Search results for: intelligent tuning

Authors: Virender Ranga, Mayank Dave, Anil Kumar Verma

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Recently wireless sensor networks (WSNs) are used in many real life applications like environmental monitoring, habitat monitoring, health monitoring etc. Due to power constraint cheaper devices used in these applications, the energy consumption of each device should be kept as low as possible such that network operates for longer period of time. One of the techniques to prolong the network lifetime is an intelligent grouping of sensor nodes such that they can perform their operation in cooperative and energy efficient manner. With this motivation, we propose a novel approach by organize the sensor nodes in cooperative multihop mini-groups so that the total global energy consumption of the network can be reduced and network lifetime can be improved. Our proposed approach also reduces the number of transmitted messages inside the WSNs, which further minimizes the energy consumption of the whole network. The experimental simulations show that our proposed approach outperforms over the state-of-the-art approach in terms of stability period and aggregated data.

Keywords: clustering, cluster-head, mini-group, stability period

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Authors: Komal Babbar

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Multiuser Detection is the intelligent estimation/demodulation of transmitted bits in the presence of Multiple Access Interference. The authors have presented the Bit-error rate (BER) achieved by linear multi-user detectors: Matched filter (which treats the MAI as AWGN), Decorrelating and MMSE. In this work, authors investigate the bit error probability analysis for Matched filter, decorrelating, and MMSE. This problem arises in several practical CDMA applications where the receiver may not have full knowledge of the number of active users and their signature sequences. In particular, the behavior of MAI at the output of the Multi-user detectors (MUD) is examined under various asymptotic conditions including large signal to noise ratio; large near-far ratios; and a large number of users. In the last section Authors also shows Matlab Simulation results for Multiuser detection techniques i.e., Matched filter, Decorrelating, MMSE for 2 users and 10 users.

Keywords: code division multiple access, decorrelating, matched filter, minimum mean square detection (MMSE) detection, multiple access interference (MAI), multiuser detection (MUD)

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Authors: Edwin Lerch, Dirk Audring, Cuong Nguyen Mau, Duc Ninh Nguyen, The Cuong Nguyen, The Van Nguyen

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The National Load Dispatch Centre of Electricity Vietnam (EVNNLDC) and Siemens PTI investigated the stability of the electrical 500/220 kV transportation system of Vietnam. The general scope of the investigations is improving the stability of the Vietnam power system and giving the EVNNLDC staff the capability to decide how to deal with expected stability challenges in the future, which are related to the very fast growth of the system. Rapid system growth leads to a very high demand of power transmission from North to South. This was investigated by stability studies of interconnected power system with neighboring countries. These investigations are performed in close cooperation and coordination with the EVNNLDC project team. This important project includes data collection, measurement, model validation and investigation of relevant stability phenomena as well as training of the EVNNLDC staff. Generally, the power system of Vietnam has good voltage and dynamic stability. The main problems are related to the longitudinal system with more power generation in the North and Center, especially hydro power, and load centers in the South of Vietnam. Faults on the power transmission system from North to South risks the stability of the entire system due to a high power transfer from North to South and high loading of the 500 kV backbone. An additional problem is the weak connection to Cambodia power system which leads to interarea oscillations mode. Therefore, strengthening the power transfer capability by new 500kV lines or HVDC connection and balancing the power generation across the country will solve many challenges. Other countermeasures, such as wide area load shedding, PSS tuning and correct SVC placement will improve and stabilize the power system as well. Primary frequency reserve should be increased.

Keywords: dynamic power transmission system studies, blackout prevention, power system interconnection, stability

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Authors: Tao Ding, Danjia Yan, Jinye Li, Chao Ren, Xinhua Hu

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The paper based on the research background of the current situation of water shortage in China and intelligent management of water resources in the information era. And the paper adopts WebGIS technology, combining the mathematical model of water resources management to develop a WebGIS-based water environment capacity inquiry and polluted water emission planning. The research significance of the paper is that it can inquiry the water environment capacity of Jinhua City in real time and plan how to drain polluted water into the river, so as to realize the effective management of water resources. This system makes sewage planning more convenient and faster. For the planning of the discharge enterprise, the decision on the optimal location of the sewage outlet can be achieved through calculation of the Sewage discharge planning model in the river, without the need for site visits. The system can achieve effective management of water resources and has great application value.

Keywords: sewerage planning, water environment capacity, water resources management, WebGIS

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Authors: Kun Qi, Yuman Zhou, Jianxin He

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A facile fabrication strategy via electrospinning and followed by in situ polymerization to fabricate a highly stretchable and conductive Poly(3,4-ethylenedioxythiophene)/Polyurethane (PEDOT/PU) nanofibrous membrane is reported. PU nanofibers were prepared by electrospinning and then PEDOT was coated on the plasma modified PU nanofiber surface via in-situ polymerization to form flexible PEDOT/PU composite nanofibers with conductivity. The results show PEDOT is successfully synthesized on the surface of PU nanofiber and PEDOT/PU composite nanofibers possess skin-core structure. Furthermore, the experiments indicate the optimal technological parameters of the polymerization process are as follow: The concentration of EDOT monomers is 50 mmol/L, the polymerization time is 24 h and the temperature is 25℃. The PEDOT/PU nanofibers exhibit excellent electrical conductivity ( 27.4 S/cm). In addition, flexible sensor made from conductive PEDOT/PU nanofibers shows highly sensitive response towards tensile strain and also can be used to detect finger motion. The results demonstrate promising application of the as-obtained nanofibrous membrane in flexible wearable electronic fields.

Keywords: electrospinning, polyurethane, PEDOT, conductive nanofiber, flexible senor

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Authors: C. K. Ngaw

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Crystal facet engineering, which involves tuning and controlling a crystal surface and morphology, is a commonly employed strategy to optimize the performance of crystalline nanocrystals. The principle behind this strategy is that surface atomic rearrangement and coordination, which inherently determines their catalytic activity, can be easily tuned by morphological control. Because of this, the catalytic properties of a nanocrystal are closely related to the surface of an exposed facet, and it has provided great motivation for researchers to synthesize photocatalysts with high catalytic activity by maximizing reactive facets exposed through morphological control. In this contribution, octahedral NaInS₂ crystals have been successfully developed via solvothermal method. The formation of the octahedral NaInS₂ crystals was investigated using field emission scanning electron microscope (FESEM) and X-Ray diffraction (XRD), and results have shown that the concentration of sulphur precursor plays an important role in the growth process, leading to the formation of other NaInS₂ crystal structures in the form of hexagonal nanosheets and microspheres. Structural modeling analysis suggests that the octahedral NaInS₂ crystals were enclosed with {012} and {001} facets, while the nanosheets and microspheres are bounded with {001} facets only and without any specific facets, respectively. Visible-light photocatalytic H₂ evolution results revealed that the octahedral NaInS₂ crystals (~67 μmol/g/hr) exhibit ~6.1 and ~2.3 times enhancement as compared to the conventional NaInS₂ microspheres (~11 μmol/g/hr) and nanosheets (~29 μmol/g/hr), respectively. The H₂ enhancement of the NaInS₂ octahedral crystal is attributed to the presence of {012} facets on the surface. Detailed analysis of the octahedron model revealed obvious differences in the atomic arrangement between the {001} and {012} facets and this can affect the interaction between the water molecules and the surface facets before reducing into H₂ gas. These results highlight the importance of tailoring crystal morphology with highly reactive facets in improving photocatalytic properties.

Keywords: H₂ evolution, photocatalysis, octahedral, reactive facets

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Authors: N. Drir, L. Barazane, M. Loudini

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It is well known that photovoltaic (PV) cells are an attractive source of energy. Abundant and ubiquitous, this source is one of the important renewable energy sources that have been increasing worldwide year by year. However, in the V-P characteristic curve of GPV, there is a maximum point called the maximum power point (MPP) which depends closely on the variation of atmospheric conditions and the rotation of the earth. In fact, such characteristics outputs are nonlinear and change with variations of temperature and irradiation, so we need a controller named maximum power point tracker MPPT to extract the maximum power at the terminals of photovoltaic generator. In this context, the authors propose here to study the modeling of a photovoltaic system and to find an appropriate method for optimizing the operation of the PV generator using two intelligent controllers respectively to track this point. The first one is based on artificial neural networks and the second on fuzzy logic. After the conception and the integration of each controller in the global process, the performances are examined and compared through a series of simulation. These two controller have prove by their results good tracking of the MPPT compare with the other method which are proposed up to now.

Keywords: maximum power point tracking, neural networks, photovoltaic, P&O

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Authors: Aliasghar Arab

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Autonomous robots interacting with humans, as safety-critical nonlinear control systems, are complex closed-loop cyber-physical dynamical machines. Keeping these intelligent yet complicated systems safe and smooth during their operations is challenging. The aim of the safe predictive output feedback linearization control synthesis is to design a novel controller for smooth trajectory following while unsafe situations must be avoided. The controller design should obtain a linearized output for smoothness and invariance to a safety subset. Inspired by finite-horizon nonlinear model predictive control, the problem is formulated as constrained nonlinear dynamic programming. The safety constraints can be defined as control barrier functions. Avoiding unsafe maneuvers and performing smooth motions increases the predictability of the robot’s movement for humans when robots and people are working together. Our results demonstrate the proposed output linearization method obeys the safety constraints and, compared to existing safety-guaranteed methods, is smoother and performs better.

Keywords: robotics, collaborative robots, safety, autonomous robots

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Authors: Nada Slimane, Foued Theljani, Faouzi Bouani

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The paper addresses the problem of fault diagnosis for systems operating in several modes (normal or faulty) based on states assessment. We use, for this purpose, a methodology consisting of three main processes: 1) sequential data clustering, 2) linear model regression and 3) state filtering. Typically, Kalman Filter (KF) is an algorithm that provides estimation of unknown states using a sequence of I/O measurements. Inevitably, although it is an efficient technique for state estimation, it presents two main weaknesses. First, it merely predicts states without being able to isolate/classify them according to their different operating modes, whether normal or faulty modes. To deal with this dilemma, the KF is endowed with an extra clustering step based fully on sequential version of the k-means algorithm. Second, to provide state estimation, KF requires state space models, which can be unknown. A linear regularized regression is used to identify the required models. To prove its effectiveness, the proposed approach is assessed on a simulated benchmark.

Keywords: clustering, diagnosis, Kalman Filtering, k-means, regularized regression

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Authors: Shayan Mohajer Hamidi

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Federated Learning (FL) has emerged as a promising paradigm for training machine learning models on distributed data without the need for centralized data aggregation. In the realm of wireless communications, FL has the potential to leverage the vast amounts of data generated by wireless devices to improve model performance and enable intelligent applications. However, the fairness aspect of FL in wireless communications remains largely unexplored. This abstract presents an idea for fair federated learning in wireless communications, addressing the challenges of imbalanced data distribution, privacy preservation, and resource allocation. Firstly, the proposed approach aims to tackle the issue of imbalanced data distribution in wireless networks. In typical FL scenarios, the distribution of data across wireless devices can be highly skewed, resulting in unfair model updates. To address this, we propose a weighted aggregation strategy that assigns higher importance to devices with fewer samples during the aggregation process. By incorporating fairness-aware weighting mechanisms, the proposed approach ensures that each participating device's contribution is proportional to its data distribution, thereby mitigating the impact of data imbalance on model performance. Secondly, privacy preservation is a critical concern in federated learning, especially in wireless communications where sensitive user data is involved. The proposed approach incorporates privacy-enhancing techniques, such as differential privacy, to protect user privacy during the model training process. By adding carefully calibrated noise to the gradient updates, the proposed approach ensures that the privacy of individual devices is preserved without compromising the overall model accuracy. Moreover, the approach considers the heterogeneity of devices in terms of computational capabilities and energy constraints, allowing devices to adaptively adjust the level of privacy preservation to strike a balance between privacy and utility. Thirdly, efficient resource allocation is crucial for federated learning in wireless communications, as devices operate under limited bandwidth, energy, and computational resources. The proposed approach leverages optimization techniques to allocate resources effectively among the participating devices, considering factors such as data quality, network conditions, and device capabilities. By intelligently distributing the computational load, communication bandwidth, and energy consumption, the proposed approach minimizes resource wastage and ensures a fair and efficient FL process in wireless networks. To evaluate the performance of the proposed fair federated learning approach, extensive simulations and experiments will be conducted. The experiments will involve a diverse set of wireless devices, ranging from smartphones to Internet of Things (IoT) devices, operating in various scenarios with different data distributions and network conditions. The evaluation metrics will include model accuracy, fairness measures, privacy preservation, and resource utilization. The expected outcomes of this research include improved model performance, fair allocation of resources, enhanced privacy preservation, and a better understanding of the challenges and solutions for fair federated learning in wireless communications. The proposed approach has the potential to revolutionize wireless communication systems by enabling intelligent applications while addressing fairness concerns and preserving user privacy.

Keywords: federated learning, wireless communications, fairness, imbalanced data, privacy preservation, resource allocation, differential privacy, optimization

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Authors: Prajamitra Bhuyan

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Transport operators have a range of intervention options available to improve or enhance their networks. But often such interventions are made in the absence of sound evidence on what outcomes may result. Cycling superhighways were promoted as a sustainable and healthy travel mode which aims to cut traffic congestion. The estimation of the impacts of the cycle superhighways on congestion is complicated due to the non-random assignment of such intervention over the transport network. In this paper, we analyse the causal effect of cycle superhighways utilising pre-innervation and post-intervention information on traffic and road characteristics along with socio-economic factors. We propose a modeling framework based on the propensity score and outcome regression model. The method is also extended to doubly robust set-up. Simulation results show the superiority of the performance of the proposed method over existing competitors. The method is applied to analyse a real dataset on the London transport network, and the result would help effective decision making to improve network performance.

Keywords: average treatment effect, confounder, difference-in-difference, intelligent transportation system, potential outcome

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Authors: Tomas Premoli, Sareh Rowlands

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In this study, we investigate the impact of various convolutional neural network (CNN) architectures on the accuracy of diagnosing Alzheimer’s disease (AD) using patient MRI scans. Alzheimer’s disease is a debilitating neurodegenerative disorder that affects millions worldwide. Early, accurate, and non-invasive diagnostic methods are required for providing optimal care and symptom management. Deep learning techniques, particularly CNNs, have shown great promise in enhancing this diagnostic process. We aim to contribute to the ongoing research in this field by comparing the effectiveness of different CNN architectures and providing insights for future studies. Our methodology involved preprocessing MRI data, implementing multiple CNN architectures, and evaluating the performance of each model. We employed intensity normalization, linear registration, and skull stripping for our preprocessing. The selected architectures included VGG, ResNet, and DenseNet models, all implemented using the Keras library. We employed transfer learning and trained models from scratch to compare their effectiveness. Our findings demonstrated significant differences in performance among the tested architectures, with DenseNet201 achieving the highest accuracy of 86.4%. Transfer learning proved to be helpful in improving model performance. We also identified potential areas for future research, such as experimenting with other architectures, optimizing hyperparameters, and employing fine-tuning strategies. By providing a comprehensive analysis of the selected CNN architectures, we offer a solid foundation for future research in Alzheimer’s disease diagnosis using deep learning techniques. Our study highlights the potential of CNNs as a valuable diagnostic tool and emphasizes the importance of ongoing research to develop more accurate and effective models.

Keywords: Alzheimer’s disease, convolutional neural networks, deep learning, medical imaging, MRI

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Authors: C. Caudwell, R. Syed, C. Lacey

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While the development and advancement of information communication technologies (ICTs) offers powerful opportunities for meaningful connections and relationships, shame is a significant barrier to social and cultural acceptance. In particular, artificial intelligence and socially oriented robots are increasingly becoming partners in romantic relationships with people, offering bonding, support, comfort, growth, and reciprocity. However, these relationships suffer hierarchical, anthropocentric shame that is a significant barrier to their success and longevity. This paper will present case studies of human and artificially intelligent agent relationships, in the context of internal and external shame, as cultivated, propagated, and communicated through ICT. Using an interdisciplinary methodology we aim to present a framework for technological shame, building on the experimental and emergent psychoanalytical theories of emotions. Our study finds principally that socialization is a powerful factor in the vectors of shame as experienced by humans. On a wider scale, we contribute understanding of social emotion and the phenomenon of shame proliferated through ICTs, which is at present under-explored, but vital, as society and culture is increasingly mediated through this medium.

Keywords: shame, artificial intelligence, romance, society

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Authors: Pius Agbebi Alaba

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The study emphasized on the effects of contemporary and prospect trends on the development of Hospitality and Tourism in Nigeria. Specifically, the study examined globalization, safety and security, diversity, service, technology, demographic changes and price–value as contemporary trends while prospect trends such as green and Eco-lodgings, Development of mega hotels, Boutique hotels, Intelligent hotels with advanced technology using the guest’s virtual fingerprint in order to perform all the operations, increasing employee salaries in order retain the existing Staff, More emphasis on the internet and technology, Guests’ virtual and physical social network were equally examined. The methodology for the study involved review of existing related study, books, journal and internet. The findings emanated from the exercise showed clearly that the impact of both trends on the development of Hospitality and Tourism in Nigeria would bring about rapid positive transformation of her socio-economic, political and cultural environment. The implication of the study is that it will prepare both private and corporate individuals in hospitality and tourism business for the challenges inherent in both trends.

Keywords: hospitality and tourism, Nigeria's centenary, trends, implications

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Authors: Manverpreet Kaur, Amarpreet Singh

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The demand of Vehicular ad hoc networks is increasing day by day, due to offering the various applications and marvelous benefits to VANET users. Clustering in VANETs is most important to overcome the connectivity problems of VANETs. In this paper, we proposed a new clustering technique Enhanced cluster based connectivity maintenance in vehicular ad hoc network. Our objective is to form long living clusters. The proposed approach is grouping the vehicles, on the basis of the longest list of neighbors to form clusters. The cluster formation and cluster head selection process done by the RSU that may results it reduces the chances of overhead on to the network. The cluster head selection procedure is the vehicle which has closest speed to average speed will elect as a cluster Head by the RSU and if two vehicles have same speed which is closest to average speed then they will be calculate by one of the new parameter i.e. distance to their respective destination. The vehicle which has largest distance to their destination will be choosing as a cluster Head by the RSU. Our simulation outcomes show that our technique performs better than the existing technique.

Keywords: VANETs, clustering, connectivity, cluster head, intelligent transportation system (ITS)

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Authors: M. Ramadan, B. Salah

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This paper introduces a framework to digitalize lean manufacturing tools to enhance smart lean-based manufacturing environments or Lean 4.0 manufacturing systems. The paper discusses the integration between lean tools and the powerful features of recent real-time data capturing systems with the help of Information and Communication Technologies (ICT) to develop an intelligent real-time monitoring and controlling system of production operations concerning lean targets. This integration is represented in the Lean 4.0 system called Dynamic Value Stream Mapping (DVSM). Moreover, the paper introduces the practice of Radio Frequency Identification (RFID) and ICT to smartly support lean tools and practices during daily production runs to keep the lean system alive and effective. This work introduces a practical description of how the lean method tools 5S, standardized work, and poka-yoke can be digitalized and smartly monitored and controlled through DVSM. A framework of the three tools has been discussed and put into practice in a German switchgear manufacturer.

Keywords: lean manufacturing, Industry 4.0, radio frequency identification, value stream mapping

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Authors: Yas Barzegaar, Atrin Barzegar

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The aim of this research is to develop an intelligent system to analyze the risk level of wheat flour product manufacturing system. The model consists of five Fuzzy Inference Systems in two different layers to analyse the risk of a wheat flour product manufacturing system. The first layer of the model consists of four Fuzzy Inference Systems with three criteria. The output of each one of the Physical, Chemical, Biological and Environmental Failures will be the input of the final manufacturing systems. The proposed model based on Mamdani Fuzzy Inference Systems gives a performance ranking of wheat flour products manufacturing systems. The first step is obtaining data to identify the failure modes from expert’s opinions. The second step is the fuzzification process to convert crisp input to a fuzzy set., then the IF-then fuzzy rule applied through inference engine, and in the final step, the defuzzification process is applied to convert the fuzzy output into real numbers.

Keywords: failure modes, fuzzy rules, fuzzy inference system, risk assessment

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Authors: Emad Amar, Tawfik Khattab, Fatma Zada

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Predicting earthquakes is an important issue in the study of geography. Accurate prediction of earthquakes can help people to take effective measures to minimize the loss of personal and economic damage, such as large casualties, destruction of buildings and broken of traffic, occurred within a few seconds. United States Geological Survey (USGS) science organization provides reliable scientific information of Earthquake Existed throughout history & Preliminary database from the National Center Earthquake Information (NEIC) show some useful factors to predict an earthquake in a seismic area like Aleutian Arc in the U.S. state of Alaska. The main advantage of this prediction method that it does not require any assumption, it makes prediction according to the future evolution of object's time series. The article compares between simulation data result from trained BP and RBF neural network versus actual output result from the system calculations. Therefore, this article focuses on analysis of data relating to real earthquakes. Evaluation results show better accuracy and higher speed by using radial basis functions (RBF) neural network.

Keywords: BP neural network, prediction, RBF neural network, earthquake

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Authors: Qiang Zhang, Xiaojian Hu

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Aiming at the problem of the existing real-time evaluation methods for traffic safety status, a video information-based urban road section traffic safety status evaluation method was established, and the rapid detection method of traffic flow parameters based on video information is analyzed. The concept of the speed dispersion of the road section that affects the traffic safety state of the urban road section is proposed, and the method of evaluating the traffic safety state of the urban road section based on the speed dispersion of the road section is established. Experiments show that the proposed method can reasonably evaluate the safety status of urban roads in real-time, and the evaluation results can provide a corresponding basis for the traffic management department to formulate an effective urban road section traffic safety improvement plan.

Keywords: intelligent transportation system, road traffic safety, video information, vehicle speed dispersion

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Authors: Kenneth Fon Mbah, Arash Habibi Lashkari, Ali A. Ghorbani

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Phishing e-mails are a security issue that not only annoys online users, but has also resulted in significant financial losses for businesses. Phishing advertisements and pornographic e-mails are difficult to detect as attackers have been becoming increasingly intelligent and professional. Attackers track users and adjust their attacks based on users’ attractions and hot topics that can be extracted from community news and journals. This research focuses on deceptive Phishing attacks and their variants such as attacks through advertisements and pornographic e-mails. We propose a framework called Phishing Alerting System (PHAS) to accurately classify e-mails as Phishing, advertisements or as pornographic. PHAS has the ability to detect and alert users for all types of deceptive e-mails to help users in decision making. A well-known email dataset has been used for these experiments and based on previously extracted features, 93.11% detection accuracy is obtainable by using J48 and KNN machine learning techniques. Our proposed framework achieved approximately the same accuracy as the benchmark while using this dataset.

Keywords: phishing e-mail, phishing detection, anti phishing, alarm system, machine learning

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Authors: Jonathan Nii Laryea Ashong, Samuel Opare

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Current sustainable cities debate focuses on the formidable problems for the Ghana’s largest urban and rural agglomerations, the majority of all urban dwellers continue to reside in far smaller urban settlements. It is estimated that by year 2030, almost all the Ghana’s population growth will likely be intense in urban areas including Nkawkaw in the Kwahu West Municipality of Ghana. Nkawkaw is situated on the road and former railway between Accra and Kumasi, and lies about halfway between these cities. It is also connected by road to Koforidua and Konongo. According to the 2013 census, Nkawkaw has a settlement population of 61,785. Many international agencies, government and private architectures’ are been asked to adequately recognize the naming of streets and property addressing system among the 170 districts across Ghana. The naming of streets and numbering of properties is to assist Metropolitan, Municipal and District Assemblies to manage the processes for establishing coherent address system nationally. Street addressing in the Nkawkaw in the Kwahu West Municipality which makes it possible to identify the location of a parcel of land, public places or dwellings on the ground based on system of names and numbers, yet agreement on how to progress towards it remains elusive. Therefore, reliable and effective development control for proper street naming and property addressing systems are required. The Intelligent Addressing (IA) technology from the UK is being used to name streets and properties in Ghana. The intelligent addressing employs the technique of unique property Reference Number and the unique street reference number which would transform national security and other service providers’ ability to respond rapidly to distress calls. Where name change is warranted following the review of existing streets names, the Physical Planning Department (PPDs) shall, in consultation with the relevant traditional authorities and community leadership (or relevant major stakeholders), select a street name in accordance with the provisions of the policy and the processes outlined for street name change for new development. In the case of existing streets with no names, the respective PPDs shall, in consultation with the relevant traditional authorities and community leadership (or relevant major stakeholders), select a street name in accordance with the requirements set out in municipality. Naming of access ways proposed for new developments shall be done at the time of developing sector layouts (subdivision maps) for the designated areas. In the case of private gated developments, the developer shall submit the names of the access ways as part of the plan and other documentation forwarded to the Municipal District Assembly for approval. The names shall be reviewed first by the PPD to avoid duplication and to ensure conformity to the required standards before submission to the Assembly’s Statutory Planning Committee for approval. The Kwahu West Municipality is supposed to be self-sustaining, providing basic services to inhabitants as a result of proper planning layouts, street naming and property addressing system that prevail in the area. The implications of these future projections are discussed.

Keywords: Nkawkaw, Kwahu west municipality, street naming, property, addressing system

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Authors: Mikiyas Adugna

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Effort estimation is among the activities carried out in software development processes. An accurate model of estimation leads to project success. The method of agile effort estimation is a complex task because of the dynamic nature of software development. Researchers are still conducting studies on agile effort estimation to enhance prediction accuracy. Due to these reasons, we investigated and proposed a model on LASSO and Elastic Net regression to enhance estimation accuracy. The proposed model has major components: preprocessing, train-test split, training with default parameters, and cross-validation. During the preprocessing phase, the entire dataset is normalized. After normalization, a train-test split is performed on the dataset, setting training at 80% and testing set to 20%. We chose two different phases for training the two algorithms (Elastic Net and LASSO) regression following the train-test-split. In the first phase, the two algorithms are trained using their default parameters and evaluated on the testing data. In the second phase, the grid search technique (the grid is used to search for tuning and select optimum parameters) and 5-fold cross-validation to get the final trained model. Finally, the final trained model is evaluated using the testing set. The experimental work is applied to the agile story point dataset of 21 software projects collected from six firms. The results show that both Elastic Net and LASSO regression outperformed the compared ones. Compared to the proposed algorithms, LASSO regression achieved better predictive performance and has acquired PRED (8%) and PRED (25%) results of 100.0, MMRE of 0.0491, MMER of 0.0551, MdMRE of 0.0593, MdMER of 0.063, and MSE of 0.0007. The result implies LASSO regression algorithm trained model is the most acceptable, and higher estimation performance exists in the literature.

Keywords: agile software development, effort estimation, elastic net regression, LASSO

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Authors: W. Hashim, H. Burok, N. Ghazaly, H. Ahmad Nasir, N. Mohamad Anas, A. F. Ismail, K. L. Yau

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Future communication networks require devices that are able to work on a single platform but support heterogeneous operations which lead to service diversity and functional flexibility. This paper proposes two cognitive mechanisms termed cognitive hybrid function which is applied in multiple broadband user terminals in order to maintain reliable connectivity and preventing unnecessary interferences. By employing such mechanisms especially for future hyper-dense network, we can observe their performances in terms of optimized speed and power saving efficiency. Results were obtained from several empirical laboratory studies. It was found that selecting reliable network had shown a better optimized speed performance up to 37% improvement as compared without such function. In terms of power adjustment, our evaluation of this mechanism can reduce the power to 5dB while maintaining the same level of throughput at higher power performance. We also discuss the issues impacting future telecommunication standards whenever such devices get in place.

Keywords: dense network, intelligent network selection, multiple networks, transmit power adjustment

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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

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Authors: Frederic Jumelle, Kelvin So, Didan Deng

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In this paper, we are introducing a model of artificial general intelligence (AGI), the functional neural network (FNN), for modeling human decision-making processes. The FNN is composed of multiple artificial mirror neurons (AMN) racing in the network. Each AMN has a similar structure programmed independently by the users and composed of an intention wheel, a motor core, and a sensory core racing at a specific velocity. The mathematics of the node’s formulation and the racing mechanism of multiple nodes in the network will be discussed, and the group decision process with fuzzy logic and the transformation of these conceptual methods into practical methods of simulation and in operations will be developed. Eventually, we will describe some possible future research directions in the fields of finance, education, and medicine, including the opportunity to design an intelligent learning agent with application in AGI. We believe that FNN has a promising potential to transform the way we can compute decision-making and lead to a new generation of AI chips for seamless human-machine interactions (HMI).

Keywords: neural computing, human machine interation, artificial general intelligence, decision processing

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Authors: F. H. Abanda, B. Kamsu-Foguem, J. H. M. Tah

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Construction cost estimation is one of the most important aspects of construction project design. For generations, the process of cost estimating has been manual, time-consuming and error-prone. This has partly led to most cost estimates to be unclear and riddled with inaccuracies that at times lead to over- or under-estimation of construction cost. The development of standard set of measurement rules that are understandable by all those involved in a construction project, have not totally solved the challenges. Emerging Building Information Modelling (BIM) technologies can exploit standard measurement methods to automate cost estimation process and improves accuracies. This requires standard measurement methods to be structured in ontologically and machine readable format; so that BIM software packages can easily read them. Most standard measurement methods are still text-based in textbooks and require manual editing into tables or Spreadsheet during cost estimation. The aim of this study is to explore the development of an ontology based on New Rules of Measurement (NRM) commonly used in the UK for cost estimation. The methodology adopted is Methontology, one of the most widely used ontology engineering methodologies. The challenges in this exploratory study are also reported and recommendations for future studies proposed.

Keywords: BIM, construction projects, cost estimation, NRM, ontology

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Authors: Yansong Zhang, Xueyuan Li, Junjie Zhou, Xufeng Yin, Shihua Yuan, Shuxian Liu

Abstract:

Aiming at the verification of control algorithms for skid-steering vehicles, a vehicle simulation model of 6×6 electric skid-steering unmanned vehicle was established based on Trucksim and Simulink. The original transmission and steering mechanism of Trucksim are removed, and the electric skid-steering model and a closed-loop controller for the vehicle speed and yaw rate are built in Simulink. The simulation results are compared with the ones got by theoretical formulas. The results show that the predicted tire mechanics and vehicle kinematics of Trucksim-Simulink simulation model are closed to the theoretical results. Therefore, it can be used as an effective approach to study the dynamic performance and control algorithm of skid-steering vehicle. In this paper, a method of motion control based on feed forward control is also designed. The simulation results show that the feed forward control strategy can make the vehicle follow the target yaw rate more quickly and accurately, which makes the vehicle have more maneuverability.

Keywords: skid-steering, Trucksim-Simulink, feedforward control, dynamics

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Authors: Albatool Al Abdulwahid, Bayan Shakally, Mariam Mohamed, Wed Almokri

Abstract:

Artificial intelligence has infiltrated every part of our life and every field we can think of. With technical developments, artificial intelligence applications are becoming more prevalent. We chose ELSA speak because it is a magnificent example of Artificial intelligent applications, ELSA speak is a smartphone application that is free to download on both IOS and Android smartphones. ELSA speak utilizes artificial intelligence to help non-native English speakers pronounce words and phrases similar to a native speaker, as well as enhance their English skills. It employs speech-recognition technology that aids the application to excel the pronunciation of its users. This remarkable feature distinguishes ELSA from other voice recognition algorithms and increase the efficiency of the application. This study focused on evaluating ELSA speak application, by testing the degree of effectiveness based on survey questions. The results of the questionnaire were variable. The generality of the participants strongly agreed that ELSA has helped them enhance their pronunciation skills. However, a few participants were unconfident about the application’s ability to assist them in their learning journey.

Keywords: ELSA speak application, artificial intelligence, speech-recognition technology, language learning, english pronunciation

Procedia PDF Downloads 106

Authors: Arindam Roy, Madhushree Das, Apurba Manna, Samir Maity

Abstract:

In this research paper, an algorithmic approach is developed to solve a novel multi-objective four-dimensional traveling salesman problem (MO4DTSP) where different paths with various numbers of conveyances are available to travel between two cities. NSGA-II and Decomposition algorithms are modified to solve MO4DTSP in an IoT-based transport system. This IoT-based transport system can be widely observed, analyzed, and controlled by an extensive distribution of traffic networks consisting of various types of sensors and actuators. Due to urbanization, most of the cities are connected using an intelligent traffic management system. Practically, for a traveler, multiple routes and vehicles are available to travel between any two cities. Thus, the classical TSP is reformulated as multi-route and multi-vehicle i.e., 4DTSP. The proposed MO4DTSP is designed with traveling cost, time, and customer satisfaction as objectives. In reality, customer satisfaction is an important parameter that depends on travel costs and time reflects in the present model.

Keywords: multi-objective four-dimensional traveling salesman problem (MO4DTSP), decomposition, NSGA-II, IoT-based transport system, customer satisfaction

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Authors: Sun Zhe, Ruggero Micheletto

Abstract:

The research of neural network is very important for the development of advanced next generation intelligent devices and the medical treatment. The most important part of the neural network research is the learning. The process of learning in our brain is essentially several adjustment processes of connection strength between neurons. It is very difficult to figure out how this mechanism works in the complex network and how the connection strength influences brain functions. For this reason, we made a model with only two coupled neurons and studied the influence of connection strength between them. To emulate the neuronal activity of realistic neurons, we prefer to use the Izhikevich neuron model. This model can simulate the neuron variables accurately and it’s simplicity is very suitable to implement on computers. In this research, the parameter ρ is used to estimate the correlation coefficient between spike train of two coupling neurons.We think the results is very important for figuring out the mechanism between synchronization of coupling neurons and synaptic plasticity. The result also presented the importance of the spike frequency adaptation in complex systems.

Keywords: neural networks, noise, stochastic processes, coupled neurons, correlation coefficient, synchronization, pacemaker, synaptic plasticity

Procedia PDF Downloads 284