Handwritten Recognition System Based on Machine Learning

مقدمة: يعد التعرف على خط اليد قضية مهمة في الوقت الحاضر ، حيث يمكن أن تكون الكتابة اليدوية صورة أو مستندًا وما إلى ذلك ، تعد قدرة الكمبيوتر على التعرف على الأرقام المكتوبة بخط اليد مهمة جدًا في أكثر من تطبيق مثل تطبيقات الترجمة والقراءة والتعرف على الأرقام. يوفر المشروع المقترح نظامًا يتعرف على الأرقام الإنجليزية المكتوبة بخط اليد ، ويتم تنزيل بيانات الإدخال من مجموعة بيانات عالمية. يتكون النظام المقترح من عدد من المراحل. المرحلة الأولى هي المعالجة المسبقة ، والتي تتضمن تغيير حجم الصور لتكون بحجم واحد (28 * 28) ، ثم يتم تطبيق خطوة (تعيين البيانات). أما بالنسبة لمرحلة التصنيف ، فقد اعتمدت على استخدام خوارزميتين ، خوارزمية KNN والشبكة العصبية (خطأ backpropagation). لبدء عملية تدريب الخوارزميات المختارة ، تم تقسيم البيانات إلى مجموعتين ، مجموعة التدريب ومجموعة الاختبار. تم استخدام خوارزميتين لغرض اختيار أفضلها من خلال تقييم أدائها باستخدام عدد من مقاييس التقييم. تم استخدام الدقة والدقة لغرض تقييم أداء الخوارزميات. كان أداء خوارزمية KNN 0.94 و 0.942 على التوالي عند k = 4. بينما كان أفضل أداء وصلت إليه آلية الشبكة العصبية 0.98673333 و 0.9698 على التوالي ، في العصر = 15. تظهر الشبكة العصبية (خطأ backpropagation) أفضل نتيجة  في مرحلة الاعتراف. طرق العمل: لا تقدم تقنية (KNN) أي افتراضات حول مجموعة البيانات الأساسية. إنه معروف بفعاليته وسهولة استخدامه. إنها خوارزمية تعلم خاضعة للإشراف. لتقدير فئة البيانات غير المسماة ، يتم توفير مجموعة تدريب معنونة تحتوي على نقاط بيانات مقسمة إلى مجموعات عديدة. الاستنتاجات: توضح مؤشرات الدقة والدقة وصفًا دقيقًا لأداء الخوارزميات المستخدمة في النظام المقترح. وصف المؤشرين أداء الخوارزمية (KNN) والتي أعطت النتائج (0.94 و 0.942) على التوالي.Background: Handwriting recognition is an important issue nowadays, where handwriting can be a image, document, etc., the ability of a computer to recognize handwritten numbers is very important in more than one application such as translation, reading and number recognition applications. The proposed project provides a system that recognizes handwritten English numbers, the input data being images downloaded from a global dataset. The proposed system consists of a number of stages. The first stage is the preprocessing, which includes resizing of the images to be one size (28 * 28), and then a step (data mapping) is applied. As for the classification stage, it relied on the use of two algorithms, the KNN algorithm and the neural network (error backpropagation). To start the process of training the selected algorithms, the data was divided into two sets, the training setand the test set. Two algorithms were used for the purpose of choosing the best of them, by evaluating their performance using a number of evaluation metrics. Accuracy and Precision were used for the purpose of evaluating the performance of the algorithms. The performance of the KNN algorithm was 0.94 and 0.942 respectively when k = 4. While the best performance reached by the neural network mechanism was 0.98673333 and 0.9698, respectively, at epoch = 15. The neural network (error backpropagation) is shows the best result in the recognation stage Materials and Methods: K-Nearest Neighbors (KNN) technique makes no assumptions about the basic dataset. It is recognized for its effectiveness and ease of use. It is a supervised learning algorithm. To estimate the category of the unlabeled data, a labeled training set containing data points separated into many groups is supplied. Results: The performance of the KNN model with various values for "K." Since the high value of model accuracy was "0.94", the "4" parameter value is the one that provides the best results and precision was "0.94". Conclusion: The problem of handwritten recognition needs high accuracy and precision indicators show an accurate description of the performance of the algorithms that were employed in the proposed system. The two indicators described the performance of the algorithm (KNN), which gave results (0.94 and 0.942)

Study the pathogenicity of Enterobacter cloacae in rats that isolated from diarrheatic buffalos calves in Babylon Province

The study was aimed to isolate Enterobacter cloacae from feces of buffalo calves suffering from diarrhea and shows its pathogenicity in rats, 150 fecal samples were collected and cultured directly on MacConky agar then tested biochemically and with EPi 20 test to confirm a diagnosis of Enterobacter cloacae. After that injected 4 groups of rat with (106,107 and 108 CFU/ml) respectively, while the fourth group not treated and considered as a control group, also extracted the cell wall from Enterobacter and used four groups of rat to injected with different concentration (150, 250 and 350 μ/ml) of extracted cell wall respectively, while the fourth group considered as a control group. Results show that 10 isolates of Enterobacter were obtained from a stool and out of 10 isolates 7 isolates belong to Enterobacter cloacae. Bacterial isolation from internal organs shows the very heavy isolation of bacteria in dose 108 CFU/ml as compared with other doses, histopathological changes in organs (liver and spleen) of animals which injected with live bacteria and extracted cell wall reveal severe changes as compared with control groups

Automatic Spike Neural Technique for Slicing Bandwidth Estimated Virtual Buffer-Size in Network Environment

The Next-generation networks, such as 5G and 6G, need capacity and requirements for low latency, and high dependability. According to experts, one of the most important features of (5 and 6) G networks is network slicing. To enhance the Quality of Service (QoS), network operators may now operate many instances on the same infrastructure due to configuring able slicing QoS. Each virtualized network resource, such as connection bandwidth, buffer size, and computing functions, may have a varied number of virtualized network resources. Because network resources are limited, virtual resources of the slices must be carefully coordinated to meet the different QoS requirements of users and services. These networks may be modified to achieve QoS using Artificial Intelligence (AI) and machine learning (ML). Developing an intelligent decision-making system for network management and reducing network slice failures requires reconfigurable wireless network solutions with machine learning capabilities. Using Spiking Neural Network (SNN) and prediction, we have developed a 'Buffer-Size Management' model for controlling network load efficiency by managing the slice's buffer size. To analyze incoming traffic and predict the network slice buffer size; our proposed Buffer-Size Management model can intelligently choose the best amount of buffer size for each slice to reduce packet loss ratio, increase throughput to 95% and reduce network failure by about 97%

Is the rotation of the femural head a potential initiation for cutting out? A theoretical and experimental approach

We conclude the center-center position in the head of femur of any kind of lag screw or blade is to be achieved to minimize rotation of the femoral head and to prevent further mechanical complications

Phylogenic diversity some of the pseudomonas Isolate from wound infection in Babylon province

The current study involved the collection of (75) swabs were collected from different animal wounds distributed Geographically  in all parts of Babylon province, for the period from October 2021 to the end of February 2022. The results showed only 42 samples were identical to positively infected in microscopic examination, including 31 (%) samples contained Pseudomonas spp, while 44 samples were not infected with any type of bacterial infection. Bacteria were diagnosed based on a culturing on different agars according to the type of bacteria (Chrom agar), as well as by  using VITEK 2 system. Pseudomonas spp. was the most dominant bacteria that isolated in the current study. So the antibiotic susceptibility of the Pseudomonas aeruginosa against 16 antibiotics belonging to 9 classes of antimicrobial agents was tested. The results showed that the resistance percentage of Pseudomonas aeruginosa was (100%) towards Ampicillin, Piperacillin, Amoxicillin, Cefepime, Cefotaxime, Ceftazidime, Aztreonam, trimethoprim and Chloramphenicol. followed by (80%) resistance to Amoxicillin clavulanate and Doxycycline. Also Nitrofurantoin showed (60%) resistance, Levofloxacin and Azithromycin showed (40%) resistance  against isolated Pseudomonas . While  Meropenem and amikacin have (20%) resistance against isolated Pseudomonas.&nbsp