LSGDM Two Stage Consensus Reaching Process for Autocratic Decision Making using Group Recommendations

The decision making is a general and significant action in day-to-day life. In some cases, experts cannot express their preferences using precise value due to inherent unreliability. The utilization of linguistic labels creates expert judgement more informative and consistent for decision making. The group recommendation is considered as a significant factors of e-commerce domain due to their direct impact on profit. The personalized experiments improve the engagement and the count of purchases of the customer when the recommended products are matched to the current interest.In this paper, the Large-Scale Group Decision Making (LSGDM) two stage consensus reaching process is proposed by using three various Amazon real world dataset.This proposed method permits an autocratic decision maker to utilize a different group recommendation for a sequence of decisions at highest level of consensus. The performance of the model is estimated by applying parameters like Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Precision and Recall. The obtained result shows that proposed methodology provides better result while comparing various other methods

Resistance to desiccation and oxygen debt in wedge clams

The wedge dams, Donax cimeatus Linnaeus and Donax faba Cimelin acclimated to about 30° C. and exposed to air, indicated that at room temperature (average 30° C) Donax faba have a longer survival time (94 hours) than D. cmeatus (69 hours) as suggested by their 50% survival time. Survival of D. cmeatus exposed to air was studied at two lower temperatures (17 and 12° C) as well. While the survival of D. cmeatus at 17° C does not appear to be markedly different from that at room temperature, at 12° C the 50% survival time of D. cuneatus was considerably reduced (29 hours)

Design and Development IoT based Smart Energy Management Systems in Buildings through LoRa Communication Protocol

Energy management is a vital tool for reducing significant supply-side deficits and increasing the efficiency of power generation. The present energy system standard emphasizes lowering the total cost of power without limiting consumption by opting to lower electricity use during peak hours. The previous problem necessitates the development and growth of a flexible and mobile technology that meets the needs of a wide variety of customers while preserving the general energy balance. In order to replace a partial load decrease in a controlled manner, smart energy management systems are designed, according to the preferences of the user, for the situation of a full power loss in a particular region. Smart Energy Management Systems incorporate cost-optimization methods based on human satisfaction with sense input features and time of utilization. In addition to developing an Internet of Things (IoT) for data storage and analytics, reliable LoRa connectivity for residential area networks is also developed. The proposed method is named as LoRa_bidirectional gated recurrent neural network (LoRa_ BiGNN) model which achieves 0.11 and 0.13 of MAE, 0.21 and 0.23 of RMSE, 0.34 and 0.23 of MAPE for heating and cooling loads

A Novel Cryptography-Based Multipath Routing Protocol for Wireless Communications

Communication in a heterogeneous, dynamic, low-power, and lossy network is dependable and seamless thanks to Mobile Ad-hoc Networks (MANETs). Low power and Lossy Networks (LLN) Routing Protocol (RPL) has been designed to make MANET routing more efficient. For different types of traffic, RPL routing can experience problems with packet transmission rates and latency. RPL is an optimal routing protocol for low power lossy networks (LLN) having the capacity to establish a path between resource constraints nodes by using standard objective functions: OF0 and MRHOF. The standard objective functions lead to a decrease in the network lifetime due to increasing the computations for establishing routing between nodes in the heterogeneous network (LLN) due to poor decision problems. Currently, conventional Mobile Ad-hoc Network (MANET) is subjected to different security issues. Weathering those storms would help if you struck a good speed-memory-storage equilibrium. This article presents a security algorithm for MANET networks that employ the Rapid Packet Loss (RPL) routing protocol. The constructed network uses optimization-based deep learning reinforcement learning for MANET route creation. An improved network security algorithm is applied after a route has been set up using (ClonQlearn). The suggested method relies on a lightweight encryption scheme that can be used for both encryption and decryption. The suggested security method uses Elliptic-curve cryptography (ClonQlearn+ECC) for a random key generation based on reinforcement learning (ClonQlearn). The simulation study showed that the proposed ClonQlearn+ECC method improved network performance over the status quo. Secure data transmission is demonstrated by the proposed ClonQlearn + ECC, which also improves network speed. The proposed ClonQlearn + ECC increased network efficiency by 8-10% in terms of packet delivery ratio, 7-13% in terms of throughput, 5-10% in terms of end-to-end delay, and 3-7% in terms of power usage variation

Decentralized Machine Learning based Energy Efficient Routing and Intrusion Detection in Unmanned Aerial Network (UAV)

Decentralized machine learning (FL) is a system that uses federated learning (FL). Without disclosing locally stored sensitive information, FL enables multiple clients to work together to solve conventional distributed ML problems coordinated by a central server. In order to classify FLs, this research relies heavily on machine learning and deep learning techniques. The next generation of wireless networks is anticipated to incorporate unmanned aerial vehicles (UAVs) like drones into both civilian and military applications. The use of artificial intelligence (AI), and more specifically machine learning (ML) methods, to enhance the intelligence of UAV networks is desirable and necessary for the aforementioned uses. Unfortunately, most existing FL paradigms are still centralized, with a singular entity accountable for network-wide ML model aggregation and fusion. This is inappropriate for UAV networks, which frequently feature unreliable nodes and connections, and provides a possible single point of failure. There are many challenges by using high mobility of UAVs, of loss of packet frequent and difficulties in the UAV between the weak links, which affect the reliability while delivering data. An earlier UAV failure is happened by the unbalanced conception of energy and lifetime of the network is decreased; this will accelerate consequently in the overall network. In this paper, we focused mainly on the technique of security while maintaining UAV network in surveillance context, all information collected from different kinds of sources. The trust policies are based on peer-to-peer information which is confirmed by UAV network. A pre-shared UAV list or used by asymmetric encryption security in the proposal system. The wrong information can be identified when the UAV the network is hijacked physically by using this proposed technique. To provide secure routing path by using Secure Location with Intrusion Detection System (SLIDS) and conservation of energy-based prediction of link breakage done by location-based energy efficient routing (LEER) for discovering path of degree connectivity.  Thus, the proposed novel architecture is named as Decentralized Federate Learning- Secure Location with Intrusion Detection System (DFL-SLIDS), which achieves 98% of routing overhead, 93% of end-to-end delay, 92% of energy efficiency, 86.4% of PDR and 97% of throughput

Prediction of 1p/19q Codeletion in Diffuse Glioma Patients Using Pre-operative Multiparametric Magnetic Resonance Imaging

Kim D, Wang N, Ravikumar V, Raghuram DR, Li J, Patel A, Wendt RE III, Rao G and Rao A (2019) Prediction of 1p/19q Codeletion in Diffuse Glioma Patients Using Pre-operative Multiparametric Magnetic Resonance Imaging. Front. Comput. Neurosci. 13:52. doi: 10.3389/fncom.2019.00052https://openworks.mdanderson.org/mdacc_imgphys_pubs/1005/thumbnail.jp

Secure Energy Aware Optimal Routing using Reinforcement Learning-based Decision-Making with a Hybrid Optimization Algorithm in MANET

Mobile ad hoc networks (MANETs) are wireless networks that are perfect for applications such as special outdoor events, communications in areas without wireless infrastructure, crises and natural disasters, and military activities because they do not require any preexisting network infrastructure and can be deployed quickly. Mobile ad hoc networks can be made to last longer through the use of clustering, which is one of the most effective uses of energy. Security is a key issue in the development of ad hoc networks. Many studies have been conducted on how to reduce the energy expenditure of the nodes in this network. The majority of these approaches might conserve energy and extend the life of the nodes. The major goal of this research is to develop an energy-aware, secure mechanism for MANETs. Secure Energy Aware Reinforcement Learning based Decision Making with Hybrid Optimization Algorithm (RL-DMHOA) is proposed for detecting the malicious node in the network. With the assistance of the optimization algorithm, data can be transferred more efficiently by choosing aggregation points that allow individual nodes to conserve power The optimum path is chosen by combining the Particle Swarm Optimization (PSO) and the Bat Algorithm (BA) to create a fitness function that maximizes across-cluster distance, delay, and node energy. Three state-of-the-art methods are compared to the suggested method on a variety of metrics. Throughput of 94.8 percent, average latency of 28.1 percent, malicious detection rate of 91.4 percent, packet delivery ratio of 92.4 percent, and network lifetime of 85.2 percent are all attained with the suggested RL-DMHOA approach

HIV Testing among Patients with Presumptive Tuberculosis: How Do We Implement in a Routine Programmatic Setting? Results of a Large Operational Research from India.

BACKGROUND: In March 2012, World Health Organization recommended that HIV testing should be offered to all patients with presumptive TB (previously called TB suspects). How this is best implemented and monitored in routine health care settings in India was not known. An operational research was conducted in Karnataka State (South India, population 64 million, accounts for 10% of India's HIV burden), to test processes and learn results and challenges of screening presumptive TB patients for HIV within routine health care settings. METHODS: In this cross-sectional study conducted between January-March 2012, all presumptive TB patients attending public sector sputum microscopy centres state-wide were offered HIV testing by the laboratory technician, and referred to the nearest public sector HIV counselling and testing services, usually within the same facility. The HIV status of the patients was recorded in the routine TB laboratory form and TB laboratory register. The laboratory register was compiled to obtain the number of presumptive TB patients whose HIV status was ascertained, and the number found HIV positive. Aggregate data on reasons for non-testing were compiled at district level. RESULTS: Overall, 115,308 patients with presumptive TB were examined for sputum smear microscopy at 645 microscopy centres state-wide. Of these, HIV status was ascertained for 62,847(55%) among whom 7,559(12%) were HIV-positive, and of these, 3,034(40%) were newly diagnosed. Reasons for non-testing were reported for 37,700(72%) of the 52,461 patients without HIV testing; non-availability of testing services at site of sputum collection was cited by health staff in 54% of respondents. Only 4% of patients opted out of HIV testing. CONCLUSION: Offering HIV testing routinely to presumptive TB patients detected large numbers of previously-undetected instances of HIV infection. Several operational challenges were noted which provide useful lessons for improving uptake of HIV testing in this important group

Strategies to detect and manage latent tuberculosis infection among household contacts of pulmonary TB patients in high TB burden countries ‐ a systematic review and meta‐analysis

OBJECTIVE: To summarise latent tuberculosis infection (LTBI) management strategies among household contacts of bacteriologically confirmed pulmonary tuberculosis (TB) patients in high‐TB burden countries. METHODS: PubMed/MEDLINE (NCBI) and Scopus were searched (January 2006 to December 2021) for studies reporting primary data on LTBI management. Study selection, data management and data synthesis were protocol‐driven (PROSPERO‐CRD42021208715). Primary outcomes were the proportions of LTBI, initiating and completing tuberculosis preventive treatment (TPT). Reported factors influencing the LTBI care cascade were qualitatively synthesised. RESULTS: From 3694 unique records retrieved, 58 studies from 23 countries were included. Most identified contacts were screened (median 99%, interquartile range [IQR] 82%–100%; 46 studies). Random‐effects meta‐analysis yielded pooled proportions for: LTBI 41% (95% confidence interval [CI] 33%–49%; 21,566 tested contacts); TPT initiation 91% (95% CI 79%–97%; 129,573 eligible contacts, 34 studies); TPT completion 65% (95% CI 54%–74%; 108,679 TPT‐initiated contacts, 28 studies). Heterogeneity was significant (I (2) ≥ 95%–100%) and could not be explained in subgroup analyses. Median proportions (IQR) were: LTBI 44% (28%–59%); TPT initiation 86% (60%–100%); TPT completion 68% (44%–82%). Nine broad themes related to diagnostic testing, health system structure and functions, risk perception, documentation and adherence were considered likely to influence the LTBI care cascade. CONCLUSION: The proportions of household contacts screened, detected with LTBI and initiated on TPT, though variable was high, but the proportions completing TPT were lower indicating current strategies used for LTBI management in high TB burden countries are not sufficient