Atrocious Impinging of COVID-19 Pandemic on Software Development Industries

COVID-19 is the contagious disease transmitted by Coronavirus. The majority of people diagnosed with COVID-19 may suffer from moderate-to- severe respiratory illnesses and stabilize without preferential treatment. Those who are most likely to experience significant infections include the elderly as well as people with a history of significant medical issues including heart disease, diabetes, or chronic breathing problems. The novel Coronavirus has affected not only the physical and mental health of the people but also had adverse impact on their emotional well-being. For months on end now, due to constant monitoring and containment measures to combat COVID-19, people have been forced to live in isolation and maintain the norms of social distancing with no community interactions. Social ties, experiences, and partnerships are not only integral part of work life but also form the basis of human evolvement. However, COVID-19 brought all such communication to a grinding halt. Digital interactions have failed to support the fervor that one enjoys in face-to-face meets. The COVID-19 disease outbreak has triggered dramatic changes in many sectors, and the main among them is the software industry. This paper aims at assessing COVID-19’s impact on Software Industries. The impact of the COVID-19 disease outbreak has been measured on the basis of some predefined criteria for the demand of different software applications in the software industry. For the stated analysis, we used an approach that involves the application of the integrated Fuzzy ANP and TOPSIS strategies for the assessment of the impact of COVID-19 on the software industry. Findings of this research study indicate that Government administration based software applications were severely affected, and these applications have been the major apprehensions in the wake of the pandemic’s outbreak. Undoubtedly, COVID-19 has had a considerable impact on software industry, yet the damage is not irretrievable and the world’s societies can emerge out of this setback through concerted efforts in all facets of life.     

Multi-layer compression algorithm for 3D deformed mesh based on multi library wavelet neural network architecture

Multimedia Tools and Applications - We propose in this paper a 3D mesh compression algorithm for 3D deformation objects to facilitate the transmission of deformed object to another. This algorithm...     

A novel approach for high dimension 3D object representation using Multi-Mother Wavelet Network

In this paper, we present a novel approach for 3D objects representation. Our idea is to prove that wavelet networks are capable for reconstruction and representing irregular 3D objects used in computer graphics. The major contribution consist to transform an input surface vertices into signals and to provide instantaneously an estimation of the output values for input values. To prove this, we will use a new structure of wavelet network founded on several mother wavelet families. This structure uses several mother wavelet, in order to maximize best wavelet selection probability. An algorithm to construct this structure is presented. First, Data is taken from 3D object. The vertices and their corresponding normal values of a 3D object are used to create a training set. To this stage, the training set can be expressed according to three functions, which interpolates all their vertices. Second we approximate each function using wavelet network. To achieve a better approximation, the network is trained several iterations to optimize wavelet selection for every mother. To guarantee a small error criterion, we adjust wavelet network parameters (weight, translation and dilation) by using an improved Orthogonal Least Squares method version. We consider our proposed approach on some 3D examples to prove that the new approach is able to approximate 3D objects with a good approximation ability.     

Growth,Proliferation and Metastasis of Prostate Cancer Cells Is Blocked by Low-Dose Curcumin in Combination with Light Irradiation

Although anti-cancer properties of the natural compound curcumin have been reported, low absorption and rapid metabolisation limit clinical use. The present study investigated whether irradiation with visible light may enhance the inhibitory effects of low-dosed curcumin on prostate cancer cell growth, proliferation, and metastasis in vitro. DU145 and PC3 cells were incubated with low-dosed curcumin (0.1–0.4 µg/mL) and subsequently irradiated with 1.65 J/cm² visible light for 5 min. Controls remained untreated and/or non-irradiated. Cell growth, proliferation, apoptosis, adhesion, and chemotaxis were evaluated, as was cell cycle regulating protein expression (CDK, Cyclins), and integrins of the α- and β-family. Curcumin or light alone did not cause any significant effects on tumor growth, proliferation, or metastasis. However, curcumin combined with light irradiation significantly suppressed tumor growth, adhesion, and migration. Phosphorylation of CDK1 decreased and expression of the counter-receptors cyclin A and B was diminished. Integrin α and β subtypes were also reduced, compared to controls. Irradiation distinctly enhances the anti-tumor potential of curcumin in vitro and may hold promise in treating prostate cancer.     

Deep CNN for Brain Tumor Classification

Neural Processing Letters - Brain tumor represents one of the most fatal cancers around the world. It is common cancer in adults and children. It has the lowest survival rate and various types...     

AMPN: a semantic resource for Arabic morphological patterns

In this paper, we present a pilot Arabic morphological Pattern Net study based on a lexical semantic resource. During this study, a limited number of Arabic Morphological Patterns have been selected in order to analyze the structure and the behavior of the verbs in the Arabic PropBank, which is a semantically annotated corpus of newswire text from the Annahar Journal. Our goal is twofold: (a) to study whether there is a direct relationship between morphological patterns and verbal semantic roles; and, (b) to verify that this direct relationship is a pervasive component of Arabic verb morphology. The approach to building our morphological Patterns database is based on linguistic generalization of the semantic roles of the verbal predicates. The results obtained show promising outcome for a future, more comprehensive study.     

From SIR to SEAIRD: A novel data-driven modeling approach based on the Grey-box System Theory to predict the dynamics of COVID-19

Applied Intelligence - Common compartmental modeling for COVID-19 is based on a priori knowledge and numerous assumptions. Additionally, they do not systematically incorporate asymptomatic cases....     

Deer Hunting Optimization with Deep Learning Model for Lung Cancer Classification

Lung cancer is the main cause of cancer related death owing to its destructive nature and postponed detection at advanced stages. Early recognition of lung cancer is essential to increase the survival rate of persons and it remains a crucial problem in the healthcare sector. Computer aided diagnosis (CAD) models can be designed to effectually identify and classify the existence of lung cancer using medical images. The recently developed deep learning (DL) models find a way for accurate lung nodule classification process. Therefore, this article presents a deer hunting optimization with deep convolutional neural network for lung cancer detection and classification (DHODCNN-LCC) model. The proposed DHODCNN-LCC technique initially undergoes pre-processing in two stages namely contrast enhancement and noise removal. Besides, the features extraction process on the pre-processed images takes place using the Nadam optimizer with RefineDet model. In addition, denoising stacked autoencoder (DSAE) model is employed for lung nodule classification. Finally, the deer hunting optimization algorithm (DHOA) is utilized for optimal hyper parameter tuning of the DSAE model and thereby results in improved classification performance. The experimental validation of the DHODCNN-LCC technique was implemented against benchmark dataset and the outcomes are assessed under various aspects. The experimental outcomes reported the superior outcomes of the DHODCNN-LCC technique over the recent approaches with respect to distinct measures.     

An Effective Secure MAC Protocol for Cognitive Radio Networks

The vast revolution in networking is increasing rapidly along with technology advancements, which requires more effort from all cyberspace professionals to cope with the challenges that come with advanced technology privileges and services. Hence, Cognitive Radio Network is one of the promising approaches that permit a dynamic type of smart network for improving the utilization of idle spectrum portions of wireless communications. However, it is vulnerable to security threats and attacks and demands security mechanisms to preserve and protect the cognitive radio networks for ensuring a secure communication environment. This paper presents an effective secure MAC protocol for cognitive radio networks, significantly enhancing the security level of the existing DSMCRN and SSMCRN protocols by eliminating the authentication server’s necessity, which can be a single point of failure to compromise the entire network communication. The proposed protocol has proven to be effective and reliable since it does not rely on a centralized entity for providing the required security for a single pair of cognitive users. The protocol also improves the performance in the context of fast switching to data channels leading to higher throughput is achieved compared to the benchmark protocols.     

Review:Advances in the control of mechatronic suspension systems

The suspension system is a key element in motor vehicles. Advancements in electronics and microprocessor technology have led to the realization of mechatronic suspensions. Since its introduction in some production motorcars in the 1980 s, it has remained an area which sees active research and development, and this will likely continue for many years to come. With the aim of identifying current trends and future focus areas, this paper presents a review on the state-of-the-art of mechatronic suspensions. First, some commonly used classifications of mechatronic suspensions are presented. This is followed by a discussion on some of the actuating mechanisms used to provide control action. A survey is then reported on the many types of control approaches, including look-ahead preview, predictive, fuzzy logic, proportional–integral–derivative(PID), optimal, robust, adaptive, robust adaptive,and switching control. In conclusion, hydraulic actuators are most commonly used, but they impose high power requirements, limiting practical realizations of active suspensions. Electromagnetic actuators are seen to hold the promise of lower power requirements, and rigorous research and development should be conducted to make them commercially usable. Current focus on control methods that are robust to suspension parameter variations also seems to produce limited performance improvements, and future control approaches should be adaptive to the changeable driving conditions.