Catálogo de publicaciones - revistas

<jats:p>This research has taken a Quantitative, interpretive and cross-sectional designs in the form of a self-administered questionnaire through survey. The aim is to investigate the adoption of Cloud Computing by IT based small and medium scales enterprises in Northwestern Nigeria. About one hundred and fifty (150) questionnaires were distributed among seven states in the North-Western Nigeria and average of one hundred and nine (109) was responded. The result of the survey shown that most of the IT professional in these SMEs are cloud provider’s end users, continue to use cloud provider in the future, is part of their strategic effort, recommended Cloud provider to others and they are very satisfied with the cloud providers. Challenges that are preventing them from getting the maximum value out of cloud providers are lack of encouragement, poor training, application is missing and lack of executive sponsorship.</jats:p>

<jats:p>Generally, conventional home wiring system use simple latching switch that is being connected to the power supply for controlling electrical appliances such as fan, light, washing machine, air conditioner and television. The switch is usually located at the wall near the electrical appliance. This requires the user to move to the location of the switches to control the appliances. There is rapid increase in the number of people with special needs like the elderly and the disabled. Smart houses are considered a good alternative for the independent life of older persons and persons with disabilities. A smart home is a home that provides its residents the comfort, the convenience and the ease of operation of devices at all times, irrespective of where the resident actually is within the house. Smart Homes include devices that have automatic functions and systems that can be remotely controlled by the user. The primary objective of a smart house is to enhance comfort, energy saving, security for the residents and independent living of people at old age and people with disabilities. A low-cost prototype of a voice controlled smart home system controlling four devices by an Arduino microcontroller via a four-channel relay is presented. Voice control is one of the easiest methods to give input commands and is a more personalized form of control, since it can be adapted and customized to a particular speaker’s voice. Voice recognition is a computer software program embedded in a hardware device with the ability to decode the human voice. Most voice recognition systems require “training” (also called “enrolment”) where an individual speaker reads text or isolated vocabulary into the system. The system analyses the person’s specific voices and uses it to fine-tune the recognition of that person’s command. Upon successful recognition of the voice command, the microcontroller drives the corresponding load with the help of the relay circuit. Voice or Speech Processing has been applied successfully for the control of the supply of energy to home appliances.</jats:p>

<jats:p>Advancement in IoT and cloud technology has opened room for various application services in different areas. With such popularity, the volume of data increases immensely and it is infeasible for cloud technology to provide real-time services in some cases. Fog computing is an extension of cloud technology which provides real-time and time-sensitive services. Data processing is done at fog nodes that allow seamless connectivity and application services. In this paper, various fog computing architectures, applications, and security issues are discussed. It aims to provide a comprehensive review of various aspects of fog computing</jats:p>

<jats:p>In this paper, we aim to introduce a survey on the applications of deep learning for breast cancer detection and diagnosis to provide an overview of the progress in this field. In the survey, we firstly provide an overview on deep learning and the popular architectures used for breast cancer detection and diagnosis. Especially we present four popular deep learning architectures, including convolutional neural networks, fully convolutional networks, auto encoders, and deep belief networks in the survey. Secondly, we provide a survey on the studies exploiting deep learning for breast cancer detection and diagnosis.</jats:p>

<jats:p>This paper presents application of the binomial and factorial identities and expansions that are used in artificial intelligence, machine learning, and cybersecurity. The factorial and binomial identities can be used as methodological advances for various algorithms and applications in information and computational science. Cybersecurity is the practice of protecting the computing systems, communication networks, data and programs from cyber-attacks. Its objective is to reduce the risk of cyber-attacks and protect against the unauthorized exploitation of systems and networks. For this purposes, we need a strong cryptographic algorithms like RSA algorithm and Elliptic Curve Cryptography. In this connection, computing and combinatorial techniques based on factorials and binomial distributions are developed for the researchers who are working in artificial intelligence and cybersecurity.</jats:p>

<jats:p>A low-cost and light-weight ultra-wideband bowtie antenna for radar applications was simulated, fabricated and tested. A concise and easy to follow step-by-step description of the performed bowtie antenna simulation in Ansys HFSS software is presented. Optimized antenna parameters were utilized to fabricate the antenna. Fabrication was achieved by utilizing an FR4 PCB. The prototype was tested using a spectrum analyzer. The fabricated bowtie antenna results were used to validate the simulation results. The results obtained from the simulation platform were in close agreement to those of the prototype antenna. Results on effect of substrate thickness and frequency on S11 are also presented. The prototype produced improved overall S11 as compared to the simulation. The results indicate that the fabricated antenna satisfies bandwidth requirements for the UHF, L and S bands.</jats:p>

<jats:p>Dimensionality Reduction is one of the useful techniques used in number of applications in order to reduce the number of features to improve the productivity and efficiency of the task. Clustering is one of the influential tasks in data mining. Dimensionality reductions are used in data mining, Image processing, Networking, Mobile computing, etc. The elementary intention of this work is to apply dimensionality reduction algorithms and then cluster the datasets to detect outliers. A bio-inspired ACO (Ant Colony optimization) algorithm has been proposed to reduce dimensionality. Also another bio-inspired algorithm FA (Firefly Algorithm) has been proposed to detect outliers. The three distinct medical datasets: thyroid dataset, Oesophagal dataset and Heart disease dataset are used for experimental results.</jats:p>

<jats:p>Security assessment of routing and authentication protocols is based on the comparison of basic and secured versions of protocols such as AODV, SAODV, DSDV, SEAD, ZRP, SRP, LHAP, HEAP etc. In this paper, a framework for security assessment is presented. It is a complete system that attempts to provide the promised services to each user or application. To assess the security of different protocols, a security index is assigned. The value of security index shows how much a protocol is secured. To assign the security index, security parameters have been found out and the performance of different protocols have been analyzed under normal condition, Black Hole attack, Wormhole attack, and DoS attack.</jats:p>

<jats:p>This paper presents the analysis of frequency and polarization scaling on rain attenuated signal of a KU-Band link. The study was carried out in Jos, Plateau state, Nigeria (9.89650 N, 8.85830 E; 1192 meters) with Maximum, Average and Minimum Temperatures of 29.80C, 22.80C and 170C respectively. Data were obtained for the months of May, June, July, August, September and October 2020.Davis Vantage Vue weather station was used to measured and record one-minute rain-rates from a minimum of 0.8 mm/h up to a value of 460 mm/h, with an accuracy of 0.2 mm/h. The precipitation data, with date and time is captured on the micro-chip of the wireless electronic data logger, which, when calibrated, logs on data every minute. The down converted Ku-band signal was fed into the digital satellite meter and a spectrum analyzer for signal level analysis, logging and recording samples of viewed spectrum over finite periods of time on a computer system. Both satellite signal and precipitation measurements were done concurrently. Data were analyzed using Microsoft excel and results were obtained based on ITU-R model. Results obtained revealed that rain attenuated signal for vertical and horizontal polarization varies for different rain rate and months under reviewed. For the month of May 2020, rain attenuated signal is more severe from rain rate of 100mm/hr with the highest rain attenuation of 25.57dB, 30.37dB, and 39.84dB at frequencies of 12GHz, 15GHz and 18GHz respectively while the rain attenuated signal for vertical polarization is more severe from 80mm/hr with highest recorded rain attenuation of 27.96dB, 40.33dB and 45.71dB at frequencies of 12GHz, 15GHz and 18GHz respectively. For the month of July 2020, the results shows that rain attenuated signal for vertical polarization is more severe from rain rate of 80mm/hr with the highest rain attenuation of 60.57dB, 73.37dB, and 100.84dB for frequencies of 12GHz, 15GHz, and 18GHz respectively. At Horizontal polarization, signal losses are more severe from 60mm/hr. The results further proves that frequency and polarization scaling of rain attenuated signalare major factors to consider when designing a microwave link budget especially in the study area that experiences high amount of rainfall annually.</jats:p>

<jats:p>Hate speech, which is a problem that affects everyone in the world, is taking on new dimensions and becoming more violent every day. The majority of people’s interest in social media has grown in recent years, particularly in the United States. Twitter placed 5th in social media usage figures in 2022, with an average of 340 million users globally, and human control of social media has become unfeasible as a result of this expansion. As a result, certain platforms leveraging deep learning approaches have been created for machine translation, word tagging, and language understanding. Different strategies are used to develop models that divide texts into categories in this way. The goal of this research is to create an effective a new hybrid prediction model that can recognize racist, xenophobic, and sexist comments published in English on Twitter, a popular social media platform, and provide efficient and accurate findings. 7.48 percent of the data were classified as racist, genderist, and xenophobic in the used dataset. A new hybrid LSTM Neural Network and Recurrent Neural Network based model was developed in this study and compared with the most popular supervised intelligent classification models such as Logistic Regression, Support Vector Machines, Naive Bayes, Random Forest, and K-Nearest Neighbors. The results of these several models were thoroughly examined, and the LSTM Neural Network model was found to have the best performance, with an accuracy rate of 95.20 percent, a recall value of 48.94 percent, a precision of 60.95 percent, and an F1 Score of 51.32 percent. The percentage of test data was then modified, and the comparison was made by attempting to get various findings. With a larger dataset, these deep learning models are believed to produce substantially better outcomes.</jats:p>