Mel-Spectrogram, chromagram, cochleogram, discrete Fourrier transform, PyTorch conv1d()

Procedia PDF Downloads 233

Authors: Mahdi Jalili

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Functional connectivity in the human brain can be represented as a network using electroencephalography (EEG) signals. Network representation of EEG time series can be an efficient vehicle to understand the underlying mechanisms of brain function. Brain functional networks – whose nodes are brain regions and edges correspond to functional links between them – are characterized by neurobiologically meaningful graph theory metrics. This study investigates the degree to which graph theory metrics are sex dependent. To this end, EEGs from 24 healthy female subjects and 21 healthy male subjects were recorded in eyes-closed resting state conditions. The connectivity matrices were extracted using correlation analysis and were further binarized to obtain binary functional networks. Global and local efficiency measures – as graph theory metrics– were computed for the extracted networks. We found that male brains have a significantly greater global efficiency (i.e., global communicability of the network) across all frequency bands for a wide range of cost values in both hemispheres. Furthermore, for a range of cost values, female brains showed significantly greater right-hemispheric local efficiency (i.e., local connectivity) than male brains.

Keywords: EEG, brain, functional networks, network science, graph theory

Procedia PDF Downloads 443

Authors: Hossain Samani, Mohammad Ehteram

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In this paper, we consider finite volume method (FVM) in water hammer. We will simulate these techniques on a looped network with complex boundary conditions. After comparing methods, we see the FVM method as the best method. We compare the results of FVM with experimental data. Finite volume using staggered grid is applied for solving water hammer equations.

Keywords: hydraulic transient, water hammer, interpolation, non-liner interpolation

Procedia PDF Downloads 349

Authors: Diogo Prosdocimi, Don Ross, Matthew Townshend

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Road authorities are mandated within limited budgets to both deliver improved access to basic services and facilitate economic growth. This responsibility is further complicated if maintenance backlogs and funding shortfalls exist, as evident in many countries including South Africa. These conditions require authorities to make difficult prioritisation decisions, with the effect that Road Asset Management Systems with a one-dimensional focus on traffic volumes may overlook the maintenance of low-volume roads that provide isolated communities with vital access to basic services. Given these challenges, this paper overlays the full South African road network with geo-referenced information for population, primary and secondary schools, and healthcare facilities to identify the network of connective roads between communities and basic service centres. This connective network is then rationalised according to the Gross Value Added and number of jobs per mesozone, administrative and functional road classifications, speed limit, and road length, location, and name to estimate the Basic Access Road Network. A two-step floating catchment area (2SFCA) method, capturing a weighted assessment of drive-time to service centres and the ratio of people within a catchment area to teachers and healthcare workers, is subsequently applied to generate a Multivariate Road Index. This Index is used to assign higher maintenance priority to roads within the Basic Access Road Network that provide more people with better access to services. The relatively limited incidence of Basic Access Roads indicates that authorities could maintain the entire estimated network without exhausting the available road budget before practical economic considerations get any purchase. Despite this fact, a final case study modelling exercise is performed for the Namakwa District Municipality to demonstrate the extent to which optimal relocation of schools and healthcare facilities could minimise the Basic Access Road Network and thereby release budget for investment in roads that best promote GDP growth.

Keywords: basic access roads, multivariate road index, road prioritisation, two-step floating catchment area method

Procedia PDF Downloads 231

Authors: Hyunbin Choi, Jihyeon Noh, Changwon Lim

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In this paper, we propose SiamMask++, an architecture that performs layer-wise aggregation and depth-wise cross-correlation and introduce multi-RPN module and multi-MASK module to improve EAO (Expected Average Overlap), a representative performance evaluation metric for Visual Object Tracking (VOT) challenge. The proposed architecture, SiamMask++, has two versions, namely, bi_SiamMask++, which satisfies the real time (56fps) on systems equipped with GPUs (Titan XP), and rf_SiamMask++, which combines mask refinement modules for EAO improvements. Tests are performed on VOT2016, VOT2018 and VOT2019, the representative datasets of Visual Object Tracking tasks labeled as rotated bounding boxes. SiamMask++ perform better than SiamMask on all the three datasets tested. SiamMask++ is achieved performance of 62.6% accuracy, 26.2% robustness and 39.8% EAO, especially on the VOT2018 dataset. Compared to SiamMask, this is an improvement of 4.18%, 37.17%, 23.99%, respectively. In addition, we do an experimental in-depth analysis of how much the introduction of features and multi modules extracted from the backbone affects the performance of our model in the VOT task.

Keywords: visual object tracking, video, deep learning, layer wise aggregation, Siamese network

Procedia PDF Downloads 159

Authors: Sean Gay, Cristina Tat

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This presentation will detail how a university in Western Japan has implemented its English for Academic Purposes (EAP) program during the onset of CoViD-19 in the spring semester of 2020. In the spring semester of 2020, after a 2 week delay, all courses within the School of Policy Studies EAP Program at Kwansei Gakuin University were offered in an online asynchronous format. The rationale for this decision was not to disadvantage students who might not have access to devices necessary for taking part in synchronous online lessons. The course coordinators were tasked with consolidating the materials originally designed for face-to-face14 week courses for a 12 week asynchronous online semester and with uploading the modified course materials to Luna, the university’s network, which is a modified version of Blackboard. Based on research to determine the social and academic impacts of this CoViD-19 ERT approach on the students who took part in this EAP program, this presentation explains how future curriculum design and implementation can be managed in a post-CoViD world. There are a wide variety of lessons that were salient. The role of the classroom as a social institution was very prominent; however, awareness of cognitive burdens and strategies to mitigate that burden may be more valuable for teachers. The lessons learned during this period of ERT can help teachers moving forward.

Keywords: asynchronous online learning, emergency remote teaching (ERT), online curriculum design, synchronous online learning

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Authors: Moshfeghi Sogand

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Free radicals can be produced during the respiratory oxidation of different cells. These free radicals can damage to various macromolecules as protein ,fat, nucleic acids and … are harmful for body. The natural defence system that can prevent the damage of free radicals and nuteralized them , have tittled under the name total antioxidant capacity (TAC ). Se is one main antioxidant part in TAC , because it is one main part in structure of some body antioxidant enzymes such as GPX(glutathione peroxidase). Blood SE ,GPX and TAC probably can change by feeding of different selenium supplement diet in late pregnancy and also may transport from maternal blood to its fetus or by clostrum after calving. In this respect we have determined 100 pregnant dairy cattle (in the same condition of age , race and number of parturient) then devided them to 4 groups feed them in 3 last pregnancy months by different selenium diets. Group1:controle no se supplementation , group2: recived 0/3 ppm of the daily diet Saccharomyces Cervisiae . group3 :recived selenium _ rich yeast(containing200ppm selenium)was mixed with total daily ration fed. Group4: recived se _rich yeast(containing300 ppm selenium)was mixed with total daily ration fed. Then measured blood SE,GPX and TAC levels in them and in 3 days newborn calves after calving. The results were analysed by Tukey Anova test and the highest level of blood SE ,GPX and TAC was shown in cattle that feed fermented SE_yeast diet and in their 3 days newborn calves.

Keywords: SE, TAC, SE DIETS, FRAP

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Authors: Hassan Nabipour Afrouzi, Naser Mahmoudnia

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Broiler production has increased rapidly in tropical and subtropical regions in the past and sustained growth is forecast for the future. One of the greatest challenges to efficient production in these regions is reduced performance from warm and hot weather conditions. There are many ways to decrease these detrimental effects of heat on broiler chickens. One way is to supplement broiler diet with betaine added to feed or drinking water. A review of the results of this study suggest that betaine supplement was effective to significantly improve body weight and feed conversion ratio at the initial stages of growth but not in the finisher stages (P<0/05). It was also demonstrated that the use of betaine significantly reduced the percentage of abdominal meat and the percentage of breast meat (P<0/05), but had no effect on other carcass compositions. Betaine may improve the digestibility of specific nutrients. Betaine, as a methyl donor provides labile methyl groups for the synthesis of several metabolically active substances such as creatine and carnitine. Oil in a broiler diet is known to promote a response to dietary betaine supplements, that is, chicks have a higher demand for betaine with a high fat diet. This study implies that betaine supplement may stimulate protection of intestinal epithelium against osmotic disturbance, improve digestion and absorption conditions of the gastrointestinal tract and promote amended use of nutrients.

Keywords: heat stress, betaine, performance, broiler‚ growth

Procedia PDF Downloads 592

Authors: Seyed Mahdi Jameii

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Underwater wireless sensor networks have been attracting the interest of many ‎researchers lately, and the past three decades have beheld the rapid progress of ‎underwater acoustic communication. One of the major problems in underwater wireless ‎sensor networks is how to transfer data from the moving node to the base stations and ‎choose the optimized route for data transmission. Secure routing in underwater ‎wireless sensor network (UWCNs) is necessary for packet delivery. Some routing ‎protocols are proposed for underwater wireless sensor networks. However, a few ‎researches have been done on secure routing in underwater sensor networks. In this ‎article, a secure routing protocol is provided to resist against wormhole and sybil ‎attacks. The results indicated acceptable performance in terms of increasing the packet ‎delivery ratio with regards to the attacks, increasing network lifetime by creating ‎balance in the network energy consumption, high detection rates against the attacks, ‎and low-end to end delay.‎

Keywords: attacks, routing, security, underwater wireless sensor networks

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Authors: Duc Dong Do, Ngoc Ha Tran, Thanh Hai Dang, Cao Cuong Dang, Xuan Huan Hoang

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Global aligning two protein-protein interaction networks is an essentially important task in bioinformatics/computational biology field of study. It is a challenging and widely studied research topic in recent years. Accurately aligned networks allow us to identify functional modules of proteins and/ororthologous proteins from which unknown functions of a protein can be inferred. We here introduce a novel efficient heuristic global network alignment algorithm called FASTAn, including two phases: the first to construct an initial alignment and the second to improve such alignment by exerting a local optimization repeated procedure. The experimental results demonstrated that FASTAn outperformed the state-of-the-art global network alignment algorithm namely SPINAL in terms of both commonly used objective scores and the run-time.

Keywords: FASTAn, Heuristic algorithm, biological network alignment, protein-protein interaction networks

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Authors: D. Dorota

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This paper presents a method to generate and schedule task in the architecture of embedded systems based on the simulated annealing. This method takes into account the attribute of divisibility of tasks. A proposal represents the process in the form of trees. Despite the fact that the architecture of Network-on-Chip (NoC) is an interesting alternative to a bus architecture based on multi-processors systems, it requires a lot of work that ensures the optimization of communication. This paper proposes an effective approach to generate dedicated NoC topology solving communication problems. Network NoC is generated taking into account the energy consumption and resource issues. Ultimately generated is minimal, dedicated NoC topology. The proposed solution is assumed to be a simple router design and the minimum number of lines.

Keywords: Network-on-Chip, NoC-based embedded systems, scheduling task in embedded systems, simulated annealing

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Authors: Senda Trabelsi, Sébastien Poncet, Michel Poirier

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Ice slurries are considered as a promising phase-changing secondary fluids for air-conditioning, packaging or cooling industrial processes. An experimental study has been here carried out to measure the rheological characteristics of ice slurries. Ice slurries consist in a solid phase (flake ice crystals) and a liquid phase. The later is composed of a mixture of liquid water and an additive being here either (1) Propylene-Glycol (PG) or (2) Ethylene-Glycol (EG) used to lower the freezing point of water. Concentrations of 5%, 14% and 24% of both additives are investigated with ice mass fractions ranging from 5% to 85%. The rheological measurements are carried out using a Discovery HR-2 vane-concentric cylinder with four full-length blades. The experimental results show that the behavior of ice slurries is generally non-Newtonian with shear-thinning or shear-thickening behaviors depending on the experimental conditions. In order to determine the consistency and the flow index, the Herschel-Bulkley model is used to describe the behavior of ice slurries. The present results are finally validated against an experimental database found in the literature and the predictions of an Artificial Neural Network model.

Keywords: ice slurry, propylene-glycol, ethylene-glycol, rheology

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Authors: Naim Karasekreter, Ugur Fidan, Fatih Basciftci

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Wireless sensor networks are networks in which more than one sensor node is organized among themselves. The working principle is based on the transfer of the sensed data over the other nodes in the network to the central station. Wireless sensor networks concentrate on routing algorithms, energy efficiency and clustering algorithms. In the clustering method, the nodes in the network are divided into clusters using different parameters and the most suitable cluster head is selected from among them. The data to be sent to the center is sent per cluster, and the cluster head is transmitted to the center. With this method, the network traffic is reduced and the energy efficiency of the nodes is increased. In this study, clustering algorithms were examined in terms of clustering performances and cluster head selection characteristics to try to identify weak and strong sides. This work is supported by the Project 17.Kariyer.123 of Afyon Kocatepe University BAP Commission.

Keywords: wireless sensor networks (WSN), clustering algorithm, cluster head, clustering

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Authors: Sakshi V. Tantak, Vishap K. Malik, Neelanjney Pilarisetty

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A patent is an exclusive right granted for an invention. It can be a product or a process that provides an innovative method of doing something, or offers a new technical perspective or solution to a problem. A patent can be obtained by making the technical information and details about the invention publicly available. The patent owner has exclusive rights to prevent or stop anyone from using the patented invention for commercial uses. Any commercial usage, distribution, import or export of a patented invention or product requires the patent owner’s consent. It has been observed that the central and important parts of patents are scripted in idiosyncratic and complex linguistic structures that can be difficult to read, comprehend or interpret for the masses. The abstracts of these patents tend to obfuscate the precise nature of the patent instead of clarifying it via direct and simple linguistic constructs. This makes it necessary to have an efficient access to this knowledge via concise and transparent summaries. However, as mentioned above, due to complex and repetitive linguistic constructs and extremely long sentences, common extraction-oriented automatic text summarization methods should not be expected to show a remarkable performance when applied to patent documents. Other, more content-oriented or abstractive summarization techniques are able to perform much better and generate more concise summaries. This paper proposes an efficient summarization system for patents using artificial intelligence, natural language processing and deep learning techniques to condense the knowledge and essential information from a patent document into a single summary that is easier to understand without any redundant formatting and difficult jargon.

Keywords: abstractive summarization, deep learning, natural language Processing, patent document

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Authors: Riddhiman Sherlekar, Umang Paladia, Rachit Desai, Yash Patel

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The competitive scenario of the oil and gas market is a challenge for today’s plant designers to achieve designs that meet client expectations with shrinking budgets, safety requirements, and operating flexibility. Natural Gas Liquids have three main industrial uses. They can be used as fuels, or as petrochemical feedstock or as refinery blends that can be further processed and sold as straight run cuts, such as naphtha, kerosene and gas oil. NGL extraction is not a chemical reaction. It involves the separation of heavier hydrocarbons from the main gas stream through pressure as temperature reduction, which depending upon the degree of NGL extraction may involve cryogenic process. Previous technologies i.e. short cycle dry desiccant absorption, Joule-Thompson or Low temperature refrigeration, lean oil absorption have been giving results of only 40 to 45% ethane recoveries, which were unsatisfying depending upon the current scenario of down turn market. Here new technology has been suggested for boosting up the recoveries of ethane+ up to 95% and up to 99% for propane+ components. Cryogenic plants provide reboiling to demethanizers by using part of inlet feed gas, or inlet feed split. If the two stream temperatures are not similar, there is lost work in the mixing operation unless the designer has access to some proprietary design. The concept introduced in this process consists of reboiling the demethanizer with the residue gas, or residue gas split. The innovation of this process is that it does not use the typical inlet gas feed split type of flow arrangement to reboil the demethanizer or deethanizer column, but instead uses an open heat pump scheme to that effect. The residue gas compressor provides the heat pump effect. The heat pump stream is then further cooled and entered in the top section of the column as a cold reflux. Because of the nature of this design, this process offers the opportunity to operate at full ethane rejection or recovery. The scheme is also very adaptable to revamp existing facilities. This advancement can be proven not only in enhancing the results but also provides operational flexibility, optimize heat exchange, introduces equipment cost reduction, opens a future for the innovative designs while keeping execution costs low.

Keywords: deethanizer, demethanizer, residue gas, NGL

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Authors: Wiwat Kittinaraporn, Napat Harnpornchai, Sutja Boonyachut

Abstract:

This paper deals with the application of a novel neural network technique, so-called Support Vector Machine (SVM). The objective of this study is to explore the variable and parameter of forecasting factors in the construction industry to build up forecasting model for construction quantity in Thailand. The scope of the research is to study the non-residential construction quantity in Thailand. There are 44 sets of yearly data available, ranging from 1965 to 2009. The correlation between economic indicators and construction demand with the lag of one year was developed by Apichat Buakla. The selected variables are used to develop SVM models to forecast the non-residential construction quantity in Thailand. The parameters are selected by using ten-fold cross-validation method. The results are indicated in term of Mean Absolute Percentage Error (MAPE). The MAPE value for the non-residential construction quantity predicted by Epsilon-SVR in corporation with Radial Basis Function (RBF) of kernel function type is 5.90. Analysis of the experimental results show that the support vector machine modelling technique can be applied to forecast construction quantity time series which is useful for decision planning and management purpose.

Keywords: forecasting, non-residential, construction, support vector machines

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Authors: Ankit Sinha, Soham Banerjee, Pratik Chattopadhyay

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Automated product recognition in retail stores is an important real-world application in the domain of Computer Vision and Pattern Recognition. In this paper, we consider the problem of automatically identifying the classes of the products placed on racks in retail stores from an image of the rack and information about the query/product images. We improve upon the existing approaches in terms of effectiveness and memory requirement by developing a two-stage object detection and recognition pipeline comprising of a Faster-RCNN-based object localizer that detects the object regions in the rack image and a ResNet-18-based image encoder that classifies the detected regions into the appropriate classes. Each of the models is fine-tuned using appropriate data sets for better prediction and data augmentation is performed on each query image to prepare an extensive gallery set for fine-tuning the ResNet-18-based product recognition model. This encoder is trained using a triplet loss function following the strategy of online-hard-negative-mining for improved prediction. The proposed models are lightweight and can be connected in an end-to-end manner during deployment to automatically identify each product object placed in a rack image. Extensive experiments using Grozi-32k and GP-180 data sets verify the effectiveness of the proposed model.

Keywords: retail stores, faster-RCNN, object localization, ResNet-18, triplet loss, data augmentation, product recognition

Procedia PDF Downloads 156

Authors: Mohd Khairul Bazli Mohd Aziz, Fadhilah Yusof, Zulkifli Yusop, Zalina Mohd Daud, Mohammad Afif Kasno

Abstract:

Recent developed rainfall network design techniques have been discussed and compared by many researchers worldwide due to the demand of acquiring higher levels of accuracy from collected data. In many studies, rain-gauge networks are designed to provide good estimation for areal rainfall and for flood modelling and prediction. In a certain study, even using lumped models for flood forecasting, a proper gauge network can significantly improve the results. Therefore existing rainfall network in Johor must be optimized and redesigned in order to meet the required level of accuracy preset by rainfall data users. The well-known geostatistics method (variance-reduction method) that is combined with simulated annealing was used as an algorithm of optimization in this study to obtain the optimal number and locations of the rain gauges. Rain gauge network structure is not only dependent on the station density; station location also plays an important role in determining whether information is acquired accurately. The existing network of 84 rain gauges in Johor is optimized and redesigned by using rainfall, humidity, solar radiation, temperature and wind speed data during monsoon season (November – February) for the period of 1975 – 2008. Three different semivariogram models which are Spherical, Gaussian and Exponential were used and their performances were also compared in this study. Cross validation technique was applied to compute the errors and the result showed that exponential model is the best semivariogram. It was found that the proposed method was satisfied by a network of 64 rain gauges with the minimum estimated variance and 20 of the existing ones were removed and relocated. An existing network may consist of redundant stations that may make little or no contribution to the network performance for providing quality data. Therefore, two different cases were considered in this study. The first case considered the removed stations that were optimally relocated into new locations to investigate their influence in the calculated estimated variance and the second case explored the possibility to relocate all 84 existing stations into new locations to determine the optimal position. The relocations of the stations in both cases have shown that the new optimal locations have managed to reduce the estimated variance and it has proven that locations played an important role in determining the optimal network.

Keywords: geostatistics, simulated annealing, semivariogram, optimization

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Authors: Naveed Ghani, Samreen Javed

Abstract:

In today’s heterogeneous network environment, there is a growing demand for distrust clients to jointly execute secure network to prevent from malicious attacks as the defining task of propagating malicious code is to locate new targets to attack. Residual risk is always there no matter what solutions are implemented or whet so ever security methodology or standards being adapted. Security is the first and crucial phase in the field of Computer Science. The main aim of the Computer Security is gathering of information with secure network. No one need wonder what all that malware is trying to do: It's trying to steal money through data theft, bank transfers, stolen passwords, or swiped identities. From there, with the help of our survey we learn about the importance of white listing, antimalware programs, security patches, log files, honey pots, and more used in banks for financial data protection but there’s also a need of implementing the IPV6 tunneling with Crypto data transformation according to the requirements of new technology to prevent the organization from new Malware attacks and crafting of its own messages and sending them to the target. In this paper the writer has given the idea of implementing IPV6 Tunneling Secessions on private data transmission from financial organizations whose secrecy needed to be safeguarded.

Keywords: network worms, malware infection propagating malicious code, virus, security, VPN

Procedia PDF Downloads 358

Authors: S. Al-Khalasi, O. Mahgoub, H. Yaakub

Abstract:

This study evaluated the use of raw or processed Prosopis juliflora (Meskit) pods as a major ingredient in a formulated ration to provide an alternative non-conventional concentrate for livestock feeding in Oman. Dry Meskit pods were reduced to lengths of 0.5- 1.0 cm to ensure thorough mixing into three diets. Meskit pods were subjected to two types of treatments; roasting and soaking. They were roasted at 150оC for 30 minutes using a locally-made roasting device (40 kg barrel container rotated by electric motor and heated by flame gas cooker). Chopped pods were soaked in tap water for 24 hours and dried for 2 days under the sun with frequent turning. The Meskit-pod-based diets (MPBD) were formulated and pelleted from 500 g/kg ground Meskit pods, 240 g/kg wheat bran, 200 g/kg barley grain, 50 g/kg local dried sardines and 10 g/kg of salt. Twenty four 10 months-old intact Omani male lambs with average body weight of 27.3 kg (± 0.5 kg) were used in a feeding trial for 84 days. They were divided (on body weight basis) and allocated to four diet combination groups. These were: Rhodes grass hay (RGH) plus a general ruminant concentrate (GRC); RGH plus raw Meskit pods (RMP) based concentrate; RGH plus roasted Meskit pods (ROMP) based concentrate; RGH plus soaked Meskit pods (SMP) based concentrate Daily feed intakes and bi-weekly body weights were recorded. MPBD had higher contents of crude protein (CP), acid detergent fibre (ADF) and neutral detergent fibre (NDF) than the GRC. Animals fed various types of MPBD did not show signs of ill health. There was a significant effect of feeding ROMP on the performance of Omani sheep compared to RMP and SMP. The ROMP fed animals had similar performance to those fed the GRC in terms of feed intake, body weight gain and feed conversion ratio (FCR).This study indicated that roasted Meskit pods based diet may be used instead of the commercial concentrate for feeding Omani sheep without adverse effects on performance. It offers a cheap alternative source of protein and energy for feeding Omani sheep. Also, it might help in solving the spread impact of Meskit trees, maintain the ecosystem and helping in preserving the local tree species.

Keywords: growth, Meskit, Omani sheep, Prosopis juliflora

Procedia PDF Downloads 476