Image classification and feature extraction have been studied extensively and used efficiently in several applications. This paper suggests a novel method by combining three main methods for texture feature extraction. The proposed method is based on Local Ternary Pattern (LTP), Texture Feature Coding Method (TFCM), and Gray Level Cooccurrence Matrix (GLCM). We have entitled our method as GCLTP which is stand for Gray Coding Local Ternary Pattern. The combination of LTP, TFCM, and GLCM is assigned a unique value used to extract the features of an image. GCLTP is tested using images are taken from the Brodatz database. A set of 22 features were extracted from images. GCLTP is experimentally accomplished a high accuracy in classification by using the most known classifiers.

Precise seasonal temperature forecasting is basic for climate versatility agrarian planning and vitality administration in parched regions This study presents a data driven approach for foreseeing seasonal temperature trends in Niamey utilizing the Prophet model a vigorous timeseries forecasting method created by Facebook Historical climate data from the Climate Inquire about Unit CRU dataset was analyzed and Prophet was utilized to model long-term temperature patterns The results demonstrate a steady warming trend with temperature variances captured viably by the model The proposed approach illustrates the capability of Prophet in dealing with complex seasonal varieties whereas keeping up high predictive exactness These discoveries give important bits of knowledge for policymakers and partners in climate sensitive divisions Future work may explore hybrid models joining extra climatic factors to improve prescient execution.

Cardiovascular disease (CVD) continues to be the predominant cause of mortality globally, underscoring the critical necessity for prompt and precise diagnostic techniques. This paper introduces an innovative machine learning framework for categorizing heartbeat sounds into four classifications—normal, murmur, additional heart sound, and artifact—utilizing audio recordings from the PhysioNet/CinC Challenge 2016 dataset. The methodology employs Mel-Spectrograms and Mel-Frequency Cepstral Coefficients (MFCCs) for feature extraction, converting raw heart sound data into comprehensive time-frequency representations. A Convolutional Neural Network (CNN) is utilized for classification, with its hyperparameters refined by the recently developed Frilled Lizard Optimization (FLO) method, a bio-inspired metaheuristic that emulates the hunting and climbing behaviors of the frilled lizard. The suggested method illustrates the capability of integrating deep learning with sophisticated optimization techniques to improve the diagnostic precision of cardiac auscultation, particularly in non-clinical and remote environments. This can facilitate the development of intelligent, scalable, and proactive intervention methods in preventative cardiovascular healthcare.

Objectives Survey about numbers of students and employers that they were influenced by recurrent using of computers and smart devices, in addition to that, the comparative study performing by knowing of relationship with different and effect on them. Materials And Methods This study was executed on individuals of students and employees during 1 year only (from January 1st 2016 to August 15th 2017) in the College of Health and Medical Technology /kufa in the province of Al-Najaf. It is included in the relation between frequent using of computers and smart devices and another risk factor. Results The results shown significant differences in student’s users of computers and smart devices in which there were little numbers of visions influencing among them in different ages and genders. Utilizing for long periods during days in different purposes yet didn't have a greater effect on users in case of computer vision syndrome according to the employer’s users. Since the preparation of students was having medical glass in male and female and the letter has rated more than the male. In another hand the ages at about (24-32) the using of computers and smart devices was, and the more risk factors among all of them had had greater rates of correlation with the glass wearing in a group of students that they have eye allergy in comparison with the time user. Also these results were shown the superiority of male in comparison with female in using these devices So, from the results mentioned above the convergence in numbers of students that they have vision affects due to employment of these devices in such of ages about [(19-23), (24-32)] in male and female, but the first ages have slightly higher rates than other)] by percentage (50%) of the students that suffer from computer vision syndrome from all proportion, whereas 2nd group of another age has low significant differences in comparison with previous by the percentage closely (36.36%). The employer’s users of computer and smart devices have significant differences (p< 0.05) from the previous results, where it was noticed large numbers of users suffer from problems in their vision due to profuse utilization for long periods of this advice. At the ages (31-40) using for different purposes whether (science, entertaining or both of them) has close correlation with hitting of employers by vision syndrome, where this factor has significant differences in comparison with other risks (p<0.05), while utilizing of these devices under different lights numbers appeared to affect lower than it , also these results were shown no significant differences with regard to employer’s users that they were utilized optical glass after they had using these devices, also effect of this factor ( light numbers) on the putting of eyeglass and presence of significant differences (p<0.05) in comparison with the individuals of users that laying screen protection . We found significant differences (p< 0.05) in numbers of employers were wearing eyeglass after utilizing these appliances and these demonstrate the vulnerability of devices users and the percentage was (58.14%). Conclusions : Using computers and smart devices of students more than employers. Appearing different ienfluencing of eye glass wearing of students and employers that utilizing different computers and smart devices with some of risk factors according to others. It is Found assuring correlations among different risk factors , but shown in employers greater than students.

With the rapid development in the field of information and communication technology, taking advantage of this development has become an urgent necessity in most areas such as the military, engineering, agriculture, and health life. The developers have provided an appropriate environment that accommodates most of these areas, which take on responsibility for looking for speed of implementation, speed of access, remote management, and other features that are in high demand and need. This paper introduces one of the important applications in the field of the Internet of Things (IoT) that is related to human life and health. a smart ambulance system implemented. Patient data collected by sensors are distributed in an ambulance and continuously received at the hospital side based on cloud computing, then it is sent to the specialists to give decisions, that may help to save critical cases according to the received information and it may need quick access and intervention by them Access to the patient shortens the time, especially during peak times. The ambulance tracking is done through the navigation monitoring system.

Geometric programming (GP) is a powerful framework for optimizing posynomial and signomial functions, which are widely used in engineering design, economics, and other fields. A critical aspect of solving geometric programs efficiently lies in understanding the convexity properties of the functions involved, as convexity ensures that local minima are also global minima. This work focuses on correcting and verifying the convexity proofs for two specific signomial functions in geometric programming: f 1 (x)=x 1 2 x 2 −1 +x 2 3 −x 1 x 2 and f₂(x) = x₁³x₂⁻² + x₁⁻¹x₂² - x₁x₂⁻¹. Through a detailed analysis, we demonstrate that f1(x) is **not convex** over the domain (x1,x2)> 0, as its Hessian matrix fails to be positive semi-definite for all positive values of x1and x2. For f2(x), the convexity **cannot be conclusively determined** based on the analysis, as the logarithmic transformation does not universally preserve convexity for signomial functions with negative coefficients. Specific cases suggest local convexity, but a general proof for all (x1,x2)> 0 remains elusive. These results highlight the challenges in analyzing signomial functions and emphasize the importance of rigorous convexity verification in geometric programming. The findings have significant implications for optimization feasibility and modeling considerations, particularly in applications where signomial functions with negative coefficients arise. This work provides a foundation for further research into advanced techniques for analyzing and optimizing such functions, ensuring more robust and reliable solutions in geometric programming.