Taraba State University Campus LAN

This research is necessitated because of under-utilization of computing resources due to lack of a campus local area network in Taraba State University (TSU), Jalingo. Related literatures on design and implementation of campus local area network were reviewed. A hybrid Bus-Star topology and hierarchical model were in the design. This was achieved using Edraw Max 7.9 Network Design Software, AutoCAD, Global Positioning System (GPS) and Google Earth Pro. The research presented a comprehensive TSU–Campus LAN and recommendation for implementation to facilitate enhanced e-learning, online applications and multimedia communication services for all campus residents in TSU, Jalingo. Keywords: Campus, LAN, Edraw, Topology, TS

Sistem Pendukung Keputusan Pemilihan Lokasi Strategis Cabang USAha Warung Makanan

Perkembangan semakin cepat  di bidang teknologi semakin banyak cara manusia untuk menggunakan teknologi tersebut.  Salah  satu  adalah  sistem  pendukung  keputusan.  Sistem  pendukung  keputusan  adalah  sistem  yang mampu  memberikan  kemampuan  pemecahan  masalah  maupun  kemampuan  pengkomunikasian  untuk  masalah dengan kondisi semi terstruktur dan tak terstruktur. Sistem pendukung keputusan yang baik adalah yang dapat membantu memecahkan pilihan yang tidak terstruktur menjadi terstruktur. Dengan adanya itu pemilihan lokasi strategis dapat membantu USAha warung makanan yang akan membuka cabang baru. Kelayakan lokasi terkadang hanya  dikira-kira  atau  hanya  memikirkan  saja  tanpa  adanya  perhitungan.  Maka  dari  itu  dibutuhkan  sistem pendukung  keputusan  pemilhan  lokasi  strategis  untuk  membuka  cabang  USAha  warung  makanan  dengan menggunakan  metode  AHP  (analytical  hierarchy  process).Metode  AHP  (analytical  hierarchy  process)  adalah metode  untuk  memecahkan  suatu  situasi  yang  komplek  tidak  terstruktur  ke  dalam  beberapa  komponen  dalam susunan  yang  hirarki  dengan  memberi  nilai  subjektif  tentang  pentingnya  setiap  variabel  secara  relatif  dan menetapkan variabel mana yang memiliki prioritas paling tinggi guna mempengaruhi hasil pada situasi tersebut. Hasil  akhir  tersebut  berbentuk  rekomendasi-rekomendasi  lokasi-lokasi  strategis  untuk  membuka cabang baru yang  diharapkan  membantu  para  pengusaha  menentukan  lokasi  cabang  baru.  Selain  itu  seleksi  lokasi  yang dilakukan dengan aplikasi ini dapat memberikan hasil cukup akurat, hal ini terbukti dengan pengujian yang telah dilakukan dari perbandingan seleksi manual dan seleksi sistem dengan keakuratan 99,8%

Social Media in Relation to Cooperate Social Responsibility in Fast Food Industry

This paper focuses on analyzing the role of social media (Facebook, Twitter, Google+,Youtube) with regards to corporate social responsibility (CSR), specifically in the fast food industry. The objectives are to identify the use of social media platforms by fast food restaurants, identify if social media integrate corporate social responsibility in their business thereby limiting marketing of unhealthy meals on their websites, and lastly, to find out if marketing of fast food products on social platforms affect users, especially the young people between the age of 15 to 25. The study analyzed and monitored 70 fast food restaurants and their social media activities. 63 0ut of 70 were found to have at least three social media platforms where they promote their products to end users and none of the restaurants have any restriction in marketing any kind of product using social media. Results from questionnaire survey also found that many social media users get adverts from fast food restaurants with 13.85% being affected to buy the food most times and 47.69% sometimes. 81.54% of the participants also agreed that social media can do a great job in limiting the free marketing of unhealthy products by fast food restaurants. Keywords: Social Media, Corporate Social Responsibility (CSR), Stakeholders, fast food

Performance Analysis of Intelligent Computational Algorithms for Biomass Higher Heating Value Prediction

 Higher heating value (HHV) is an essential parameter to consider when evaluating and choosing biomass substrates for combustion and power generation. Traditionally, HHV is determined in the laboratory using an adiabatic oxygen bomb calorimeter. Meanwhile, this approach is laborious and cost-intensive. Hence, it is essential to explore other viable options. In this study, two distinct artificial intelligence-based techniques, namely, a support vector machine (SVM) and an artificial neural network (ANN) were employed to develop proximate analysis-based biomass HHV prediction models. The input variables comprising ash, volatile matter, and fixed carbon were paired to form four separate inputs to the prediction models. The overall findings showed that both the ANN and the SVM tools can guarantee accurate prediction in all the input combinations. The optimal prediction performances were observed when fixed carbon and volatile matter were paired as the input combination. This combination showed that the ANN outperformed the SVM, having presented the least root mean squared error of 0.0008 and the highest correlation coefficient of 0.9274. This study, therefore, concluded that the ANN is more preferred compared to SVM for biomass HHV prediction based on the proximate analysis

Retinal Layer Segmentation in Optical Coherence Tomography Images

The four major causes of blindness are age-related diseases, out of which three affects the retina. These diseases, i.e., glaucoma, diabetic retinopathy, and age-related macular degeneration, require life-long treatment and cause irreversible blindness. Conversely, early diagnosis has been shown to curtail or prevent blindness and visual impairments. A critical element of the clinical diagnosis is the analysis of individual retinal layer properties, as the manifestation of the dominant eye diseases has been shown to correlate with structural changes to the retinal layers. Regrettably, manual segmentation is dependent on the ophthalmologist’s level of expertise, and currently becoming impractical due to advancement in imaging modalities. Inherently, much research on computer-aided diagnostic methods is conducted to aid in extracting useful layer information from these images, which were inaccessible without these techniques. However, speckle noise and intensity inhomogeneity remain a challenge with a detrimental effect on the performance of automated methods. In this paper, we propose a method comprising of fuzzy image processing techniques and graph-cut methods to robustly segment optical coherence tomography (OCT) into five (5) distinct layers. Notably, the method establishes a specific region of interest to suppress the interference of speckle noise, while Fuzzy C-means is utilized to build data terms for better integration into the continuous max-flow to handle inhomogeneity. The method is evaluated on 225 OCT B-scan images, and promising experimental results were achieved. The method will allow for early diagnosis of major eye diseases by providing the basic, yet critical layer information necessary for an effective eye examination

Automatic Annotation of Retinal Layers in Optical Coherence Tomography Images

Early diagnosis of retinal OCT images has been shown to curtail blindness and visual impairments. However, the advancement of ophthalmic imaging technologies produces an ever-growing scale of retina images, both in volume and variety, which overwhelms the ophthalmologist ability to segment these images. While many automated methods exist, speckle noise and intensity inhomogeneity negatively impacts the performance of these methods. We present a comprehensive and fully automatic method for annotation of retinal layers in OCT images comprising of fuzzy histogram hyperbolisation (FHH) and graph cut methods to segment 7 retinal layers across 8 boundaries. The FHH handles speckle noise and inhomogeneity in the preprocessing step. Then the normalised vertical image gradient, and it’s inverse to represent image intensity in calculating two adjacency matrices and then the FHH reassigns the edge-weights to make edges along retinal boundaries have a low cost, and graph cut method identifies the shortest-paths (layer boundaries). The method is evaluated on 150 B-Scan images, 50 each from the temporal, foveal and nasal regions were used in our study. Promising experimental results have been achieved with high tolerance and adaptability to contour variance and pathological inconsistency of the retinal layers in all (temporal, foveal and nasal) regions. The method also achieves high accuracy, sensitivity, and Dice score of 0.98360, 0.9692 and 0.9712, respectively in segmenting the retinal nerve fibre layer. The annotation can facilitate eye examination by providing accurate results. The integration of the vertical gradients into the graph cut framework, which captures the unique characteristics of retinal structures, is particularly useful in finding the actual minimum paths across multiple retinal layer boundaries. Prior knowledge plays an integral role in image segmentation

Skin Cancer Detection in Dermoscopy Images Using Sub-Region Features

Abstract. In the medical field, the identification of skin cancer (Malignant Melanoma) in dermoscopy images is still a challenging task for radiologists and researchers. Due to its rapid increase, the need for decision support systems to assist the radiologists to detect it in early stages becomes essential and necessary. Computer Aided Diagnosis (CAD) systems have significant potential to increase the accuracy of its early detection. Typically, CAD systems use various types of features to characterize skin lesions. The features are often concatenated into one vector (early fusion) to represent the image. In this paper, we present a novel method for melanoma detection from images. First the lesions are segmented by combining Particle Swarm Optimization and Markov Random Field methods. Then the K-means is applied on the segmented lesions to separate them into homogeneous clusters, from which important features are extracted. Finally, an Artificial Neural Network with Radial Basis Function is applied for the detection of melanoma. The method was tested on 200 dermoscopy images. The experimental results show that the proposed method achieved higher accuracy in terms of melanoma detection, compared to alternative methods

The acrylic vessel for JSNS2^{2}-II neutrino target

The JSNS$^{2}$ (J-PARC Sterile Neutrino Search at J-PARC Spallation Neutron Source) is an experiment designed for the search for sterile neutrinos. The experiment is currently at the stage of the second phase named JSNS$^{2}$-II with two detectors at near and far locations from the neutrino source. One of the key components of the experiment is an acrylic vessel, that is used for the target volume for the detection of the anti-neutrinos. The specifications, design, and measured properties of the acrylic vessel are described

Characterization of the correlated background for a sterile neutrino search using the first dataset of the JSNS2^2 experiment

JSNS$^2$ (J-PARC Sterile Neutrino Search at J-PARC Spallation Neutron Source) is an experiment that is searching for sterile neutrinos via the observation of $\bar{\nu}_{\mu} \to \bar{\nu}_{e}$ appearance oscillations using muon decay-at-rest neutrinos. Before dedicated data taking in the first-half of 2021, we performed a commissioning run for 10 days in June 2020. Using the data obtained in this commissioning run, in this paper, we present an estimate of the correlated background which imitates the $\bar{\nu}_{e}$ signal in a sterile neutrino search. In addition, in order to demonstrate future prospects of the JSNS$^2$ experiment, possible pulse shape discrimination improvements towards reducing cosmic ray induced fast neutron background are described.Comment: 7 pages, 3 figure