Autism Spectrum Disorder (ASD) is a complicated collection of neurodevelopmental illnesses characterized by a variety of developmental defects. It is a binary classification system that cannot cope with reality. Furthermore, ASD, data label noise, high dimension, and data distribution imbalance have all hampered the existing classification algorithms. As a result, a new ASD was proposed. This strategy employs label distribution learning (LDL) to deal with label noise and uses support vector regression (SVR) to deal with sample imbalance. The experimental results show that the proposed method balances the effects of majority and minority classes on outcomes. It can effectively deal with imbalanced data in ASD diagnosis, and it can help with ASD diagnosis. This study presents a cost-sensitive approach to correct sample imbalance and uses a support vector regression (SVR)-based method to remove label noise. The label distribution learning approach overcomes high-dimensional feature classification issues by mapping samples to the feature space and then diagnosing multiclass ASD. This technique outperforms previous methods in terms of classification performance and accuracy, as well as resolving the issue of unbalanced data in ASD diagnosis.

This article has proposed an efficient area-optimized elliptic curve cryptographic processor architecture over $GF\left(2^{409}\right)$ and $GF\left(2^{571}\right)$. The proposed architecture employs Lopez-Dahab projective point arithmetic operations. To do this, a hybrid Karatsuba multiplier of 4-split polynomials is proposed. The proposed multiplier uses general Karatsuba and traditional schoolbook multiplication approaches. Moreover, the multiplier resources are reused to implement the modular squares and addition chains of the Itoh-Tsujii algorithm for inverse computations. The reuse of resources reduces the overall area requirements. The implementation is performed in Verilog (HDL). The achieved results are provided on Xilinx Virtex 7 device. In addition, the performance of the proposed design is evaluated on ASIC 65 nm process technology. Consequently, a figure-of-merit is constructed to compare the FPGA and ASIC implementations. An exhaustive comparison to existing designs in the literature shows that the proposed architecture utilizes less area. Therefore, the proposed design is the right choice for area-constrained cryptographic applications.

KG (Knowledge Generation) and understanding has traditionally been a Human-centric activity. KE (Knowledge Engineering) and KM (Knowledge Management) have tried to augment human knowledge on two separate planes: the first deals with machine interpretation of knowledge while the later explores interactions in human networks for KG and understanding. However, both remain computercentric. Crowdsourced HC (Human Computations) have recently utilized human cognition and memory to generate diverse knowledge streams on specific tasks, which are mostly easy for humans to solve but remain challenging for machine algorithms. Literature shows little work on KM frameworks for citizen crowds, which gather input from the diverse category of Humans, organize that knowledge with respect to tasks and knowledge categories and recreate new knowledge as a computer-centric activity. In this paper, we present an attempt to create a framework by implementing a simple solution, called ExamCheck, to focus on the generation of knowledge, feedback on that knowledge and recording the results of that knowledge in academic settings. Our solution, based on HC, shows that a structured KM framework can address a complex problem in a context that is important for participants themselves.

The current global health crisis is a consequence of the pandemic caused by COVID-19. It has impacted the lives of people from all factions of society. The re-emergence of new variants is threatening the world, which urges the development of new methods to prevent rapid spread. Places with more extensive social dealings, such as offices, organizations, and educational institutes, have a greater tendency to escalate the viral spread. This research focuses on developing a strategy to find out the key transmitters of the virus, particularly at educational institutes. The reason for considering educational institutions is the severity of the educational needs and the high risk of rapid spread. Educational institutions offer an environment where students come from different regions and communicate with each other at close distances. To slow down the virus’s spread rate, a method is proposed in this paper that differs from vaccinating the entire population or complete lockdown. In the present research, we identified a few key spreaders, which can be isolated and can slow down the transmission rate of the contagion. The present study creates a student communication network, and virus transmission is modeled over the predicted network. Using student-to-student communication data, three distinct networks are generated to analyze the roles of nodes responsible for the spread of this contagion. Intra-class and inter-class networks are generated, and the contagion spread was observed on them. Using social network strategies, we can decrease the maximum number of infections from 200 to 70 individuals, with contagion lasting in the network for 60 days.

A Lossy network is defined as a network which produced losing of data packet, time delay and network error during data transmission from start to end nodes. Due to highly loaded routers, switches, insufficient bandwidth and network utilization that effects reduction of throughput, request turnaround time and decreasing network reliability. This problem is mostly faced in a real time application such as video streaming, online gaming a voice over IP (VoIP). The best technique is to mitigate Lossy Network for adopting network congestion control algorithm, network coding and Quality of Service (QoS). A Swarm-based nature-inspired metaheuristic approach is a juncture of bionic science and Swarm intelligence. It is based on the collective behavior to solve complex distributed computational problems such as optimizing data traffic and minimizing queue tasks on certain thresholds—the two-stage bionic-inspired algorithm to forage food by eliminating pheromones to explore the positioning stage of cobweb. A cobweb is a hexagonal-based network model in a spider web that attracts other species through electric force to exchange information, reproduce, and generate offspring. A Cloud-based network model contains a variety of data, huge volumes of information expanding at different velocities. In this type of scenario, fog computing and a 6-G network are required for the survival of big data and network platforms. In spider webs prey to generate vibration to attract other spider species to move it where the rate of traffic is minimal therefore the queue task of certain nodes has been minimized to optimize the flow of traffic constantly everywhere. The newly SWO algorithm is a technique to solve complex engineering problem and it an attractive choice of newly practicians and researcher, a viable choice in the field of web optimization. Comparison of reduction of traffic via Circle multi-modal function 57.1% with Raster-gin function and Bohachevsky function are 42.35% and 52.9%.