Cassava, a significant crop in Africa, Asia, and South America, is a staple food for millions. However, classifying cassava species using conventional color, texture, and shape features is inefficient, as cassava leaves exhibit similarities across different types, including toxic and non-toxic varieties. This research aims to overcome the limitations of traditional classification methods by employing deep learning techniques with pre-trained AlexNet as the feature extractor to accurately classify four types of cassava: Gajah, Manggu, Kapok, and Beracun. The dataset was collected from local farms in Lamongan Indonesia. To collect images with agricultural research experts, the dataset consists of 1,400 images, and each type of cassava has 350 images. Three fully connected (FC) layers were utilized for feature extraction, namely fc6, fc7, and fc8. The classifiers employed were support vector machine (SVM), k-nearest neighbors (KNN), and Naive Bayes. The study demonstrated that the most effective feature extraction layer was fc6, achieving an accuracy of 90.7% with SVM. SVM outperformed KNN and Naive Bayes, exhibiting an accuracy of 90.7%, sensitivity of 83.5%, specificity of 93.7%, and F1-score of 83.5%. This research successfully addressed the challenges in classifying cassava species by leveraging deep learning and machine learning methods, specifically with SVM and the fc6 layer of AlexNet. The proposed approach holds promise for enhancing plant classification techniques, benefiting researchers, farmers, and environmentalists in plant species identification, ecosystem monitoring, and agricultural management.
Whenever, the Internet of Things (IoT) applications and devices increased, the capability of the its access frequently stressed. That can lead a significant bottleneck problem for network performance in different layers of an end point to end point (P2P) communication route. So, an appropriate characteristic (i.e., classification) of the time changing traffic prediction has been used to solve this issue. Nevertheless, stills remain at great an open defy. Due to of the most of the presenting solutions depend on machine learning (ML) methods, that though give high calculation cost, where they are not taking into account the fine-accurately flow classification of the IoT devices is needed. Therefore, this paper presents a new model bas
... Show MoreAn excellent reputation earned by initiating and practicing sustainable business practices has additional benefits, of which are reducing environmental incidents and an improvement in operational efficiency as this has the potential to help firms improve on productivity and bring down operating costs. Taken further, with ever-increasing socially and environmentally-conscious investors and the public alike, this act of natural resources management could have a significant implication on market value and income of the practicing firms.
The above proposition has been supported by sustainable business practices literature that is continuously conversing and deliberating upon the impact of efficient resource d
... Show Moreملخص البحث
تبحث الدراسھ عن تنفیذ افضل لمفھوم التعلم مدى الحیاة كھیكل موجھ للسیاسة التربویة في العراق بشكل عام وفي
التعلیم العالي بشكل خاص. تحدد الدراسة استراتجیات التعلم مدى الحیاة وتناقش اھمیتھ وسماتھ الرئیسیة لتسھیل
الوصول الى فرص تعلم متمیز و ملائم لحاجات الطلبة مدى الحیاة، كما تناقش دور الجامعة في تحقیق ھذا الھدف.
Television white spaces (TVWSs) refer to the unused part of the spectrum under the very high frequency (VHF) and ultra-high frequency (UHF) bands. TVWS are frequencies under licenced primary users (PUs) that are not being used and are available for secondary users (SUs). There are several ways of implementing TVWS in communications, one of which is the use of TVWS database (TVWSDB). The primary purpose of TVWSDB is to protect PUs from interference with SUs. There are several geolocation databases available for this purpose. However, it is unclear if those databases have the prediction feature that gives TVWSDB the capability of decreasing the number of inquiries from SUs. With this in mind, the authors present a reinforcement learning-ba
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Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an ob
... Show MoreThe aim of the current research is to reveal the effect of using brain-based learning theory strategies on the achievement of Art Education students in the subject of Teaching Methods. The experimental design with two equal experimental and control groups was used. The experimental design with two independent and equal groups was used, and the total of the research sample was (60) male and female students, (30) male and female students represented the experimental group, and (30) male and female students represented the control group. The researcher prepared the research tool represented by the cognitive achievement test consisting of (20) questions, and it was characterized by honesty and reliability, and the experiment lasted (6) weeks
... Show MoreProducts’ quality inspection is an important stage in every production route, in which the quality of the produced goods is estimated and compared with the desired specifications. With traditional inspection, the process rely on manual methods that generates various costs and large time consumption. On the contrary, today’s inspection systems that use modern techniques like computer vision, are more accurate and efficient. However, the amount of work needed to build a computer vision system based on classic techniques is relatively large, due to the issue of manually selecting and extracting features from digital images, which also produces labor costs for the system engineers.
 
... Show MoreCyber security is a term utilized for describing a collection of technologies, procedures, and practices that try protecting an online environment of a user or an organization. For medical images among most important and delicate data kinds in computer systems, the medical reasons require that all patient data, including images, be encrypted before being transferred over computer networks by healthcare companies. This paper presents a new direction of the encryption method research by encrypting the image based on the domain of the feature extracted to generate a key for the encryption process. The encryption process is started by applying edges detection. After dividing the bits of the edge image into (3×3) windows, the diffusions
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