The spectral conjugate gradient (SCG) method is one of the most commonly used methods to solve large- scale nonlinear unconstrained optimization problems. It is also the research and application hot spot of optimization theorists and optimization practitioners. In this paper, a new hybrid spectral conjugate gradient method is proposed based on the classical nonlinear spectral conjugate gradient method. A new parameter  is given. Under the usual assumptions, the descending direction independent of any line search is generated, and it has good convergence performance under the strong Wolfe line  search condition . On a set of test problems, the numerical results show that the algorithm is effective.

COVID-19 hit the world unexpectedly, forcing humans to isolate themselves. It has placed the lives of people in jeopardy with its fury. The global pandemic had a detrimental effect on the worlds' education spheres. It has imposed a global lockdown, with a negative impact on the students' lives. Continuing regular classes on-campus was out of the question. At that moment, online learning came to us as a savior. The quality of online education was yet to be tested on a large scale compared to regular schooling. Educational data mining is a modern arena that holds promise for those who work in education. Data mining strategies are developed to uncover latent information and identify valuable trends that can increase students' performance and, in turn, contribute to the improvement of the educational system in the long run. This research mainly aims to identify a comparative analysis of the students' academic performance between online and on-campus environments and distinguish the significant characteristics that influence their academic endeavors. The impact of the factors on the students' performance is visualized with the help of the Decision Tree Classification Model. This paper will assist in giving a good overview that influences the distinguished factors on students' academic performance. Moreover, educators will also be benefited from this paper while making any important decision regarding the educational activity.

In this paper, an attempt has been made to develop an Adaptive Social Acceleration Constant based PSO (ASACPSO). ASACPSO converge faster in comparison to basic PSO. The best value has been selected based on the minimum number of kounts required to minimize the function. Adaptive Social Acceleration Constant based PSO (ASACPSO) has been developed using the best value of adaptive social acceleration constant. The Adaptive Social Acceleration Constant has been searched using three formulations which led to the development of three algorithms-ALDPSO, AELDPSO-I and AELDPSO-II. All three were implemented on Rosenbrock   function to get the best value of adaptive social acceleration constant. Similarly it has been implemented on seven mathematical benchmark   functions and its performance has been compared to Basic Particle Swarm Optimization (BPSO). ASACPSO was observed to converge faster and give better accuracy. Results show that Kounts required for convergence of mathematical function is lesser for ASACPSO in comparison to basic PSO.ASACPSO reduces the computational time to optimize the function. 

Data privacy is being breached occasionally whether in storage or in transmission. This is due to the spate of attack occasioned by the movement of data and information on an insecure internet. This study aimed to design a system that would be used by both sender and receiver of a secret message. The system used the combination of Steganography (MSB) and Cryptography (RSA) approaches to ensure data privacy protection. The system generates two keys: public and private keys, for the sender and receiver to encrypt and decrypt the message respectively. The steganography method used does not affect the size of cover image. The software was designed using python programming language in PyCharmIDE. The designed system enhanced the security and privacy of data. The results of this study reveal the effectiveness of combination of steganography and cryptography over the use of either cryptography or steganography and other existing systems.

Alterations in environmental and demographic equations have resulted in phenomenal rise of human centric diseases, ocular being one of them. Technological advancements have witnessed early diagnosis of much of the previously un-ciphered diseases. This paper addresses two research questions (RQs) with the study being focused on conjunctivitis (the most prevalent eye ailment in adults as well as minors). The motive of both the RQs rests in implementing three state-of-the art deep learning framework for classification of the ocular disease and validation of the frameworks. Validation of the frameworks is seconded by improvised proposals for enhancements. RQ1 establishes and validates whether the three off the shelf Deep Learning frameworks VGG19, ResNet50, and Inception V3 properly classify the disease or not. RQ2 analyses the effectiveness of each classifier with further enhancement proposals. The algorithms were implemented on 210 images and generated an accuracy of 87.3%, 93.6%, and 95.2% for VGG19, ResNet50, and Inception V3 using Adam optimizer, with slightly variant results when applying Adadelta optimizer. These results were typical of the classification frameworks with enhancements. With pervasive penetration of Artificial Intelligence in healthcare, this paper presents the efficacy of Deep Learning Frameworks in conjunctivitis classification.