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The current version of imaging equipment cannot quickly and effectively make up for the reduction of visibility triggered by bad weather. Traditional strategies minimize hazy impacts by employing an image depth model and a physical model. Following experts, erroneous depth data reduces the efficacy of the dehazing algorithm. Dehazing methods based on CNN are imperfect to handle region which is bright or similar to atmospheric light and thus leads to oversaturation of pixels. These challenges can be addressed by proposing a novel model that incorporates the idea of a Graphical Neural Network. The amount of light coming from the atmosphere is estimated using normalization where the contrast of the image gets adjusted using Bias Contrast stretch Histogram Equalization. An enhanced Transmission map estimator is used to render the hazy scene. Finally, the cross-layer graphical neural network-based CNN model is applied to produce a haze-free image and eliminate the over-saturation of pixels. Extensive evaluation findings show that the proposed approach can significantly recuperate misty imagery, even if the images have a substantial amount of haze. [ABSTRACT FROM AUTHOR]

Traffic has increased significantly over the past few years as a result of an increase in the number of vehicles using the common routes. It has been noted that manually operating signals has grown to be difficult. The difficulties encountered during an accident are the backed-up ambulances during emergencies caused by vehicle congestion and poor weather conditions like fog and haze. Considering this, the study focuses on the outcomes of an intelligent accident detection system using Vehicular Fog Computing (VFC). Automatic identification of crash spots and free flow of ambulances on roadways at peak hours of traffic. VFC has recently gained popularity as a useful tool for assisting vehicles in computing and storing service demands. Using the built-in sensors on a smartphone to monitor vehicular collisions and report them to the closest accessible first responder, as well as providing real-time location monitoring for paramedics and emergency victims would greatly improve the odds of recovery for emergency victims while saving time and money. This computing model guarantees the optimization of traffic congestion and energy consumption in the accident and foggy environment. This method also relies on delivering medical records to the closest hospital before the ambulance arrives, so pre-treatment can begin in the hospital. [ABSTRACT FROM AUTHOR]

Digital transition has started to change the way people read news articles more through a digital device and less on paper. Youngsters today do not spend enough time reading news articles. In this work, a knowledge-driven news story generation using collaborative learning to represent the gist of news is proposed. The entire work focuses on two major concerns. Initially, the dialogues associated with the corresponding speaker are extracted from the news. Secondly, the audio of the mapped dialogues is incorporated into the final video. Logistic Regression is deployed to identify the theme the news. Deep learning techniques are employed to identify the main characters in a supervised manner using Named Entity Recognition (NER) tagging algorithm, suitable cartoon dispositions and their semantic relations. This approach improves not the reader's comprehension and creativity but also improves mutual goals, opportunities for peer discussion and engaging the underachievers to think reflexively. In addition, it also improves the learner's motivation and participation. The proposed framework outperforms an accuracy of 83.98% when compared with conventional methods also suggests that the readers found the packages interesting and informative on digital devices. Moreover, this method can be used efficiently in real-time for various applications. [ABSTRACT FROM AUTHOR]

Objective: To investigate the effect of epigallocatechin-3-gallate (EGCG) on endothelial dysfunction in spontaneously hypertensive rats (SHR). Methods: Wistar-Kyoto (WKY) rats and SHR were divided into four groups; WKY control, SHR control and SHR treated with EGCG (50 mg/kg/day) or losartan (10 mg/kg/day). The treatment was given daily for 4 weeks by oral gavage and the blood pressure was monitored by tail-cuff method every 3 days. Acetylcholineinduced endothelium-dependent relaxations were assessed in isolated phenylephrine-precontracted aortic rings at the end of treatment. The vascular levels of reactive oxygen species, nitric oxide, tetrahydrobiopterin, and cyclic guanosine monophosphate were also measured. Moreover, the expression of angiotensin II type 1 (AT1) receptor protein was determined. Results: The systolic blood pressure was significantly decreased in SHR treated with EGCG. The impaired endothelium-dependent relaxation was significantly improved in aortic ring isolated from the EGCG-treated SHR group. EGCG also significantly increased the levels of nitric oxide, tetrahydrobiopterin, and cyclic guanosine monophosphate, while decreasing the level of reactive oxygen species and the protein expression of AT1 receptor in SHR. Conclusions: EGCG attenuates endothelial dysfunction in SHR by decreasing oxidative stress and increasing vascular nitric oxide bioavailability, which may be modulated partly by inhibition of vascular AT1 receptors. An increase in endothelium-dependent relaxation may contribute to a decrease in blood pressure in hypertensive animals. [ABSTRACT FROM AUTHOR]

Chromium is one of the most abundant metals in the earth's crust mined as chromites. Industries that widely use compounds containing chromium as Cr(III) in the tanning process generally release effluents with high chromium levels into natural water resources, frequently without appropriate effluent treatment. Though physical and chemical methods of chromium removal are adopted, the processes are expensive, use a lot of energy and generate secondary toxic waste. The removal of chromium by biological resources on the other hand is more environmentally friendly, less expensive, and lacks the release of secondary pollutants. The present study is aimed towards the assessment of the Cr(VI) reduction capability of two bacterial strains, Alcaligenes sp. (S11) and Listeria sp. (S18) used as a consortium. These bacterial strains were isolated from the Periyar river of Kerala, India. Batch mode studies were carried out by live bacterial cells and an integrated study was carried out to test the bacterial strains on chromium-contaminated water and sediments through bioreactor and column study respectively under optimized conditions. The phytotoxicity of the treated water was studied on black gram. In the bioreactor study, the maximum Cr(VI) reduction capacity of bacterial consortium (S11 and S18) was found to be 97%. The successive experiment for the toxicity of treated water on the black gram indicated that there was no toxicity in the growth parameters of the plant. [ABSTRACT FROM AUTHOR]

Over the past era, subgraph mining from a large collection of graph database is a crucial problem. In addition, scalability is another big problem due to insufficient storage. There are several security challenges associated with subgraph mining in today's on-demand system. To address this downside, our proposed work introduces a Blockchain-based Consensus algorithm for Authenticated query search in the Large-Scale Dynamic Graphs (BCCA-LSDG). The two-fold process is handled in the proposed BCCA-LSDG: graph indexing and authenticated query search (query processing). A blockchain-based reputation system is meant to maintain the trust blockchain and cloud server of the proposed architecture. To resolve the issues and provide safe big data transmission, the proposed technique also combines blockchain with a consensus algorithm architecture. Security of the big data is ensured by dividing the BC network into distinct networks, each with a restricted number of allowed entities, data kept in the cloud gate server, and data analysis in the blockchain. The consensus algorithm is crucial for maintaining the speed, performance and security of the blockchain. Then Dual Similarity based MapReduce helps in mapping and reducing the relevant subgraphs with the use of optimal feature sets. Finally, the graph index refinement process is undertaken to improve the query results. Concerning query error, fuzzy logic is used to refine the index of the graph dynamically. The proposed technique outperforms advanced methodologies in both blockchain and non-blockchain systems, and the combination of blockchain and subgraph provides a secure communication platform, according to the findings. [ABSTRACT FROM AUTHOR]

Data transmission through a wireless network has faced various signal problems in the past decades. The orthogonal frequency division multiplexing (OFDM) technique is widely accepted in multiple data transfer patterns at various frequency bands. A recent wireless communication network uses OFDM in longterm evolution (LTE) and 5G, among others. The main problem faced by 5G wireless OFDM is distortion of transmission signals in the network. This transmission loss is called peak-to-average power ratio (PAPR). This wireless signal distortion can be reduced using various techniques. This study uses machine learning-based algorithm to solve the problem of PAPR in 5G wireless communication. Partial transmit sequence (PTS) helps in the fast transfer of data in wireless LTE. PTS is merged with deep belief neural network (DBNet) for the efficient processing of signals in wireless 5G networks. Result indicates that the proposed system outperforms other existing techniques. Therefore, PAPR reduction in OFDM by DBNet is optimized with the help of an evolutionary algorithm called particle swarm optimization. Hence, the specified design supports in improving the proposed PAPR reduction architecture. [ABSTRACT FROM AUTHOR]

Food is required for human survival. Paddy is a vital food crop serving 60% of the Indian population. Food quality is determined by the plant yield. Unfavorable environmental circumstances, soil fertility, bacteria, viruses, nematodes, fertilizer use, and the absence of nutritional shortages substantially influence plant yield. As a result, it is critical to protect the plants from illness. Crop yield must be improved to meet food scarcity of growing population. Although disease symptoms are apparent in various parts of plant like leaves, stem, fruits and stem, the infections are commonly observed in the leaves. Understanding plant pathology plays a vital role in disease detection. Early detection of diseases is a prompt intervention that aids the farmers in controlling disease spread, resulting in increased agricultural quantity and quality. Image processing techniques with advanced computer vision technologies like machine learning and deep learning have proven the automation of plant disease diagnosis precisely. The main objectives of this research are to investigate computer vision technologies for the early identification of plant diseases and help novice researchers in the same domain learn about plant diseases and the methodologies for disease detection in paddy plant leaves. Consequently this manuscript reviews significant paddy plant infections, highlights related study of tools and techniques, current research, limitations and conclusions for future research in this field. [ABSTRACT FROM AUTHOR]

This paper aims to enable accurate and reliable emergency message broadcast in Vehicular Ad hoc Network (VANET). The VANET is the most common topology used in Intelligent Transportation Systems (ITS), where changes in standard topology due to the mobility of nodes create challenges in broadcasting the emergency message and efficient data delivery in both highway and urban scenarios. The main problems in urban scenarios are channel contention, message redundancy and road structure. To obtain information, broadcast protocols for VANET typically use beacon messages, which are distributed among the vehicles. When multiple vehicles transmit messages at the same time, a broadcast storm occurs and vehicles experience message delivery failure. To address this problem, Adaptive Scheduled Partitioning and Broadcasting Technique (ASPBT) for emergency message broadcast and beacon retransmissions for message reliability were proposed. This protocol dynamically modifies several partitions and beacon periodicity to reduce the number of retransmissions. Later, the partition size is determined by estimating the network transmission density of each partition schedule via the Black Widow Optimization (BWO) algorithm is proposed. The simulation is carried out with different network densities at the vehicle speed of 110 km/h, a direct path length of 12 km under a four-way direction path and performance analysis was performed. [ABSTRACT FROM AUTHOR]

Paddy is a major food crop serving more than half the population of people in the world. It is inevitable to improve the quantity and quality of food crop with the growing population. Different factors including soil fertility, water availability, erratic climate variations, diseases, and pests, have an impact on paddy crop yield. It is crucial to identify the root cause for the reduction in yield of paddy. Early disease diagnosis prevents the plants from getting worst through its consecutive stage. The concern with manually diagnosing plant leaf diseases with the naked eye is that the results can be less accurate and even unreliable. Automatic disease diagnosis eliminates the need for experts and provides accurate results. This paper will assist the farmers to identify the leaf diseases automatically with the aid of Convolutional Neural Networks. This research includes paddy leaf disease categories: bacterial blight, blast, tungro, brown spot and healthy leaves. The dataset contains 800 images, 160 images from each of the five categories. Images are resized to 256 * 256 pixels and normalized. The network architecture created with convolutional, maxpooling, flatten and dense layers. The Dataset is divided into training and validation set in 70:30 ratios and model is trained with 20 epochs of batch size 16. The novelty of the study is the implementation of extended Huber loss function for minimizing the loss. Furthermore, it is cross compared with existing loss functions. The Proposed model has achieved 96.63% training accuracy and 86.61% validation accuracy with 5 classes. Performance of model is evaluated with confusion matrix with precision, recall, F1-score and support as parameters. [ABSTRACT FROM AUTHOR]

A chaotic system can have a series of values for the developing variable that completely repeat themselves, giving periodic behavior beginning from any point in that series. It is very sensitive to initial conditions and control parameters which make them suitable for image encryption. The two maps Duffing and Logistic maps are combined to generate a random sequence. The Logistic map is 1 Dimensional and contains one control parameter (r). Duffing map contains 2 dimensions X and Y, and two control parameters A and B. Both are XOR'ed to get the random numbers. The control parameters of Duffing map are controlled by the control parameter r. X and Y are looped to get repetitive iterations. The random sequences are analyzed for statistical tests for randomness, keyspace, key sensitivity (or) cross-correlation, periodicity, auto-correlation, information entropy, equilibrium, linear complexity, and speed are the analyses to verify their conformity for use in cryptographic applications. Image encryption is the process of encoding a grayscale image with the help of a series of random numbers created by using a combination of chaotic maps, in such a way that uncertified users can't access it. NPCR, UACI, Histogram, and Information Entropy are the analyses performed. Image encryption and secret key generation are presented based on the Duffing map and logistic chaotic map. The series of random numbers and the greyscale image pixels are XOR'ed to shuffle the pixels and it turned the original image into a cipher image. [ABSTRACT FROM AUTHOR]

Evidence gathered on the same component over a period at various points in time are known as time series. In order to make predictions and guide strategic decision-making, time series forecasting means examining time series da-ta using statistics and simulation. Due to the various components in time series data, prediction of any target element have high risk of failure and still remains a challenge for the researchers. This paper propose three stacked long short-term memory that utilize the significant huge Stock Market Dataset extracted from the KAGGLE database. The Stock Market dataset contains 1344 exchange traded fund data and 7195 stock data. The data is pre-processed with encoding and incomplete values. The prescriptive information analysis have been accomplished to investigate the time series distribution. The original dataset applied to one, two, three and four stacked long short-term memory to analyze the error performance stock rate prediction. The dataset also applied to one, two, three and four stacked long short-term memory with dropout to analyze the error performance stock rate prediction. The performance metrics utilized for analysis are Mean Squared Error, Mean Absolute Error and Root Mean Squared error. Implementation results exhibits that proposed three-stacked long short-term memory shows minimum Mean Absolute Error of 0.004384, Mean Squared Error of 0.000045 and Root Mean Squared error of 0.006680 when compared with all other long short-term memory models. [ABSTRACT FROM AUTHOR]

This systematic review and meta-synthesis seeks to explore cancer patients' journey towards resiliency. The secondary aim of this review is to identify unique resilience protective factors among cancer patients. A thorough search was conducted in eight electronic databases and the grey literature for published or unpublished qualitative and mixed methods studies. Studies that explored resilience among cancer patients were included. The studies were appraised using the Critical Appraisal Skill Programme Checklist. The overall certainty of evidence was further evaluated using the Grading of Recommendations, Assessment, Development, and Evaluation's Confidence in Evidence from Reviews of Qualitative Research. Themes identified were synthesized using Sandelowski and Barroso's meta-synthesis method. A total of 34 studies comprising 987 cancer patients were included in this review. Three themes and nine subthemes were generated from the meta-synthesis. The themes were: (1) Confronting the cancer diagnosis, (2) personal adaptations to cancer, and (3) drawing strength from others. The findings highlighted how individuals overcame cancer adversities through resilience, which is influenced by various factors, including life experiences, social-cultural stigmas, spirituality, social support networks, coping strategies, motivation, acceptance of illness, positive mindset, and engagement with healthcare facilities. This review highlights the role of resilience in a cancer patient's journey. It emphasizes on the importance of building resilience in both cancer patients and survivors to effectively overcome the challenges of their cancer diagnosis. These insights are essential for developing interventions that promote resilience and improve existing psychosocial oncology services. Future research should focus on longitudinal studies to better understand how resilience evolves and pinpoint factors that can further influence one's resilience. • Resilience can play a positive role in supporting cancer patients. • Cancer patients' resilience can be influenced by personal, relational, environmental, and societal protective factors. • Psychosocial oncology services should consider the integration of resilience programs to promote patients' wellbeing. [ABSTRACT FROM AUTHOR]

The present paper deals with LP-Sasakian manifolds equipped with generalized Tanaka-Webster connection. Here, we have shown that the m-projective curvature tensor and concircular curvature tensor of LP-Sasakian manifold with respect to generalized Tanaka-Webster connection, provided the vector fields are orthogonal to ξ, are linearly dependent if and only if the manifold is an η-Einstein manifold. Later, we have proved that if Mn is n-dimensional ϕ-concircularly flat LP-Sasakian manifold with respect to generalized Tanaka-Webster connection, provided the vector fields are orthogonal to ξ, then Mn is an η-Einstien manifold. [ABSTRACT FROM AUTHOR]

The present paper is an attempt to study the geometrical properties of the concircular curvature tensor on LP-Sasakian manifolds with generalised Tanaka-Webster connection. In this manner, we have shown that if an n-dimensional LP-Sasakian manifold is ξ-concircularly flat with respect to generalized Tanaka-Webster connection, then its scalar curvature with respect to generalized Tanaka-Webster connection vanishes. Also, we have proved that if an n-dimensional LP-Sasakian manifold satisfies the condition, C (X, Y). S = 0, with respect to generalized Tanaka-Webster connection, then the scalar curvature with respect to generalized Tanaka-Webster connection vanishes. [ABSTRACT FROM AUTHOR]