In Quest of a Pangram (Part 2)

Looking back on it, I suppose the failure of the Pangram Machine Mark I was piece of good fortune. I mean, otherwise the faster Mark II model might never have come into existence. Another advantage of the latter was that different initial text constraints of rewiring. This meant I could experiment at will, confined only by the initially chosen set of number-word ranges, an important limitation, but still granting much scope

A Comprehensive Survey of Deep Learning Models Based on Keras Framework

Python is one of the most widely adopted programming languages, having replaced a number of those in the field. Python is popular with developers for a variety of reasons, one of which is because it has an incredibly diverse collection of libraries in which users can run. This paper provides the most current survey on Keras, which is a Python-based deep learning Application Programming Interface (API) that runs on top of the machine learning framework TensorFlow. The mentions library is used in conjunction with TensorFlow, PyTorch, CODEEPNEATM, and Pygame to allow integration of deep learning models such as cardiovascular disease diagnostics, graph neural networks, identify health issues, COVID-19 recognition, skin tumors, image detection, and so on, in the applied area. Furthermore, the author used Keras details, goals, challenges, and significant outcomes, as well as the findings, obtained using this method.   Keywords

Supervised classification and improved filtering method for shoreline detection

Shoreline monitoring is important to overcome the problems in the measurement of the shoreline. Recently, many researchers have directed attention to methods of predicting shoreline changes by the use of multispectral images. However, the images being captured tend to have several problems due to the weather. Therefore, identification of multi class features which includes vegetation and shoreline using multispectral satellite image is one of the challenges encountered in the detection of shoreline. An efficient framework using the near infrared–histogram equalisation and improved filtering method is proposed to enhance the detection of the shoreline in Tanjung Piai, Malaysia, by using SPOT-5 images. Sub-pixel edge detection and the Wallis filter are used to compute the edge location with the subpixel accuracy and reduce the noise. Then, the image undergoes image classification process by using Support Vector Machine. The proposed method performed more effectively and reliable in preserving the missing line of the shoreline edge in the SPOT-5 images

IoT for Smart Environment Monitoring Based on Python: A Review

Air pollution, water pollution, and radiation pollution are significant environmental factors that need to be addressed. Proper monitoring is crucial with the goal that by preserving a healthy society, the planet can achieve sustainable development. With advancements in the internet of things (IoT) and the improvement of modern sensors, environmental monitoring has evolved into a smart environment monitoring (SEM) system in recent years. This article aims to have a critical overview of significant contributions and SEM research, which include monitoring the quality of air , water pollution, radiation pollution, and agricultural systems. The review is divided based on the objectives of applying SEM methods, analyzing each objective about the sensors used, machine learning, and classification methods. Moreover, the authors have thoroughly examined how advancements in sensor technology, the Internet of Things, and machine learning methods have made environmental monitoring into a truly smart monitoring system

Leukemia Diagnosis using Machine Learning Classifiers Based on Correlation Attribute Eval Feature Selection

Leukemia refers to a disease that affects the white blood cells (WBC) in the bone marrow and/or blood. Blood cell disorders are often detected in advanced stages as the number of cancer cells is much higher than the number of normal blood cells. Identifying malignant cells is critical for diagnosing leukemia and determining its progression. This paper used machine learning with classifiers to detect leukemia types as a result, it can save both patients and physicians time and money. The primary objective of this paper is to determine the most effective methods for leukemia detection. The WEKA application was used to evaluate and analyze five classifiers (J48, KNN, SVM, Random Forest, and Naïve Bayes classifiers). The results were respectively as follows: 83.33%, 87.5%, 95.83%, 88.88%, and 98.61%, with the Naïve Bayes classifier achieving the highest accuracy; however, accuracy varies according to the shape and size of the sample and the algorithm used to classify the leukemia types

A Comprehensive Survey of 5G mm-Wave Technology Design Challenges

Physical layer protection, which protects data confidentiality using information-theoretic methods, has recently attracted a lot of research attention. Using the inherent randomness of the transmission channel to ensure protection in the physical layer is the core concept behind physical layer security. In 5G wireless communication, new challenges have arisen in terms of physical layer security. This paper introduces the most recent survey on various 5G technologies, including millimeter-Wave, massive multi-input multiple outputs, microcells, beamforming, full-duplex technology, etc. The mentioned technologies have been used to solve this technology, such as attenuation, millimeter-Wave penetration, antenna array architecture, security, coverage, scalability, etc. Besides, the author has used descriptions of the techniques/algorithms, goals, problems, and meaningful outcomes, and the results obtained related to this approach were demonstrated