scholarly journals A Model to Determine the Propagation Losses Based on the Integration of Hata-Okumura and Wavelet Neural Models

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Luis F. Pedraza ◽  
Cesar A. Hernández ◽  
Danilo A. López
Keyword(s):  
Cellular Network ◽  
Large Scale ◽  
Wireless Systems ◽  
Neural Model ◽  
Propagation Model ◽  
Neural Models ◽  
Mobile Cellular ◽  
Proposed Model ◽  
Propagation Losses ◽  
High Degree

Radioelectric spectrum occupancy forecast has proven useful for the design of wireless systems able to harness spectrum opportunities like cognitive radio. This paper proposes the development of a model that identifies propagation losses and spectrum opportunities in a channel of a mobile cellular network for an urban environment using received signal power forecast. The proposed model integrates the Hata-Okumura (H-O) large-scale propagation model with a wavelet neural model. The model results, obtained through simulations, show that the wavelet neural model forecasts with a high degree of precision, which is consistent with the observed behavior in experiments carried out in wireless systems of this type.

A GPS Based Deterministic Channel Allocation for Cellular Network in Mobile Computing

2011 ◽  
pp. 277-290
Author(s):  
Lutfi Mohammed Omer Khanbary ◽  
Deo Prakash Vidyarthi
Keyword(s):  
Cellular Network ◽  
Computational Models ◽  
Channel Allocation ◽  
Radio Channel ◽  
Mobile Hosts ◽  
Knowledge Based ◽  
Mobile Cellular ◽  
Proposed Model ◽  
Two Parameters

The scarcity of the radio channel is the main bottleneck toward maintaining the quality of service (QoS) in a mobile cellular network. As channel allocation schemes become more complex and computationally demanding, alternative computational models that include knowledge-based algorithms and provide the means for faster processing are becoming a topic of research interest. An efficient deterministic technique, capable of handling channel allocation problems, is introduced as an alternative. The proposed model utilizes the Global Positioning System (GPS) data for tracing the hosts’ likely movements within and across the cells and allocates the channels to the mobile devices accordingly. The allocation of the channels to the mobile hosts is deterministic in the sense that the decision of the channel allocation is based on the realistic data received from the GPS about the hosts’ movements. The performance of the proposed technique has been evaluated by conducting the simulation experiments for the two parameters—call blocking and handoff failures. Also, a comparison of the proposed model with an earlier model has been carried out to estimate the effectiveness of the proposed technique. Experimental results reveal that the proposed technique performs better and is more realistic as well.

A GPS Based Deterministic Channel Allocation for Cellular Network in Mobile Computing

2010 ◽  
pp. 1614-1630
Author(s):  
Lutfi Mohammed Omer Khanbary ◽  
Deo Prakash Vidyarthi
Keyword(s):  
Cellular Network ◽  
Computational Models ◽  
Channel Allocation ◽  
Radio Channel ◽  
Mobile Hosts ◽  
Knowledge Based ◽  
Mobile Cellular ◽  
Proposed Model ◽  
Two Parameters

The scarcity of the radio channel is the main bottleneck toward maintaining the quality of service (QoS) in a mobile cellular network. As channel allocation schemes become more complex and computationally demanding, alternative computational models that include knowledge-based algorithms and provide the means for faster processing are becoming a topic of research interest. An efficient deterministic technique, capable of handling channel allocation problems, is introduced as an alternative. The proposed model utilizes the Global Positioning System (GPS) data for tracing the hosts’ likely movements within and across the cells and allocates the channels to the mobile devices accordingly. The allocation of the channels to the mobile hosts is deterministic in the sense that the decision of the channel allocation is based on the realistic data received from the GPS about the hosts’ movements. The performance of the proposed technique has been evaluated by conducting the simulation experiments for the two parameters—call blocking and handoff failures. Also, a comparison of the proposed model with an earlier model has been carried out to estimate the effectiveness of the proposed technique. Experimental results reveal that the proposed technique performs better and is more realistic as well.

A GPS Based Deterministic Channel Allocation for Cellular Network in Mobile Computing

2009 ◽  
Vol 5 (4) ◽  
pp. 33-51
Author(s):  
Lutfi Mohammed Omer Khanbary ◽  
Deo Prakash Vidyarthi
Keyword(s):  
Cellular Network ◽  
Computational Models ◽  
Channel Allocation ◽  
Radio Channel ◽  
Mobile Hosts ◽  
Knowledge Based ◽  
Mobile Cellular ◽  
Proposed Model ◽  
Two Parameters

The scarcity of the radio channel is the main bottleneck toward maintaining the quality of service (QoS) in a mobile cellular network. As channel allocation schemes become more complex and computationally demanding, alternative computational models that include knowledge-based algorithms and provide the means for faster processing are becoming a topic of research interest. An efficient deterministic technique, capable of handling channel allocation problems, is introduced as an alternative. The proposed model utilizes the Global Positioning System (GPS) data for tracing the hosts’ likely movements within and across the cells and allocates the channels to the mobile devices accordingly. The allocation of the channels to the mobile hosts is deterministic in the sense that the decision of the channel allocation is based on the realistic data received from the GPS about the hosts’ movements. The performance of the proposed technique has been evaluated by conducting the simulation experiments for the two parameters—call blocking and handoff failures. Also, a comparison of the proposed model with an earlier model has been carried out to estimate the effectiveness of the proposed technique. Experimental results reveal that the proposed technique performs better and is more realistic as well.

scholarly journals Meaningful Answer Generation of E-Commerce Question-Answering

2021 ◽  
Vol 39 (2) ◽  
pp. 1-26
Author(s):  
Shen Gao ◽  
Xiuying Chen ◽  
Zhaochun Ren ◽  
Dongyan Zhao ◽  
Rui Yan
Keyword(s):  
Large Scale ◽  
Question Answering ◽  
State Of The Art ◽  
Neural Model ◽  
Product Reviews ◽  
Neural Models ◽  
Product Attributes ◽  
Human Evaluation ◽  
Final Answer ◽  
Answer Pattern

In e-commerce portals, generating answers for product-related questions has become a crucial task. In this article, we focus on the task of product-aware answer generation , which learns to generate an accurate and complete answer from large-scale unlabeled e-commerce reviews and product attributes. However, safe answer problems (i.e., neural models tend to generate meaningless and universal answers) pose significant challenges to text generation tasks, and e-commerce question-answering task is no exception. To generate more meaningful answers, in this article, we propose a novel generative neural model, called the Meaningful Product Answer Generator ( MPAG ), which alleviates the safe answer problem by taking product reviews, product attributes, and a prototype answer into consideration. Product reviews and product attributes are used to provide meaningful content, while the prototype answer can yield a more diverse answer pattern. To this end, we propose a novel answer generator with a review reasoning module and a prototype answer reader. Our key idea is to obtain the correct question-aware information from a large-scale collection of reviews and learn how to write a coherent and meaningful answer from an existing prototype answer. To be more specific, we propose a read-and-write memory consisting of selective writing units to conduct reasoning among these reviews . We then employ a prototype reader consisting of comprehensive matching to extract the answer skeleton from the prototype answer. Finally, we propose an answer editor to generate the final answer by taking the question and the above parts as input. Conducted on a real-world dataset collected from an e-commerce platform, extensive experimental results show that our model achieves state-of-the-art performance in terms of both automatic metrics and human evaluations. Human evaluation also demonstrates that our model can consistently generate specific and proper answers.

Model and Method for Contributor’s Quality Assessment in Community Image Tagging Systems

2018 ◽  
pp. 45-51
Author(s):  
A. V. Ponomarev
Keyword(s):  
Large Scale ◽  
Wide Spectrum ◽  
Preference Relation ◽  
Pairwise Comparison ◽  
Ground Truth ◽  
Comparison Method ◽  
Characteristic Matrix ◽  
Image Tagging ◽  
Proposed Model

Introduction: Large-scale human-computer systems involving people of various skills and motivation into the information processing process are currently used in a wide spectrum of applications. An acute problem in such systems is assessing the expected quality of each contributor; for example, in order to penalize incompetent or inaccurate ones and to promote diligent ones.Purpose: To develop a method of assessing the expected contributor’s quality in community tagging systems. This method should only use generally unreliable and incomplete information provided by contributors (with ground truth tags unknown).Results:A mathematical model is proposed for community image tagging (including the model of a contributor), along with a method of assessing the expected contributor’s quality. The method is based on comparing tag sets provided by different contributors for the same images, being a modification of pairwise comparison method with preference relation replaced by a special domination characteristic. Expected contributors’ quality is evaluated as a positive eigenvector of a pairwise domination characteristic matrix. Community tagging simulation has confirmed that the proposed method allows you to adequately estimate the expected quality of community tagging system contributors (provided that the contributors' behavior fits the proposed model).Practical relevance: The obtained results can be used in the development of systems based on coordinated efforts of community (primarily, community tagging systems). 

Hybrid Deep Neural Model for Duplicate Question Detection in Transliterated Bi-lingual Data

2019 ◽  
Vol 12 ◽  
Author(s):  
Seema Rani ◽  
Avadhesh Kumar ◽  
Naresh Kumar
Keyword(s):  
Question Answering ◽  
Neural Model ◽  
Manhattan Distance ◽  
Semantic Matching ◽  
English Only ◽  
Detection Model ◽  
Proposed Model ◽  
Reported Study ◽  
Social Media Platforms ◽  
Efficient Information

Background: Duplicate content often corrupts the filtering mechanism in online question answering. Moreover, as users are usually more comfortable conversing in their native language questions, transliteration adds to the challenges in detecting duplicate questions. This compromises with the response time and increases the answer overload. Thus, it has now become crucial to build clever, intelligent and semantic filters which semantically match linguistically disparate questions. Objective: Most of the research on duplicate question detection has been done on mono-lingual, majorly English Q&A platforms. The aim is to build a model which extends the cognitive capabilities of machines to interpret, comprehend and learn features for semantic matching in transliterated bi-lingual Hinglish (Hindi + English) data acquired from different Q&A platforms. Method: In the proposed DQDHinglish (Duplicate Question Detection) Model, firstly language transformation (transliteration & translation) is done to convert the bi-lingual transliterated question into a mono-lingual English only text. Next a hybrid of Siamese neural network containing two identical Long-term-Short-memory (LSTM) models and Multi-layer perceptron network is proposed to detect semantically similar question pairs. Manhattan distance function is used as the similarity measure. Result: A dataset was prepared by scrapping 100 question pairs from various social media platforms, such as Quora and TripAdvisor. The performance of the proposed model on the basis of accuracy and F-score. The proposed DQDHinglish achieves a validation accuracy of 82.40%. Conclusion: A deep neural model was introduced to find semantic match between English question and a Hinglish (Hindi + English) question such that similar intent questions can be combined to enable fast and efficient information processing and delivery. A dataset was created and the proposed model was evaluated on the basis of performance accuracy. To the best of our knowledge, this work is the first reported study on transliterated Hinglish semantic question matching.

Multi Disease-Prediction Framework Using Hybrid Deep Learning: An Optimal Prediction Model (Preprint)

2020 ◽  
Author(s):  
Anusha Ampavathi ◽  
Vijaya Saradhi T
Keyword(s):  
Feature Extraction ◽  
Big Data ◽  
Deep Learning ◽  
Weight Function ◽  
Optimization Algorithm ◽  
Large Scale ◽  
Heuristic Algorithms ◽  
Disease Prediction ◽  
Health Care Decisions ◽  
Proposed Model

UNSTRUCTURED Big data and its approaches are generally helpful for healthcare and biomedical sectors for predicting the disease. For trivial symptoms, the difficulty is to meet the doctors at any time in the hospital. Thus, big data provides essential data regarding the diseases on the basis of the patient’s symptoms. For several medical organizations, disease prediction is important for making the best feasible health care decisions. Conversely, the conventional medical care model offers input as structured that requires more accurate and consistent prediction. This paper is planned to develop the multi-disease prediction using the improvised deep learning concept. Here, the different datasets pertain to “Diabetes, Hepatitis, lung cancer, liver tumor, heart disease, Parkinson’s disease, and Alzheimer’s disease”, from the benchmark UCI repository is gathered for conducting the experiment. The proposed model involves three phases (a) Data normalization (b) Weighted normalized feature extraction, and (c) prediction. Initially, the dataset is normalized in order to make the attribute's range at a certain level. Further, weighted feature extraction is performed, in which a weight function is multiplied with each attribute value for making large scale deviation. Here, the weight function is optimized using the combination of two meta-heuristic algorithms termed as Jaya Algorithm-based Multi-Verse Optimization algorithm (JA-MVO). The optimally extracted features are subjected to the hybrid deep learning algorithms like “Deep Belief Network (DBN) and Recurrent Neural Network (RNN)”. As a modification to hybrid deep learning architecture, the weight of both DBN and RNN is optimized using the same hybrid optimization algorithm. Further, the comparative evaluation of the proposed prediction over the existing models certifies its effectiveness through various performance measures.

scholarly journals Cyberstalking Victimization Model Using Criminological Theory: A Systematic Literature Review, Taxonomies, Applications, Tools, and Validations

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1670
Author(s):  
Waheeb Abu-Ulbeh ◽  
Maryam Altalhi ◽  
Laith Abualigah ◽  
Abdulwahab Ali Almazroi ◽  
Putra Sumari ◽  
...  
Keyword(s):  
Data Analysis ◽  
Structural Equation ◽  
Large Scale ◽  
Review Paper ◽  
Essential Element ◽  
Routine Activities ◽  
Criminological Theory ◽  
Equation Modeling ◽  
Future Research ◽  
Proposed Model

Cyberstalking is a growing anti-social problem being transformed on a large scale and in various forms. Cyberstalking detection has become increasingly popular in recent years and has technically been investigated by many researchers. However, cyberstalking victimization, an essential part of cyberstalking, has empirically received less attention from the paper community. This paper attempts to address this gap and develop a model to understand and estimate the prevalence of cyberstalking victimization. The model of this paper is produced using routine activities and lifestyle exposure theories and includes eight hypotheses. The data of this paper is collected from the 757 respondents in Jordanian universities. This review paper utilizes a quantitative approach and uses structural equation modeling for data analysis. The results revealed a modest prevalence range is more dependent on the cyberstalking type. The results also indicated that proximity to motivated offenders, suitable targets, and digital guardians significantly influences cyberstalking victimization. The outcome from moderation hypothesis testing demonstrated that age and residence have a significant effect on cyberstalking victimization. The proposed model is an essential element for assessing cyberstalking victimization among societies, which provides a valuable understanding of the prevalence of cyberstalking victimization. This can assist the researchers and practitioners for future research in the context of cyberstalking victimization.

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