Klasifikasi Gender Berdasarkan Gambar Menggunakan Metode Deep Learning Pada MATLAB

In the present era, machine intelligence, also known as Artificial Intelligence (AI), is demanded not only to execute specific commands but also to recognize, analyze, or even make decisions, thereby providing desired outputs. By harnessing the power of AI, it is anticipated that desired outcomes will be more accurate and goal achievement will be optimized, minimizing losses. With the capabilities of AI in mind, a research study has been conducted on AI's ability to analyze and make decisions based on specific data. In this study, data in the form of images of men and women were utilized. The objective of this research is to analyze the ability of AI, particularly in gender classification. The method employed in designing this system is Deep Learning, with GoogLeNet as the Convolutional Neural Network utilized. In testing, the data accuracy ranged from 61.8% to 100% for the system without training algorithm options and from 97.5% to 100% for the system with training algorithm options. Testing was also carried out on a smaller set of training data and grayscale images, yielding lower accuracy ranges. From this research, it can be concluded that the quantity of training data, image preprocessing, and training algorithm options are crucial indicators for enhancing prediction accuracy.Pada masa ini, kecerdasan mesin yang disebut sebagai Artificial Intelligence (AI) dituntut untuk tidak hanya melakukan perintah tertentu, tetapi juga mampu untuk mengenal, menganalisis, atau bahkan mengambil keputusan sehingga mampu memberikan keluaran yang diinginkan. Dengan menggunakan AI, diharapkan hasil yang diinginkan menjadi lebih akurat dan mampu mencapai tujuan dengan meminimalkan kerugian. Berlatar dari kemampuan AI tersebut, maka dilakukannya suatu penelitian mengenai kemampuan AI yang dapat menganalisis dan mengambil keputusan berdasarkan data-data tertentu. Pada penelitian ini, digunakan data berupa gambar pria dan wanita. Tujuan dari penelitian ini ialah menganalisis kemampuan AI khususnya dalam hal mengelompokkan gender. Metode yang digunakan adalah Deep Learning dengan GoogLeNet sebagai Convolutional Neural Network yang digunakan. Untuk hasil pengujian, akurasinya mulai dari 61,8-100% untuk sistem tanpa training algorithm options dan 97,5-100% untuk sistem dengan training algorithm options. Pengujian juga dilakukan terhadap data training yang lebih sedikit dan juga gambar abu-abu dengan hasil rentang akurasi yang lebih rendah. Dari penelitian ini, dapat ditarik kesimpulan bahwa jumlah data training, preprocessing image, dan training algorithm options merupakan indikator yang penting untuk meningkatkan keakuratan prediksi

Simulasi Pengendalian Kecepatan Putar Motor DC Menggunakan Metode Logika Fuzzy

This paper discusses DC motor rotational speed control using fuzzy logic method. The system works by utilizing ultrasonic sensors to detect distance. The detected distance affects the rotational speed of the DC motor which is controlled by the Arduino UNO microcontroller. Fuzzy logic is applied to the Arduino UNO microcontroller in which distance as the input, and the rotational speed of the DC motor in the form of Pulse Width Modulation as the output. There are several classifications of distance: very close, close, normal, far and very far. In addition, there are several classifications in the motor rotational speed response: slow, very slow, medium, fast, and very fast. The design of the fuzzy logic system is carried out in the Arduino IDE application and MATLAB using the Mamdani method at the inference stage. Fuzzy logic system is run on the Arduino UNO microcontroller in which the simulation is carried outthrough Proteus application. The test results show that the largest percentage of errors/discrepancies between the Proteus system design and the MATLAB is 1.55%. In the future, this simulation can be applied to systems such as selfdriving cars

Pathogenesis of Rhinitis

Rhinitis is a heterogeneous condition that has been associated with inflammatory responses as in allergic rhinitis but can also occur in the absence of inflammation such as in so-called ‘idiopathic’ (previously ‘vasomotor’) rhinitis. Allergic rhinitis affects approximately 1 in 4 of the population of westernised countries and is characterized by typical symptoms of nasal itching, sneezing, watery discharge and congestion. The intention of this review is to illustrate key concepts of the pathogenesis of rhinitis. Imbalance in innate and adaptive immunity together with environmental factors is likely to play major roles. In allergic rhinitis, initial allergen exposure and sensitization involves antigen presenting cells, T and B lymphocytes and results in the generation of allergen-specific T cells and allergen specific IgE antibodies. On re-exposure to relevant allergens crosslinking of IgE on mast cells results in the release of mediators of hypersensitivity such as histamine and immediate nasal symptoms. Within hours, there is an infiltration by inflammatory cells, particularly Th2 T lymphocytes, eosinophils and basophils into nasal mucosal tissue that results in the late-phase allergic response. Evidence for nasal priming and whether or not remodelling may be a feature of allergic rhinitis will be reviewed. The occurrence of so-called ‘local’ allergic rhinitis in the absence of systemic IgE will be discussed. Non-allergic (non-IgE mediated) rhinitis will be considered in the context of inflammatory and non-inflammatory disorders

Ischemia of rat stomach mobilizes ECL cell histamine

Microdialysis was used to study how ischemia-evoked gastric mucosal injury affects rat stomach histamine, which resides in enterochromaffin-like (ECL) cells and mast cells. A microdialysis probe was inserted into the gastric submucosa, and the celiac artery was clamped (30 min), followed by removal of the clamp. Microdialysate histamine was determined by enzyme-linked immunosorbent assay. In addition, we studied the long-term effects of ischemia on the oxyntic mucosal histidine decarboxylase activity in omeprazole-treated rats. Gastric mucosal lesions induced by the ischemia were enlarged on removal of the clamp. The microdialysate histamine concentration increased immediately on clamping (50-fold rise within 30 min) and declined promptly after the clamp was removed. In contrast, histidine decarboxylase activity of the ECL cells was lowered by the ischemia and returned to preischemic values 9 days later. Mast cell-deficient rats responded to ischemia-reperfusion much like wild-type rats with respect to histamine mobilization. Pretreatment with the irreversible inhibitor of histidine decarboxylase, alpha-fluoromethylhistidine, which is known to eliminate histamine from ECL cells, prevented the rise in microdialysate histamine. Pharmacological blockade of acid secretion (cimetidine or omeprazole) prevented the lesions induced by ischemia-reperfusion insult but not the mobilization of histamine. In conclusion, ischemia of the celiac artery mobilizes large amounts of histamine from ECL cells, which occurs independently of the gross mucosal lesions. The prompt reduction of the mucosal histidine decarboxylase activity in response to ischemia probably reflects ECL cell damage. The lesions develop not because of mobilization of histamine per se but because of ischemia plus reperfusion plus gastric acid

Nasal Sensitization with Ragweed Pollen Induces Local-Allergic-Rhinitis-Like Symptoms in Mice

<div><p>Recently, the concept of local allergic rhinitis (LAR) was established, namely rhinitis symptoms with local IgE production and negative serum antigen-specific IgE. However, the natural course of LAR development and the disease pathogenesis is poorly understood. This study investigated the pathophysiology of mice with allergic rhinitis that initially sensitized with ragweed pollen through the nasal route. Mice were nasally administrated ragweed pollen over consecutive days without prior systemic immunization of the allergen. Serial nasal sensitization of ragweed pollen induced an allergen-specific increase in sneezing, eosinophilic infiltration, and the production of local IgE by day 7, but serum antigen-specific IgE was not detected. Th2 cells accumulated in nose and cervical lymph nodes as early as day 3. These symptoms are characteristic of human LAR. Continual nasal exposure of ragweed pollen for 3 weeks resulted in the onset of classical AR with systemic atopy and adversely affected lung inflammation when the allergen was instilled into the lung. <i>Fcer1a</i>^−/− mice were defective in sneezing but developed normal eosinophilic infiltration. Contrary, <i>Rag2</i>^−/− mice were defective in both sneezing and eosinophilic infiltration, suggesting that T cells play a central role in the pathogenesis of the disease. These observations demonstrate nasal allergen sensitization to non-atopic individuals can induce LAR. Because local Th2 cell accumulation is the first sign and Th2 cells have a central role in the disease, a T-cell-based approach may aid the diagnosis and treatment of LAR.</p></div

Nasal sensitization of ragweed adversely affects allergic inflammation in lungs.

<p>Nasal, ragweed (RW)-sensitized mice were intratracheally challenged with ragweed 3 days after final sensitization. (A) Experimental schema. (B) BALF CD45⁺ cells and eosinophils. Hematoxylin and eosin (C) and Periodic acid-Schiff (D) staining of lungs. Data representative of three independent experiments (means and SEMs, n = 4). *P<0.05, **P<0.01.</p

Local IgE production by nasal ragweed sensitization.

<p>Mice were nasally administered ragweed (RW) pollen on indicated consecutive days. GLTs and PSTs expression in cLN (A) and nasal (B) B cells. (C) qPCR analysis of <i>Aicda</i> expression in cLN and nasal B cells. Data representative of three independent experiments (n = 2). Nasal B cells were pooled from five mice.</p

Repeated nasal ragweed sensitization induces systemic atopy.

<p>Nasal, ragweed (RW)-sensitized mice were analyzed 1 day after final sensitization. (A) Experimental schema. (B) Serum immunoglobulin levels. (C) Production of cytokines in cLN cells. (D–F) Histological examinations. (D) Hematoxylin and eosin (HE) staining of coronal section of nasal cavity. (E) HE staining of nasal lateral mucosa; black square in (D). (F) Periodic acid-Schiff staining of nasal septum; red square in (D). (G) Presence of eosinophils in nasal mucosa. Data representative of three independent experiments (means, SEMs, n = 5). *P<0.05, ***P<0.001. N.D. not detected.</p