Gordon Gear Gift Card POS System

Gordon Gear Gift Card POS system is a point of sale system that was created for the purpose of being able to make purchases efficiently and effectively during Gordon Food Service Annual Meeting. At the Annual Meeting, each employee receives a gift card in the amount of sixty five dollars, a gift from Gordon family. Employees have an option to use the gift card at the store that was temporarily set up at the Annual Meeting location for the sole purpose of employees to shop while they are enjoying food and entertainment. The Gordon Gear store offers a huge collection of all traditional GFS logo items. This temporary store consists of such things as, Gordon Gear apparel, sporting goods, toys, dishware, electronics, etc. If an employee chooses not to use their gift card at the temporary store created for the Annual Meeting, they have the option to spend it in any Gordon Food Marketplace Stores. When employees are done shopping, they take their purchase to one of the many cashiers. The cashier scans the item, then the point of sale Gift Card POS system calculates the amount owed by the employee and provides options for the employee to make payment, either by gift card, payroll deduction, or both. Once verified by the employee that the purchase items are correct, system process transactions real time to Gordon Food Service database. Thousands of employees are making purchases with their card at the same time, which can cause a lot of chaos. Before this solution, the checkout application was a desktop application written in Visual Basic. Visual basic is no longer supported by Microsoft and newer operating system does not support Visual Basic installation, which is why another solution had to be implemented. This is why Web Gordon Gear POS system was created. Web Gordon Gear Apparel presentation layer written with the newest technologies, AngularJS, html5 and deployed under Weblogic 12c application server and followed top 10 Open Web Application Security Project (OWASP) guidelines

Efficient Fake News Detection Method using Feature Reduction

Historically, fake news was mainly propaganda. Now it\u27s used to modify people\u27s beliefs and perceptions about specific phenomena, resulting in their change of behaviors. Identifying fake news articles can be difficult for many people.Algorithms to identify if a news article is fake can be used to help people avoid potential misinformation and lead to a reduction in the spread of such false news and discourage the creation of such articles.The research project aims to make fake news detection more efficient computationally and storage-wise by combining two methods. The first method performs feature reduction, which means reducing the number of parameters in a dataset (in this case, a news article) to compress it. The feature reduction method we use is called singular-valued decomposition (SVD). The compressed data is passed into a neural network model. Building a neural network model refers to constructing a model in a computer that is similar to how the neurons in our brain pass information. In this study, we use a natural language processing (NLP) architecture of long short-term memory (LSTM) based on neural networks.For example, an article Trump has won the 2020 US elections , with a label 1 i.e. fake news. When the article is passed through the proposed work, using SVD the article is reduced to a representation like [1.238, 4.56, 0.87]. This representation has a length of 3, instead of the length of the article. This reduced representation is used to train the LSTM to learn the label of the article

Ayer Hitam community participations in conservation: a conceptual paper

Introduction: Bullous pemphigoid is a rare acquired, chronic autoimmune blistering disease; which typically produces tense and large blisters with erosions on skin or mucous membrane. It is primarily affects elderly patients in the fifth to seventh decade of life and can be fatal, particularly in patients who are debilitated, with underlying medical problems or left untreated because of misdiagnosis. There are various factors documented able to facilitate bullous pemphigoid such as physical agents, viral infections and drug intake including gabapentin. Gabapentin is an anticonvulsant drugs, but also widely used in the management of neuropathic pain especially in primary care. Even it is rare, gabapentin has been found to cause bullous pemphigoid from the previous literatures. Nevertheless, the previous reported cases were mainly involving trunks and limbs; spared the oral cavity. Report: We herein report a case of a 66-year-old gentleman, who presented with swollen, itchy and tender upper and lower lips with localized erythematous macular rashes over left side of his face for two days duration. He had a history of taking oral gabapentin 300mg three times a day for one week prior to the onset of the lesions. The drug was prescribed in the primary care clinic for his neuropathic pain secondary to bilateral lower limb atherosclerotic gangrene. Examination revealed multiple bullous over his swollen upper lips with desquamative gingivitis. Other system examinations were unremarkable. The offending drug was immediately ceased and he was then referred to Dermatology team for further evaluation. Result: The diagnosis of Gabapentin-induced bullous pemphigoid was confirmed by clinical assessment and histopathological examination. His symptoms gradually improved after a week commencement of high potency topical corticosteroid. Conclusion: This case illustrates the importance of adequate physical examination including detailed oral examination to look for the pathognomonic features in a case suspected to be drug induced rashes. Indeed, adequate knowledge on the side effect of the possible medication that can induce catastrophe skin problem and prompt action to withhold the precipitating drug is really essential in primary care in order to save the pati ent’ s l i fe

The Significance of Machine Learning in Clinical Disease Diagnosis: A Review

The global need for effective disease diagnosis remains substantial, given the complexities of various disease mechanisms and diverse patient symptoms. To tackle these challenges, researchers, physicians, and patients are turning to machine learning (ML), an artificial intelligence (AI) discipline, to develop solutions. By leveraging sophisticated ML and AI methods, healthcare stakeholders gain enhanced diagnostic and treatment capabilities. However, there is a scarcity of research focused on ML algorithms for enhancing the accuracy and computational efficiency. This research investigates the capacity of machine learning algorithms to improve the transmission of heart rate data in time series healthcare metrics, concentrating particularly on optimizing accuracy and efficiency. By exploring various ML algorithms used in healthcare applications, the review presents the latest trends and approaches in ML-based disease diagnosis (MLBDD). The factors under consideration include the algorithm utilized, the types of diseases targeted, the data types employed, the applications, and the evaluation metrics. This review aims to shed light on the prospects of ML in healthcare, particularly in disease diagnosis. By analyzing the current literature, the study provides insights into state-of-the-art methodologies and their performance metrics.Comment: 8 page

ANTIMICROBIAL ACTIVITY OF SOME SUDANESE MEDICINAL PLANTS AGAINST DIABETIC WOUNDS INFECTIONS

ملخص الاطروحة اشتملت الدراسة على اجراء مسح للفعالية المضادة للبكتريا ل 12 من مستخلصات الكلوروفورم والايثانول النباتية لستة نباتات طبية سودانية: الدمسيسة, الخلة, الكمون, الحرمل, الرمان والحلبة (Ambrosi martima, Ammi visnaga, Nigella sativa, Peganum harmala, Punica granatum and Trigonella foenum-graecum) تنتمي الى ستة عوائل مختلفة باستخدام طريقة الانتشار في الاجار. تم اختبار تاثير جميع المستخلصات ضد خمسة انواع من البكتريا المعيارية والبكتريا المعزولة طبيا من جروح مرضى السكري من مركز زينام لمرضى السكري بالخرطوم ,ٍ نوعين من البكتريا الموجبة جرام ( العصوية الرقيقة والعنقودية الذهبية) (Bacillus subtilis NCTC 8236 and Staphylococcus aureus وثلاثة انواع من البكتريا السالبة جرام ( الاشريكية القولونية, الزائفة الزنجبارية والمتقلبة الاعتيادية). (Escherichia coli ATCC 25922, Proteus vulgaris ATCC 6380 and Pseudomonas aeruginosa ATCC 27853). وجد ان جميع المستخلصات اظهرت فاعلية ضد نوع او اكثر من انواع البكتريا المختبرة بالاضافة الى ان لها مفعول مثبط لتلك الانواع البكترية , وكانت العنقودية الذهبية اكثر انواع البكتريا حساسية لمستخلصات الكلوروفورم والايثانول للكمون, الحرمل والرمان (21,20; 34,34 and 30,30mm respectively), اما الزائفة الزنجبارية فقد اظهرت اقل حساسية لمستخلصات الكلوروفورم والايثانول للخلة والحلبة بين انواع البكتريا المختبرة (0,10 and 0 respectivily). وكذلك شتملت الدراسة على تحديد اقل تركيز مثبط لنمو البكتريا وكذلك اقل تركيز قاتل للبكتريا المعيارية والمعزولة طبيا لاكثر المستخلصات فاعلية , وهي الاربع مستخلصات الايثانولية لبذور نبات الحرمل وقشور ثمار الرمان بطريقة تخفيف الاجار. تم تحديد فاعلية اثنان من مضادات حيوية مرجعية وهي الاريثرومايسين والجنتامايسين ضد الانواع الخمسة البكترية المختبرة وقورنت فاعليتها مع المستخلصات النباتية. تلك المستخلصات تم اختبار فاعليتها على البكتريا الموجودة في عدد 100 عينة معزولة جمعت عشوائيا من مركز زينام لمرضى السكري بالخرطوم. لقد تم في هذه الدراسة التحقق من ثاثير المستخلصات الايثانولية لنباتي الحرمل والرمان على التئام جروح السكري المفتوحة في 28 من الفئران السويسرية (البيضاء). وتم تحضير المستخلصات الايثانولية للحرمل والرمان ومن ثم تحضير المراهم 2% (وزن \ وزن) من المستخلصات في البولي ايثلين جليكول , مع استخدام مرهم التتراسيكلين 3% كحكم . تم عمل تجربة مكونة من 4 مجموعات من الفئران المصابة بداء السكري والمصابة معمليا بالعنقودية الذهبية المعيارية . قورنت المجموعات المعالجة بالمراهم المعدة من المستخلصات الايثانولية للحرمل والرمان مع مرهم التتراسيكلين والمجموعات المصابة الغير معالجة بالمرهم , حيث تم تقدير الالتئام بالنقص في منطقة الجرح . واكدت النتائج ان مرهمي مستخلصي الايثانول بذور الحرمل وقشور ثمرة الرمان 2% هما عاملا التئام فعال , بل وجد انهما افضل من مرهم التتراسيكلين 3% المختبر

Efektivitas hypnoqur’an dalam meningkatkan taqarrab Ilaallah pada mahasiswa di organisasi Persatuan Kebangsaan Pelajar-Pelajar Malaysia di Indonesia Cabang Surabaya (PKPMICS)

Fokus masalah yang diteliti dalam penelitian ini adalah (1) Bagaimanakah pelaksanaan HypnoQur’an dalam meningkatkan Taqarrab Ilaallah Pada Mahasiswa Di Organisasi Persatuan Kebangsaan Pelajar-Pelajar Malaysia Di Indonesia Cabang Surabaya (PKPMICS)? (2) Bagaimanakah hasil pelaksanaan HypnoQur’an dalam meningkatkan Taqarrab Ilaallah Pada Mahasiswa Di Organisasi Persatuan Kebangsaan Pelajar-Pelajar Malaysia Di Indonesia Cabang Surabaya (PKPMICS)? Dalam menjawab permasalahan tersebut, penelitian ini menggunakan pendekatan kuantitatif dengan metode pre-eksperiment design dengan teknik one shot case study yaitu kelompok yang diberikan treatment selanjutnya diobservasi hasilnya yang berguna untuk memeriksa fakta dan data mengenai efektivitas HypnoQur’an dalam meningkatkan Taqarrab Ilaallah Pada Mahasiswa Di Organisasi Persatuan Kebangsaan Pelajar-Pelajar Malaysia Di Indonesia Cabang Surabaya (PKPMICS). subyek dalam penelitian ini adalah Mahasiswa PKPMICS yang berjumlah 15 orang. Metode pengumpulan data yang digunakan dalam penelitian ini adalah dengan metode angket yang dianalisa mengunakan Product Moment. Adapun dari hasil analisa mengunakan Product Moment korelasi antara variabel HypnoQur’an dengan variabel Taqarrab Ilaallah pada Mahasiswa PKPMICS adalah 0.644. Setelah membandingkan dengan r tabel yaitu 0.553, maka Ho ditolak dan Ha diterima yang ertinya HypnoQur’an efektif dalam meningkatkan Taqarrab Ilaallah pada Mahasiswa di Organisasi Persatuan Kebangsaan Pelajar-Pelajar Malaysia di Indonesia Cabang Surabaya (PKPMICS) dengan memiliki bacaan nilai korelasi yang “Tinggi.

Efektivitas hypnoqur’an dalam meningkatkan taqarrab Ilaallah pada mahasiswa di organisasi Persatuan Kebangsaan Pelajar-Pelajar Malaysia di Indonesia Cabang Surabaya (PKPMICS)

Fokus masalah yang diteliti dalam penelitian ini adalah (1) Bagaimanakah pelaksanaan HypnoQur’an dalam meningkatkan Taqarrab Ilaallah Pada Mahasiswa Di Organisasi Persatuan Kebangsaan Pelajar-Pelajar Malaysia Di Indonesia Cabang Surabaya (PKPMICS)? (2) Bagaimanakah hasil pelaksanaan HypnoQur’an dalam meningkatkan Taqarrab Ilaallah Pada Mahasiswa Di Organisasi Persatuan Kebangsaan Pelajar-Pelajar Malaysia Di Indonesia Cabang Surabaya (PKPMICS)? Dalam menjawab permasalahan tersebut, penelitian ini menggunakan pendekatan kuantitatif dengan metode pre-eksperiment design dengan teknik one shot case study yaitu kelompok yang diberikan treatment selanjutnya diobservasi hasilnya yang berguna untuk memeriksa fakta dan data mengenai efektivitas HypnoQur’an dalam meningkatkan Taqarrab Ilaallah Pada Mahasiswa Di Organisasi Persatuan Kebangsaan Pelajar-Pelajar Malaysia Di Indonesia Cabang Surabaya (PKPMICS). subyek dalam penelitian ini adalah Mahasiswa PKPMICS yang berjumlah 15 orang. Metode pengumpulan data yang digunakan dalam penelitian ini adalah dengan metode angket yang dianalisa mengunakan Product Moment. Adapun dari hasil analisa mengunakan Product Moment korelasi antara variabel HypnoQur’an dengan variabel Taqarrab Ilaallah pada Mahasiswa PKPMICS adalah 0.644. Setelah membandingkan dengan r tabel yaitu 0.553, maka Ho ditolak dan Ha diterima yang ertinya HypnoQur’an efektif dalam meningkatkan Taqarrab Ilaallah pada Mahasiswa di Organisasi Persatuan Kebangsaan Pelajar-Pelajar Malaysia di Indonesia Cabang Surabaya (PKPMICS) dengan memiliki bacaan nilai korelasi yang “Tinggi.

A Comprehensive Literature Review on Convolutional Neural Networks

The fields of computer vision and image processing from their initial days have been dealing with the problems of visual recognition. Convolutional Neural Networks (CNNs) in machine learning are deep architectures built as feed-forward neural networks or perceptrons, which are inspired by the research done in the fields of visual analysis by the visual cortex of mammals like cats. This work gives a detailed analysis of CNNs for the computer vision tasks, natural language processing, fundamental sciences and engineering problems along with other miscellaneous tasks. The general CNN structure along with its mathematical intuition and working, a brief critical commentary on the advantages and disadvantages, which leads researchers to search for alternatives to CNN’s are also mentioned. The paper also serves as an appreciation of the brain-child of past researchers for the existence of such a fecund architecture for handling multidimensional data and approaches to improve their performance further

An Internet of Things (IoT) based wide-area Wireless Sensor Network (WSN) platform with mobility support.

Wide-area remote monitoring applications use cellular networks or satellite links to transfer sensor data to the central storage. Remote monitoring applications uses Wireless Sensor Networks (WSNs) to accommodate more Sensor Nodes (SNs) and for better management. Internet of Things (IoT) network connects the WSN with the data storage and other application specific services using the existing internet infrastructure. Both cellular networks, such as the Narrow-Band IoT (NB-IoT), and satellite links will not be suitable for point-to-point connections of the SNs due to their lack of coverage, high cost, and energy requirement. Low Power Wireless Area Network (LPWAN) is used to interconnect all the SNs and accumulate the data to a single point, called Gateway, before sending it to the IoT network. WSN implements clustering of the SNs to increase the network coverage and utilizes multiple wireless links between the repeater nodes (called hops) to reach the gateway at a longer distance. Clustered WSN can cover up to a few km using the LPWAN technologies such as Zigbee using multiple hops. Each Zigbee link can be from 200 m to 500 m long. Other LPWAN technologies, such as LoRa, can facilitate an extended range from 1km to 15km. However, the LoRa will not be suitable for the clustered WSN due to its long Time on Air (TOA) which will introduce data transmission delay and become severe with the increase of hop count. Besides, a sensor node will need to increase the antenna height to achieve the long-range benefit of Lora using a single link (hop) instead of using multiple hops to cover the same range. With the increased WSN coverage area, remote monitoring applications such as smart farming may require mobile sensor nodes. This research focuses on the challenges to overcome LoRa’s limitations (long TOA and antenna height) and accommodation of mobility in a high-density and wide-area WSN for future remote monitoring applications. Hence, this research proposes lightweight communication protocols and networking algorithms using LoRa to achieve mobility, energy efficiency and wider coverage of up to a few hundred km for the WSN. This thesis is divided into four parts. It presents two data transmission protocols for LoRa to achieve a higher data rate and wider network coverage, one networking algorithm for wide-area WSN and a channel synchronization algorithm to improve the data rate of LoRa links. Part one presents a lightweight data transmission protocol for LoRa using a mobile data accumulator (called data sink) to increase the monitoring coverage area and data transmission energy efficiency. The proposed Lightweight Dynamic Auto Reconfigurable Protocol (LDAP) utilizes direct or single hop to transmit data from the SNs using one of them as the repeater node. Wide-area remote monitoring applications such as Water Quality Monitoring (WQM) can acquire data from geographically distributed water resources using LDAP, and a mobile Data Sink (DS) mounted on an Unmanned Aerial Vehicle (UAV). The proposed LDAP can acquire data from a minimum of 147 SNs covering 128 km in one direction reducing the DS requirement down to 5% comparing other WSNs using Zigbee for the same coverage area with static DS. Applications like smart farming and environmental monitoring may require mobile sensor nodes (SN) and data sinks (DS). The WSNs for these applications will require real-time network management algorithms and routing protocols for the dynamic WSN with mobility that is not feasible using static WSN technologies. This part proposes a lightweight clustering algorithm for the dynamic WSN (with mobility) utilizing the proposed LDAP to form clusters in real-time during the data accumulation by the mobile DS. The proposed Lightweight Dynamic Clustering Algorithm (LDCA) can form real-time clusters consisting of mobile or stationary SNs using mobile DS or static GW. WSN using LoRa and LDCA increases network capacity and coverage area reducing the required number of DS. It also reduces clustering energy to 33% and shows clustering efficiency of up to 98% for single-hop clustering covering 100 SNs. LoRa is not suitable for a clustered WSN with multiple hops due to its long TOA, depending on the LoRa link configurations (bandwidth and spreading factor). This research proposes a channel synchronization algorithm to improve the data rate of the LoRa link by combining multiple LoRa radio channels in a single logical channel. This increased data rate will enhance the capacity of the clusters in the WSN supporting faster clustering with mobile sensor nodes and data sink. Along with the LDCA, the proposed Lightweight Synchronization Algorithm for Quasi-orthogonal LoRa channels (LSAQ) facilitating multi-hop data transfer increases WSN capacity and coverage area. This research investigates quasi-orthogonality features of LoRa in terms of radio channel frequency, spreading factor (SF) and bandwidth. It derived mathematical models to obtain the optimal LoRa parameters for parallel data transmission using multiple SFs and developed a synchronization algorithm for LSAQ. The proposed LSAQ achieves up to a 46% improvement in network capacity and 58% in data rate compared with the WSN using the traditional LoRa Medium Access Control (MAC) layer protocols. Besides the high-density clustered WSN, remote monitoring applications like plant phenotyping may require transferring image or high-volume data using LoRa links. Wireless data transmission protocols used for high-volume data transmission using the link with a low data rate (like LoRa) requiring multiple packets create a significant amount of packet overload. Besides, the reliability of these data transmission protocols is highly dependent on acknowledgement (ACK) messages creating extra load on overall data transmission and hence reducing the application-specific effective data rate (goodput). This research proposes an application layer protocol to improve the goodput while transferring an image or sequential data over the LoRa links in the WSN. It uses dynamic acknowledgement (DACK) protocol for the LoRa physical layer to reduce the ACK message overhead. DACK uses end-of-transmission ACK messaging and transmits multiple packets as a block. It retransmits missing packets after receiving the ACK message at the end of multiple blocks. The goodput depends on the block size and the number of lossy packets that need to be retransmitted. It shows that the DACK LoRa can reduce the total ACK time 10 to 30 times comparing stop-wait protocol and ten times comparing multi-packet ACK protocol. The focused wide-area WSN and mobility requires different matrices to be evaluated. The performance evaluation matrices used for the static WSN do not consider the mobility and the related parameters, such as clustering efficiency in the network and hence cannot evaluate the performance of the proposed wide-area WSN platform supporting mobility. Therefore, new, and modified performance matrices are proposed to measure dynamic performance. It can measure the real-time clustering performance using the mobile data sink and sensor nodes, the cluster size, the coverage area of the WSN and more. All required hardware and software design, dimensioning, and performance evaluation models are also presented

Hand Gesture Recognition Using a Radar Echo I–Q Plot and a Convolutional Neural Network

We propose a hand gesture recognition technique using a convolutional neural network applied to radar echo inphase/quadrature (I/Q) plot trajectories. The proposed technique is demonstrated to accurately recognize six types of hand gestures for ten participants. The system consists of a low-cost 2.4-GHz continuous-wave monostatic radar with a single antenna. The radar echo trajectories are converted to low-resolution images and are used for the training and evaluation of the proposed technique. Results indicate that the proposed technique can recognize hand gestures with average accuracy exceeding 90%