Globally, many studies are being conducted on foods, especially processed items, which actually play a role in changing the genetic characteristics of humans due to the genetic changes caused by these foods. Clear efforts are being made to use food as a treatment for many diseases by giving specific nutritional advice to specific people based on their genes and how they are affected by particular foods. Among these non-communicable diseases are obesity, diabetes, hypertension, and many lifestyle-related diseases. On the personal level, we are working on disseminating a diet system known as the ancestral diet (A-diet), which is a dietary modification plan that focuses on simplicity, traditions, and plant-based nutrients, inspired by ancient nutrition. The aim of this article was to focus on the importance of age and genetics in dealing with the change in foods intake behaviors and nutritional patterns that each individual consumes based on his/her genes and desires, the latter of which are linked to the lifestyle of each person’s age group. An example of this is the tendency of people to become obese, diabetic, or hypertensive as a result of a certain food. Some sort of food that makes people sick, while it does not effect on other people`s health, and the explanation for this is the difference in genes. While the change and diversity in eating patterns, especially among children and young people have become greatly influenced by the environment, friends, schoolmates, and social media.

Abstract— Speech recognition is a valuable tool in various industries; however, achieving high accuracy remains a major challenge, despite the rapid growth of the speech recognition market. Arabic in particular lags behind other languages in the field of speech recognition, requiring further attention and development. To address this issue, this research uses deep neural networks to develop an automatic Arabic speech recognition model based on isolated words technology. A hybrid model, which is originally developed by Radfar et al. [1] for English speech recognition, is adopted and adapted to be used for Arabic speech recognition. This model combines the strengths of recurrent neural networks (RNNs), which are critical in speech recognition tasks, with convolutional neural networks (CNNs) to form a hybrid model known as ConvRNN. A specific model for Arabic speech recognition which is referred to as “Arabic_ConvRNN” model has been developed based on “ConvRNN” model. The adopted model is trained using an Arabic speech publicly available dataset of isolated words, along with a custom-generated dataset specially prepared for this research. The performance of the built model has been evaluated using standard metrics, including word error rate (WER), accuracy, precision, recall, and F-measure (also referred to as f1 score). In addition, K-fold cross-validation method has been employed generalizability. to ensure robustness and The results demonstrated that Arabic_ConvRNN model achieved a high accuracy rate of 95.7% on unseen data, with a minimal WER of about 4.3%. These findings highlight the model's effectiveness in accurately recognizing Arabic speech with minimal errors. Comparisons with similar models from previous studies further Arabic_ConvRNN validated model. the superiority Overall, of the Arabic_ConvRNN model shows great promise for applications requiring accurate and efficient Arabic speech recognition. This research contributes to narrowing the gap in Arabic speech recognition technology, offering a robust solution for accurately converting Arabic speech into text. 

Abstract— The verification of handwritten signatures is a significant area of research in computer vision and machine learning (ML). Handwritten signatures serve as unique biometric identifiers, making it essential to distinguish between genuine and forged signatures. This binary classification task is crucial in legal and financial contexts to prevent fraud and protect customers from potential losses. However, verifying offline handwritten signatures is challenging due to variations in handwriting influenced by factors such as mood, fatigue, writing surface, and writing instrument. This research paper focuses on recognizing offline handwritten Arabic signatures using deep learning (DL), specifically transfer learning (TL) technique which is called “Inception-V3 TL model”. Three distinct datasets are used to build a model for recognizing signatures. The first dataset is referred to as Dataset1. It is an English signature dataset called I. INTRODUCTION A signature is defined as a unique, individual, and personal sign. It is regarded as one of the biometric measurements that can be used for identification and verification. Handwritten signatures have been used in different practical areas of life for many centuries, for example, in contracts, financial operations, documents, identification documents such as passports, driver’s licenses, etc. Additionally, signatures are used in bank cheques and money transfers. However, with the great benefits of using a handwritten signature, came certain challenges for societies such as identity and fraud [1]. "CEDAR" which contains 1,320 genuine and 1,320 forged signatures. Dataset1 is publicly available at: https://www.kaggle.com/datasets/shreelakshmigp/cedard ataset .The second dataset is referred to as Dataset2. It is a new Arabic signature dataset created for this research which contains 1,320 genuine and 1,320 forged signatures. The third dataset is referred to as Dataset3. It is created by merging the English and Arabic signature datasets (Dataset1 and Dataset2). The Inception-V3 TL model is trained on these distinct datasets (Dataset1, Dataset2, and Dataset3). Both normal training and k-fold cross-validation (CV) methods are applied to evaluate the model’s performance, ensuring robustness and reliability. The Inception-V3 model achieved impressive accuracies of 97.48% on the Dataset1, 98.23% on Dataset2, and 97.85% on Dataset3, demonstrating its effectiveness in distinguishing between genuine and forged signatures. 

The study area situated in the center of the Hameimat trough which is located in the southeast

of the Sirte basin. The Hameimat trough contains two of the largest oil fields in Libya,

Gialo and Abu-Attifel fields. The Upper Cretaceous Rachmat, Tagrifet, and Sirte

Formations are considered as the main source rock in Sirte Basin.

Organic geochemical study of the Upper Cretaceous Rachmat, Tagrifet and Sirte

Formations show these Formations have total organic carbon content values of 0.53% to

3.35% fair to excellent as source rock. The Kerogen types are type II and III mixed

continental and marine organic matter. The thermal maturity of these formations indicates a

mature stage in oil window.

Oil saturation index (OSI: S1*100/TOC) shows that Sirte and Rachamt formations have

low oil saturation, while the Tagrifet formation has good potential, where OSI exceeds 140

mg HC/g TOC in the most samples of the formation. The Tagrifet formation considers a

good unconventional reservoir for shale oil, where the Sirte and Rachmat formations

• consider possible for shale oil with high risk.