Sponge media drying using a swirling fluidized bed dryer

Surface preparation today has seen the introduction of sponge media as an alternative product against the traditionally used abrasive materials. Being soft and elastic, the sponge media reduces air borne emission significantly during surface preparation with capability to be re-used. However the environmental conditions limit the sponge media usage whereby wet surroundings prohibit the re-use of the sponge without being dried properly. This study proposes the swirling fluidized bed dryer as a novel drying technique for sponge media. Batch experiments were conducted to study the bed’s hydrodynamics followed by drying studies for three bed loadings of 0.5 kg, 0.75 kg and 1.0 kg at three drying temperatures of 80°C, 90°C and 100°C. It was found that, minimum fluidization velocities for the wet sponge particles were found to be 1.342, 1.361 and 1.382 m/s with minimum swirling velocities of 1.400, 1.469 and 1.526 m/s. Drying times were recorded between 6 to 16 minutes depending on bed loading and drying temperature. Smaller bed weights exhibits faster drying with constant-rate drying period while higher drying temperature and larger bed load resulted in falling-rate drying period. Thin layer modelling for the falling-rate region indicates that Verma et. al model provides the best fit for the present experimental data with coefficient of determination, R2 = 0.98773, root mean square error, RMSE = 0.05048, residuals = 0.3442 and reduced chi-square, χ2 = 0.00254. The effective diffusivity, Deff, for 0.5 kg bed load was found to be 3.454 x 10-9 m2/s and 1.751 x 10-9 m2/s for 0.75 kg bed load. In conclusion, SFBD was found to be a viable and efficient method in drying of sponge media for various industrial applications particularly surface preparation

Validate the Content and Authenticity of the Sender for Text Messages Using QR Code

في ظل ثورة المعلومات التي يشهدها عالمنا الحديث, اصبحت المراسلات الالكترونية ضرورية ومن المهم حفظ هذه المعلومات المرسلة. لذلك عرضنا هذه التقنية لضمان سلامة محتوى الرسائل وأصالة المرسل عبر شبكات الاتصالات عن طريق تحويل رمز الرسالة إلى أرقام، كل واحد من رموز الرسالة (الحروف والأرقام والرموز) سوف تحول إلى ثلاثة أرقام، الرقم الأول يمثل أسكي كود الرمز، والرقم الثاني يمثل تردد هذا الرمز في الرسالة (عدد المرات التي يظهر فيها هذا الرمز في الرسالة)، والرقم الثالث يمثل العدد الإجمالي لمواقع تكرارات هذا الرمز (يحسب موقع الرمز من الرمز الأول في الرسالة إلى هذا الرمز نفسه وتحسب الفراغات أيضا). وسيتم تحويل التوقيع الرقمي للمرسل إلى أرقام مثل رموز الرسالة كما أوضحناها سابقا، هذه الأرقام للتوقيع الرقمي سوف تجمع معا لإنتاج ثلاثة أرقام فقط، وهذا الرقام الثلاثة تجمع مع أرقام رموز الرسالة ، بعدها تم تحويل هذه الأرقام إلى كيو ار كود، يوضع كيو ار كود مع الرسالة ترسال إلى المستلم. المستلم يقوم بأجراء خطوات المرسل (تكوين كيو ار كود من الرسالة المستلمة) ويتم مقارنة الكيو ار كود ما إذا كان مطابق أم لا. وسيضمن المستلم أن المحتوى آمن، ويؤكد صحة المرسل.In light of the information revolution taking place in the modern world, therefore it becomes necessary and important to save this electronic messages. So we offered this technique to ensure the safety of the content of the messages and authenticity of the sender through  networks communication by converting the message's symbols to numbers , each one of this symbols (letters, numbers, symbols) will converted into three digits, the first digit represents the ASCII code of the symbol, the second digit represents the frequency of this symbol in the message (the number of times this symbol is appear in the message), and the third digit represents the total number of the locations of the symbol (calculates the symbol location from the first symbol in the message to this symbol itself and blanks also calculated too) .The digital signature of the sender will converted to numbers like the symbols of message we explained it before, and this numbers of the digital signature will gathering together to produce three numbers only, this number will gathering with each numbers of the message's symbols, the final  numbers will converted to QR Code, the QR Code will placed with the message and sent to the recipient. The recipient returns the steps of the sender (produce QR Code from the received message) and compared it the received QR Codes, if it is match or not. The recipient will ensure that the content is secure, and confirms the authenticity of the sender

An Expert System for Laptop Fault Diagnostic Assistance

Laptop has always been a very useful tool for studies and most university students own at least one laptop. However, not all university student knows how to diagnose and troubleshoot their laptop when it is not working right. Most university student would rather hire computer technician to fix their laptop even though the solution to their laptop problem is rather simple. As cost of living increases every year, so does the cost of hiring a computer technician. Therefore, an expert system for laptop fault diagnostics was proposed to be developed. The objective of this study is to develop an expert system capable of diagnosing the cause of a laptop problem by analyzing user’s answers to the questions displayed by the system. The proposed system would also provide easy to follow solution to the user on how to solve the problem. The methodological approach to developing this system is by following waterfall model. There are five phases in a waterfall model, however only four phases will be executed namely requirement, design, implementation, testing phase. By using this system, user can easily identify possible root causes to their laptop problem by answering few questions related to their laptop problem. On top of that, the expert system will provide appropriate recommendation to the diagnosed laptop problem. Using this system, the time required to diagnose a computer as compared to manual diagnosis will reduce drastically. Most process in manual diagnosis will be automated and handled by the expert system. Lastly, user can save up unnecessary expenditure used to hire a computer technician

Deep learning in distributed denial-ofservice attacks detection method for Internet of Things networks

With the rapid growth of informatics systems’ technology in this modern age, the Internet of Things (IoT) has become more valuable and vital to everyday life in many ways. IoT applications are now more popular than they used to be due to the availability of many gadgets that work as IoT enablers, including smartwatches, smartphones, security cameras, and smart sensors. However, the insecure nature of IoT devices has led to several difficulties, one of which is distributed denial-of-service (DDoS) attacks. IoT systems have several security limitations due to their disreputability characteristics, like dynamic communication between IoT devices. The dynamic communications resulted from the limited resources of these devices, such as their data storage and processing units. Recently, many attempts have been made to develop intelligent models to protect IoT networks against DDoS attacks. The main ongoing research issue is developing a model capable of protecting the network from DDoS attacks that is sensitive to various classes of DDoS and can recognize legitimate traffic to avoid false alarms. Subsequently, this study proposes combining three deep learning algorithms, namely recurrent neural network (RNN), long short-term memory (LSTM)-RNN, and convolutional neural network (CNN), to build a bidirectional CNN-BiLSTM DDoS detection model. The RNN, CNN, LSTM, and CNN-BiLSTM are implemented and tested to determine the most effective model against DDoS attacks that can accurately detect and distinguish DDoS from legitimate traffic. The intrusion detection evaluation dataset (CICIDS2017) is used to provide more realistic detection. The CICIDS2017 dataset includes benign and up-to-date examples of typical attacks, closely matching real-world data of Packet Capture. The four models are tested and assessed using Confusion Metrix against four commonly used criteria: accuracy, precision, recall, and F-measure. The performance of the models is quite effective as they obtain an accuracy rate of around 99.00%, except for the CNN model, which achieves an accuracy of 98.82%. The CNN-BiLSTM achieves the best accuracy of 99.76% and precision of 98.90%