5G, 6G, and beyond networks promise to support vertical industrial services with strict QoS parameters, but the hardware-based "one-size-fits-all" model of legacy networks lacks the flexibility needed for diverse services. The foundation of 5G networks lies in softwarization, with network slicing, Software Defined Networking (SDN), and Network Function Virtualisation (NFV) serving as its core components. The network-slicing-based shared network environment necessitates an intelligent and flexible resource management approach. In this case, traditional approaches are no longer suitable for dealing with a dynamic network environment. With recent advancements, AI-based approaches have the potential to manage resources autonomously. This paradigm shift underscores the need for deep and extensive investigation. However, existing literature on this subject is fragmented and lacks a cohesive overview of network slicing. To address these gaps, our review paper aims to provide a comprehensive scope of network slicing in a unified manner. In this sequence at first, this paper presented a conceptual overview of network slicing and enabling technologies, including SDN, NFV, and edge computing. Secondly, this paper identifies the relevant phases of resource management and presents AI-based resource management for network traffic classification, admission, allocation, and scheduling. Finally, it also discusses the deployment of network slicing-enabled key use cases and their practical deployment, the research gap, and open research challenges. To the best of our knowledge, this is the first attempt to critically analyze and present a consolidated review of the state of the art in network slicing resource management modules and network slicing-enabled key industrial use cases. This paper aims to guide researchers in developing innovative solutions and assist network players in the practical deployment of network slices for industrial applications.

Workload pattern learning-based resource management is crucial for cloud computing environments for achieving higher performance, sustainability, fault-tolerance, and quality of service. The existing literature lacks a comprehensive discussion and metaanalysis of workload pattern learning centered cloud resource management. In this context, this paper presents a first comprehensive study about five pattern learning and analysis-driven techniques applied for achieving higher efficiency and performance during multi-constrained cloud resource management. The paper manifests utility and significance of workload pattern learning-based resource management as compared with traditional resource management. The five principle techniques are thoroughly discussed with coherent depiction of intended concept alongwith numerical illustration. The most prominent state-of-the-art models belonging to each technique are further distinguished based on distinct objectives conferring an extensive survey and comparison. Besides, conceptual and theoretical analysis, the leading models underlying the major resource management techniques are implemented on a common platform and thoroughly examined using real-world Google Cluster workload traces. Based on the all-inclusive study and performance evaluation, trade-off discussion among these techniques are capsuled to put forward imperative concluding remarks with concrete open issues and insightful future research directions.

Virtual Machine Placement involves the selection of the optimal physical machine for deploying a requested virtual machine within extensive cloud data centers. VM placement must be performed strategically by considering different factors of the available resources for optimal exploitation of them. Numerous methods have been devised to address this issue. Nevertheless, existing solutions only account for a restricted set of resource types, leading to an uneven workload distribution that triggers the activation of unnecessary PMs within the data center. This work introduces a Non-dominated sorting genetic algorithm II that integrates various resource-constraint metrics to determine the optimal PMs for deploying VMs in a cloud environment. This algorithm maximizes resource utilization while minimizing the data center’s energy consumption and carbon footprints. The algorithm’s performance assessment is conducted using the Google Cluster Data set, and the outcomes are compared with established methodologies. The results showcase a substantial reduction in energy consumption, carbon footprints, and number of active PMs by 19.29

Cloud computing is flourishing at a rapid pace. Significant consequences related to data security appear as a malicious user may get unauthorized access to sensitive data which may be misused, further. This raises an alarm-ringing situation to tackle the crucial issue related to data security and proactive malicious user prediction. This article proposes a Federated learning driven Malicious User Prediction Model for Secure Data Distribution in Cloud Environments (FedMUP). This approach firstly analyses user behavior to acquire multiple security risk parameters. Afterward, it employs the federated learning-driven malicious user prediction approach to reveal doubtful users, proactively. FedMUP trains the local model on their local dataset and transfers computed values rather than actual raw data to obtain an updated global model based on averaging various local versions. This updated model is shared repeatedly at regular intervals with the user for retraining to acquire a better, and more efficient model capable of predicting malicious users more precisely. Extensive experimental work and comparison of the proposed model with state-of-the-art approaches demonstrate the efficiency of the proposed work. Significant improvement is observed in the key performance indicators such as malicious user prediction accuracy, precision, recall, and f1-score up to 14.32%, 17.88%, 14.32%, and 18.35%, respectively.

This study examines the influence of planting mixture variations on the quality of the percolated water of the rain garden with and without plants. Six planting mixtures in experimental rain gardens have been used. It has been noted that pollutant removal efficiency of RG can exhibit variations based on specific parameters. Notably, RG6, utilizing a planting mix of 75% topsoil and 25% compost, demonstrated the highest performance. These results draw attention to the critical role of the specific planting mixtures in influencing the performance of vital parameters related to pollutant removal. The observation shows that RG5 exhibits exceptional removal efficiency in pH, Total Suspended Solids (TSS), Biological Oxygen Demand (BOD), and Chemical Oxygen Demand (COD), and RG6 performs best in electrical conductivity (EC), Total Dissolved Solids (TDS), Total Nitrogen (TN), and Total Phosphorus (TP) removal. In particular, when analyzing pollutant removal on a surface with Madagascar periwinkle plants, RG6 emerges as the most effective, achieving an impressive efficiency of approximately 49%. For the bare surface, pollutant removal efficiency is 40%. The study outcome will be useful in deciding the composition of the planting mixture, which will keep the rain garden to improve quality and quantitatively hydrological performance, lowering urban flooding magnitude.

Heart disease is one of the main causes of death worldwide; rendering early and accurate detection is crucial for efficient care. The research presented here suggests a clinical and demographic data-driven machine learning strategy for predicting the existence of heart disease in patients. Several classification techniques, including logistic regression, decision trees, K-nearest neighbors, and support vector machines, were implemented within the ensemble framework by using a publicly accessible dataset of patient information. Metrics like accuracy, precision, recall, and F1 score have been employed to evaluate the performance of the proposed model. The suggested ensemble model’s highest accuracy of 94.95

Numerous mathematical models were developed for flood hazard mapping, flood inundation modeling and flood risk assessment. This study aims to have a comprehensive literature review of various mathematical models available in literature for flood modeling. For this study a total of 42 research articles are reviewed from year 1995 to 2020. The assessment is carried out on the basis of the model's dimensionality i.e.; one dimensional (1-D), two-dimensional (2-D), coupled 1-D/2-D or three-dimensional (3-D), and numerical solutions available in literature i.e.; Finite element, Finite difference, Finite volume or some others methods for the models. The study reviewed the literature for flood modeling and prepared a table of various models used for flood modeling. It was concluded that coupled models are more preferable than other models because they had the strength of both 1-D and 2-D models and computationally efficient with less computational time. For open channel modeling models based on finite difference method are preferred over numerical solution techniques.

In water resources planning and management, computer-based models for rainfall-runoff simulation are recognized as powerful tools. The southwest monsoon brings abundant rain to the Krishna basin as the monsoon winds make landfall on the Indian peninsula. With multiple hydrological interventions impacting the basin and causing severe flooding, the need for a reliable flood forecasting model cannot be overstated. Such a model facilitates efficient flood management and enhances community readiness for extreme weather events. In developing countries, integrating rainfall-runoff and hydrodynamic models for flood simulations has been rarely used due to a lack of data availability. Integrating rainfall-runoff and hydrodynamic models leads to better flood estimation, enhancing cohesive flood management strategies. The current study aims to develop an integrated Rainfall-Runoff (RR) and Hydrodynamic model for the Krishna River sub-basin in India. The study utilized the MIKE + RDI model for rainfall-runoff modeling in conjunction with the River network model for hydrodynamic modeling to develop a coupled model. The digital elevation model (DEM), gridded rainfall over the sub-basin, evaporation, discharge, and water level data for 2005 and 2006 were input parameters to develop and calibrate the coupled model and validated for 2009 data. Various performance assessing criteria such as coefficient of determination (R2), mean absolute error (MAE), index of agreement (d), Nash–Sutcliffe efficiency (NSE), and percentage deviation in peak were used to evaluate the model accuracy. The validation results demonstrate a decent agreement between the observed and simulated water levels, with R2 of 0.90, MAE of 1.24, d of 0.75, NSE of 0.52, and a percentage deviation of peak at − 2.82

Floods are catastrophic natural disasters that cause a substantial toll on human lives, infrastructure, and the economy. Structural and non-structural measures are developed for planning flood mitigation strategies. Flood inundation mapping is valuable information for decision-makers and authorities to develop flood mitigation strategies and resource allocation. This study uses the HEC-RAS 2D model for flood inundation mapping in the Krishna River Basin. Digital elevation models (DEMs) of 12.5 and 30 m resolutions were used to model the inundation map. The study also investigated the effect of change in upstream boundary data on the inundated area. The simulated results with 12.5 m resolution DEM are found in good agreement with the validation data and conform to the inundated areas with the available reports. This study proves the 2D capabilities of HEC-RAS and helps the experts with better management practices.

Cancer is one of the painful diseases which is the most cause of the death. If we could predict the cancer in the early stage then not only, we can save the human life as well as we can escape a very painful process of a cancer patient. If we could detect cancer in early stage then the diagnosis of the cancer will be easy and there is less probability of any loss of the human life. For detecting the cancer in early stage, we can use various image processing techniques. This paper review various kinds of cancers and the algorithms which are used for recognition of the particular type of cancer. We will focus on machine learning based algorithms and also analyses different processes which need to be followed during the diagnosis of the cancer.

Flood is the most catastrophic event occurring in developing countries which causes heavy toll on the economy and human lives. The destruction caused by the event can be minimized by prior information of flood magnitude, its extent, and time of occurrence, as timely action can be taken by planners and managerial authorities. In developing countries, due to data scantiness, this information is inaccessible. This study demonstrates the use of 30 m and 90 m resolution Shuttle Radar Topographic Mission Digital Elevation Models to perform 1 D hydrodynamic modeling using MIKE Hydro (River) for Krishna River Basin, India. The research proposes a new methodology to improve flood magnitude estimates using modified cross sections with limited data. The analysis employs two flood events of the Krishna river basin that occurred in years 2006 and 2009 for simulation. The model parameters were calibrated using data from 2006, and the results were validated utilizing the data from year 2009. Manning’s Roughness coefficient ( n ) was used as simulating parameter to perform hydrodynamic modeling. The flood magnitude estimates procured with the modified cross sections are encouraging as model performance indices viz. Correlation coefficient, root mean square error, Index of agreement ( d ), Nash Sutcliffe efficiency, and percentage deviation in peak (% deviation) are improved significantly.

One-dimensional hydrodynamic models overestimate river cross-section derived from freely available SRTM DEMs. The present study aims to minimize the overestimation of river flow. DEM-extracted cross-sections obtained from 30 m and 90 m resolutions show higher elevation values than the actual river cross sections of Krishna and Bhima rivers, India. To minimize the overestimation of the river flow, DEM-extracted cross-sections are modified using known cross-section of the river. The corrections for cross sections extracted from DEM, are obtained by subtracting the DEM-derived cross-sections from a known cross-section of the river. Monsoons flows that occurred in years 2006 and 2009 in Krishna and Bhimariver have been used for modeling. The MIKE HYDRO River model performance with modified DEM-extracted cross-sections of river improves as the correlation coefficient, root mean square error, index of agreement, Nash Sutcliffe efficiency & Percentage deviation in peak (%) values are improved.

The study attempts to explore the onset of monsoon in the Haryana region during (1997-2014) in monsoon season (June-September). The onset of summer monsoon indicates the beginning of summer monsoon, and thus, a valuable input for many applications. In this work, daily rainfall data is analysed and the onset of monsoon is determined in the Haryana, and the selected cities of Haryana (Yamunanagar, Kurukshetra and Panchkula) is determined. The onset of the Indian Summer Monsoon (ISM) is based on the agronomic need that ensured sufficient soil moisture in the subsequent days after the local onset for the germination of the planted seeds. The definition was such that at least a 5-day wet sequence of >= 1 mm per day followed by a 10-day dry spell in the 30 days following the first rains. Mann-Kendall test is applied to detect a trend in the onset of monsoon and Sen's slope estimator for the determination of slope. The results demonstrate an increasing or decreasing trend in the onset of monsoon in the cities of Haryana within the year.

Rain gardens or bioretention systems are the best storm water management practices, which use natural processes of the hydrological cycle such as infiltration and evapotranspiration. Rain gardens were first coined for residential use in 1990 in Prince George’s County, Maryland which was an alternative to the conventional system of sidewalks and gutters. However, countries like Japan, China, Australia and U.S.A. are encouraging the use of rain garden these days for the sustainable development of the country. From the last few decades, the world is witnessing harmful results of urbanization. This has led to a rapid increase in impervious land cover and deterioration of the quality of the ecosystem. The impervious surface of concrete and asphalt seal the soil layers and causes excessive surface runoff, which leads to the problem of urban flooding. Also, chemical and oils falling from vehicles on roads get washed away with storm water and enters the natural water bodies leading to their pollution. Rain garden reduces and delays the flood peaks as well as helps in groundwater recharge and enhances the biodiversity. Moreover, its vegetation works as a filter media for storm water treatment. Rain garden is a low impact development (LID) technique having a long-term performance and is aesthetically pleasing. This paper quotes the benefits and chronological order of implementation of bioretention systems in urban cities having the problem of storm water management with an aim to create awareness among scientific communities.

The study attempts to explore rainfall pattern characteristics in the Haryana region during (1997–2014) Kharif season. In this work, daily and seasonal variations of rainfall along with the determination of dry spells (interval between two wet spells of 7 days magnitude with at least 25 mm of rain) and wet spells (a period of number of consecutive days on each of which precipitation exceeding a specific minimum amount has occurred) during the specified time period have been studied. Mann–Kendall test is applied to detect trend and Sen’s slope estimator is for the determination of slope. Results demonstrate that monthly maximum and total rainfall have positive trend and there is a strong spatial relationship in their variability. The increase of monthly precipitation is mainly associated with the increase of frequency and intensity of heavy precipitation during Kharif season. The variation of precipitation is likely to increase flood and drought risk.

An optimal quantum-dot cellular automata (QCA) design for full adder (FA) based on an optimal three-input exclusive-OR (XOR) gate is presented. This XOR structure utilises a new configuration of cells unlike traditional gate-level approaches. The coplanar QCA FA spans over and delays of 0.5 clock cycles with 40 cells. It achieves total energy dissipation as low as 0.144 eV at 1.5 energy level. The utility of proposed gate is leveraged to design a ripple-carry adder (RCA) as a specific application. For performance evaluation, the authors use traditional cost metrics and QCA-specific cost function. Results show that proposed n -bit RCA outperforms most of the best state-of-the-art designs known in the literature. For example, cell count (area consumption) of 4, 8, and 16 bit adders is 62% (70%), 66% (84%), and 70% (86%) less than the best coplanar RCA design results. In addition, by taking the new cost metrics into account, it is found that proposed adder performs fairly well as compared to the previous adders too. These designs are realised and simulated using QCADesigner.

This paper proposes an efficient design for testability technique for reversible logic circuits, which promises ultralow energy computation.

: - In reversible watermarking algorithm for medical images, the original image will be extracted back. So that it is broadly used in the medical image protection. It is proposed in this paper is new reversible watermark medical image scheme based on integer discrete cosine transform and difference expansion. The integer discrete cosine transformation is not only stay away from truncation error dissertation and but it is also lead to a faster processing speed hardware is easily handle implement. This scheme is firstly divided new image in to some non overlapping blocks after then used blocks in difference expansion embedding and extracting algorithm. This energy is less than some predefined thresholds .If it is medical image is order to satisfy the reversible underflow and overflow of data is considered and prevented. It is to end with experimental results point up that the proposed algorithm has been possible applications in medical image processing.

Coalition formation (CF) creates a coherent group between autonomous agents to efficiently achieve individual or collective goals. However, the idle agents are not able to maximize their utility. In this paper, the weighted voting mechanism (WVM) has been proposed that allows agents to join existing coalitions. The experiments endorsed agents' number and the trust element as parameters to study the voting result percentage. The simulation of the WVM shows the complete voting session percentage which is a measurement of agents' voting efficiency has been reduced to 26.4% in a society of 500 agents. Utilizing the WVM, the voting session of the coalition in deciding agents' joining coalition request (JCR) can be reduced and increase the efficiency of the decision making.