The uses of Telecoms and IT technology change the daily life of million Africans. Our researches focus on tracking, monitoring and safety of the small traditional pirogue used by fisherman, alongside with allowing better ocean resources management. Using mobile technology for data transfer network and low-cost embedded device; we propose a solution model for developing the efficiency of the sea activities, optimizing the distribution of natural resource, and increasing security.
: Association rules are a data mining technique used to discover frequent patterns in a data set. In this work, association rules are used in the medical domain, where data sets are generally high dimensional. The chief disadvantage of mining association rules in a high dimensional data set is the huge number of patterns that are discovered, most of which are irrelevant or redundant. This disadvantage is grown when Big data is used. The multidimensional view of the data is well used for data visualization and analysis tasks, but due to the hypercube dissipation, the amount of data, in this case, is greater than the relational representation that is not acceptable to the Big Data. Object representation allows you to store an object in the form of attributes, their characteristics, and relationships between characteristics. For some modification, it can be used for Big Data. In medical and biological research, as well as in practical medicine, the range of tasks to be solved is so wide that it is possible to use any of the methodologies of Data Mining. An example can be the construction of a diagnostic system or the study of the effectiveness of a surgical intervention.
The paper presents the architecture of the platform for Big data preprocessing and processing. The method for prevention of the risk disease based on Probabilistic Production Dependencies is developed. The investigation is started from the platform for Big data in medical domain analysis. The platform consists of 6 layers: Data layer, Communication layers, Preprocessing layer, Data processing layer, User layer, Integrational layer. The platform architecture for Big data processing in medical sector is developed. The accuracy of the proposed method is estimated.
Nowadays the central region of Mali is in a total insecurity. Malian army is not able to protect local population. The uses of Telecoms and IT technology can change the daily life of these populations. Each village is not able to prevent threat alone. Neighboring villages can together resist the attacks of bad guys. Because of barriers such as cost and skills/knowledge in ICT, operator’s mobile technologies are not adapted for these populations. The most of villages are connected to operator’s network. The average rate of mobile device equipment is correct. Our study focuses on the provision of low-cost telecommunications services from access points running on OpenWrt. OpenWrt’s ability to transform access points into Linux mini machines is used among other things to integrate IP telephony and VPN services. Thanks to the judicious choice of OpenVPN mode of operation we propose in this article to extend the functionalities of the Zeroconf protocol beyond a local network. This solution enables the reduction of communication costs and access to network services without configuration. Using a Wi-Fi extension of the operators' GSM networks in each village and low-cost devices we propose a solution for increasing security alerts between neighboring villages, developing the efficiency of the farmer’s activities and optimizing the distribution of resource.
This paper deals with the process of online branding of scientific medical conference. The use of scientific-metric databases and modern capabilities that provide social networks for communication between scientists is, in combination, an effective method for promoting scientific publications, events and scholars. The popularizations of scientific results, the publications in which these results are published, play an important role in shaping the brand of a modern successful scientist, which in turn is a component of the brand of higher education. Successful scientific activity of a scientist is impossible without the publication of the results of research in reputable international publications. The development of the concept of popularization of periodicals through scientometric international databases is a current and in-demand research. The results of the implementation of the stages of the proposed algorithmic complex are the creation of conference accounts and the formation rating and brabding the medical conference be online scientific services. The practical implementation of the proposed method has shown significant results in the popularization of the scientific publication. The economic expediency of online branding of scientific medical conference is proposed.
Telemedicine is a field that will be increasingly developed in African countries south of the Sahara. These countries are generally characterised by low health coverage and a lack of financial resources. The maturity of the optical transmission and access networks associated with the development of connected objects in the field of health suggests the achievement of the goal n°3 relating to health for all by 2030 defined by the United Nations Organization. Today, the telemedicine model adopted in the developed countries cannot meet the African context described above, as it requires fairly complex solutions not yet mastered by the Internet of Things and a content-centric network approach (CCN). In this paper, we propose an integrated optical transport and distribution solution based on Wave Length Multiplexing (WDM) and Passive Optical Networks (PON) technologies to deliver health services to rural centres from urban referral centres. We will use simulation to evaluate the performance of our proposal taking into account the requirements of telemedicine.
Since years ago and currently, the world has witnessed great development and interest in the fields of Machine learning, Deep learning, which provides solutions at all levels, especially in medical image analysis. These developments have a huge potential for medical imaging technology, medical data analysis, medical diagnostics and healthcare in general, slowly being realized. We provide a short overview of recent advances and some associated challenges in machine learning applied to medical image processing and image analysis. As this has become a very broad and fast expanding field we will not survey the entire landscape of applications, but put particular focus on deep learning in Magnetic Resonance Imaging (MRI). First, a brief introduction of deep learning and imaging modalities of MRI images is given. Then, common deep learning architectures are introduced. Next, deep learning applications of MRI images, such as image detection, image registration, image segmentation, and image classification are discussed. Subsequently, the deep learning tools in the applications of MRI images are presented. Finally; the limitation and future of Deep learning and a small conclusion.
The digitization of modern health care data in a rural community has produced a vast amount of patient data stored in health care record systems. Together with the rise of computing power this data could produce effective insight through advanced analysis of this data and include it in medical applications for use in daily operations. This is the case in which structured, semi-structured and unstructured dataset from emergency room admissions is used for machine learning, in order to develop models that predict the possibility of an elderly patient returning to an emergency room within 96 hours. Logistic regression was the selected algorithm since it commonly used in the healthcare data set. The results from the model had a recall of 73% and a precision of 78%. This paper discusses the implementation of such a model in daily operations with a new approach to cost benefits. In other instances, the study is a proof of the concept of predictive modeling in a health care context in rural communities.
The modernization has influenced people in these days in lifestyle and food habits are trying to defray from the healthier food which we have from an ancient culture, but are falling into indigent practices, and we have to find the reasons for the main causes of disease and how people are prone to illness. A variety of artificial neural network prototypes are examining in terms of their categorization effectiveness in a swine flu infection. The implementation results exhibit enhanced accuracy than conventional classification methods and are a reasonable and earlier diagnosis of swine flu. This method provides a suitable alternative of medical features for the identification of swine flu status. It also provides insight into the classification of individuals regarded as an “unidentified” phenotype on the origin of standard diagnosis methods. Scalability is the key challenge for large volumes of data, and we applied the Rainforest algorithm for improving the quality of classification.
In this paper, we address the problem of very long execution durations during the management activities in rural infrastructures and elderly community information systems. This problem is a major challenge in the NGNM field in terms of constructing ICT4D large-scale collaborative infrastructures for a rural community in developing countries. It is an optimization problem in the strong sense with resource constraints in the context of real-time reconfiguration systems. It is also related to the operability of distributed highavailability networks systems used in NGN communications networks in order to support development in all life areas such as Education, Health, Economy, Agriculture and even to ensure the survival of living beings in developing countries. The resolution of such problems requires a significant and simultaneous reduction of several performance temporal criteria. The reduction of the execution durations allows optimizing performances of network resources. From a practical point of view, the reduction of network resources consumption automatically decreases the overall energy consumption in the remote networks infrastructures in rural areas technologies such as WSN, RFID, NFC, IoT, LoRa, WiMax, AirMax, Wideband satellites access and VSAT. We propose thus an algorithm capable of determining and decreasing in realtime the execution durations for ICT4D rural infrastructures and rural community information systems. This algorithm allows guaranteeing data access, data integration, information sharing and rapid professionals assistance across multiple heterogeneous centers. This algorithm is based on minimal temporal criteria, lower and upper bounds, dominance rules, a branching scheme, and an exploration strategy. Our experiments show that the results of the proposed algorithm are more efficient than those of the existing reference algorithms.
This study deals with the review of e-commerce and e-health. The investigation of e-commerce and e-health in the United Kingdom is proposed. The related works of e-commerce and e-health sectors is analysed. The world market for e-commerce is studied.
. For the development of rural areas, many drillings has been installed by the States. However, once these drillings are installed, unfortunately, they do not benefit from effective monitoring despite the huge budgets invested. This paper proposes a computer platform for remote monitoring submerged pump allows extracting water from of the drilling at the castle. Solar panels or Generators groups are often used for pump operations, especially in remote areas where the use of an alternative energy source is desired. The volume of water pumped in a given interval depends on the total amount of solar energy (supply voltage) available in that time. The objective is to measure the supply voltage of the submerged pump and compare this voltage to normal. If there is a voltage difference, an alert message is sent to the equipments remote monitoring center. The proposed solution consists of a central server for processing measures connected to an acquisition unit that monitors a set of sensors. If a failure is detected, the faulty equipment is first identified and the installation is located to facilitate the maintenance team's intervention.
Identifying and characterizing the patient's blood samples is indispensable in diagnostics of malignance suspicious. A painstaking and sometimes subjective task is used in laboratories to manually classify white blood cells. Neural mathematical methods as deep learnings can be very useful in the automated recognition of blood cells. This study uses a particular type of deep learning i.e., convolutional neural networks (CNNs or ConvNets) for image recognition of the four (4) blood cell types (neutrophil, eosinophil, lymphocyte and monocyte) and to enable it to tag them employing a dataset of blood cells with labels for the corresponding cell types. The elements of the database are the input of our CNN and they allowed us to create learning models for the image recognition/classification of the blood cells. We evaluated the recognition performance and outputs learned by the networks in order to implement a neural image recognition model capable of distinguishing polynuclear cells (neutrophil and eosinophil) from those of mononuclear cells (lymphocyte and monocyte). The validation accuracy is 97.77%.
Wireless Telecommunication services are becoming a part of our everyday life because they support a lot of services. However, they are often misused in environments such as hospitals where some patients would require quietness or sometimes they distract medical practitioners from carrying out their duties of saving lives. Therefore, there is a need to use a jammer that prevents mobile phone from working in such an environment. Hence, this paper proposes a cost-effective tri-band mobile phone jammer for hospitals applications. The jammer is designed, constructed, test and found very effective in terms of jamming signal within the bands. Keywords—Cell Phone, Jammer, Mobile Phone, Hospital
The proliferation of smartphones usage necessitates the increase of mobile data traffic volumes. Thus, Mobile Network Operators (MNOs) have to expand their network infrastructure in terms of coverage and capacity. However, the limited attributes of the electromagnetic spectrum, endlessly, pose challenges in meeting these demands. Recently, the International Telecommunication Union (ITU) identified TV (694-790 MHz), L-band (1.427-1.518 GHz) and lower part of C-band (3.4 -3.6 GHz) for possible mobile broadband services. In this paper, a comprehensive spectrum survey in the recommended bands is provided. Spectrum occupancy measurements were conducted at the dense urban areas of University of Ilorin, Kwara state, Nigeria. Energy Detection (ED) technique and Duty Cycle (DC) model were used for measurement evaluation analysis. Findings from this work revealed that Radar L band is fairly occupied with duty cycle of 17.19%. The C band is completely free and unoccupied by neither fixed satellite nor radar systems as only about 1% of the spectrum is presumably occupied. The occupancies in the Television (TV) and Industrial, Scientific and Medical (ISM) and Wireless Local Area Networks (WLAN), bands are 9.54% and 15.09% respectively. Findings show that huge amount of bandwidth is available for wireless broadband in the 2.4 GHz and C bands but not in the proposed TV and L bands.
Infectious disease expansion among the population has attracted many research trying to know who gets sick, how best to prevent a large outbreak.For many years, mathematicians used models to approximate answers to these questions. however, these older models used simplifying assumptions about the host population that drastically reduced the accuracy of the models predictions. Recently, researchers have introduced graph theory to simulate the spread of disease and results reect more the reality.This study considers the sample population as a network in which each person represents a node and the edges represent the social relations. To simulate the propagation of an epidemic, each new infected node becomesthe center of the network and communities representing family members, work place members, friends, traditions and hospital treatment body and others are formed around it. The spreading rate is evaluated using the visitprobability from each community based on the strength of the relation he may have with them.The model is evaluated using the characteristics and data from Lassa fever in west Africa. The accuracy of the simulation results with real expansion of the epidemic disease demonstrates the model's e?ciency.
