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Numerous research based on offline Tamil recognition deals only with few Tamil characters since it becomes extremely complicated in distinguishing small variations in large handwritten document. The writer's complexity affects the overall formation of the characters. Such types of complexities are due to discontinuation of structures, unnecessary over loops, variation in shapes as well as irregular curves. This complex issue results in enhanced error value rate. Therefore, to conquer such issues, this paper proposes a novel approach to enhance the offline Tamil handwritten character recognition by utilizing four principal steps: pre-processing, segmentation, feature extraction and classification. For optimal segmentation of Tamil characters, this paper utilizes the Tsallis entropy approach-based atom search (TEAS) optimization algorithm. Then a Newton algorithm based deep convolution extreme learning (DELM) approach is utilized for the extraction and classification of input images. Finally, experiments are carried out for numerous Tamil handwritten recognition-based approaches. The proposed Tamil character recognition utilizes the datasets of isolated Tamil handwritten characters established by HP lab India to evaluate the efficiency of the system. [ABSTRACT FROM AUTHOR]

Global warming caused by CO2 emissions will directly harm the health and quality of life of people. Accurate prediction of CO2 emissions is highly important for policy-makers to formulate scientific and reasonable low-carbon environmental protection policies. To accurately predict the CO2 emissions of the world's major economies, this paper proposes a new fractional grey Bernoulli model (FGBM(1,1, t α )). First, this paper introduces the modeling mechanism and characteristics of the FGBM(1,1, t α ) model. The new model can be transformed into other grey prediction models through parameter adjustment, so the new model exhibits high adaptability. Second, this paper employs four carbon emission datasets to establish a grey prediction model, calculates model parameters with three optimization algorithms, adopts two evaluation criteria to evaluate the accuracy of the model results, and selects the optimization algorithm and model results that yield the highest model accuracy, which verifies that the FGBM(1,1, t α ) model is more feasible and effective than the other six grey models. Finally, this paper applies the FGBM(1,1, t α ) model to predict the CO2 emissions of the USA, India, Asia Pacific, and the world over the next 5 years. The forecast results reveal that from 2020 to 2024, the CO2 emissions of India, the Asia Pacific region, and the world will gradually rise, but that in USA will slowly decline over the next 5 years. [ABSTRACT FROM AUTHOR]

Objective: Due to the alarming rise in COVID-19 cases every day in India, numerous academics are already developing a variety of math-based estimation models to forecast the pandemic future course. This paper makes use of publicly available data to anticipate certain COVID-19 trajectories in India. Methods: We used the Auto-Regressive Integrated Moving Average Model, a time series model, to anticipate the number of COVID-19 infected cases every day soon. People need to consume nutritious food that is well-balanced and contains the right number of calories, nutrients, as well as vitamins for strong development, bearing in mind that sustaining and repairing bodily tissues is the goal while preventing unfavorable illnesses and disease. Results: Recent studies have shown that healthy eating can help lower the possibilities of developing cancer, cardiovascular disease and for COVID patients. Therefore, an optimization strategy and LINDO software was used to solve the model. This study offers an exhaustive daily meal plan created especially for hospital patients, acting as a helpful resource for school administrators. With six different food choices, the diet plan makes sure that you get all the nutrients you need each day at a reasonable price. Conclusions: The model is solved using LINDO software in the study, which shows how effective it is when compared to other heuristic techniques like biological algorithms. It is determined through thorough investigation that the chosen meals are both financially and nutritionally feasible to serve in ho spital settings. Hospital patients make up the study's participants, and each day's total cost comes to Rs109.34. This cost makes it possible to deliver meals that are minimally more costly but of higher quality, improving the patients' overall nutritional value. [ABSTRACT FROM AUTHOR]

Agriculture and its allied sector provide the livelihoods of vast rural areas in India. It also plays a vital role in the growth of developing countries like India. Rice, one of the primary components of Indian agriculture plays a significant role in the food security of the country and the world. However, it is stricken by many diseases that affect the quality and quantity of rice yield. This paper proposes a custom CNN architecture for detecting and classifying common diseases found in rice plants by reducing the number of parameters associated with the network. The proposed CNN architecture has been trained using a dataset of four types of common rice plant diseases. In addition, 1400 on-field images of a healthy rice leaf image dataset have been introduced in the paper for the detection of disease-free plants as well. Independent experiments were carried out with and without the inclusion of the healthy leaf image dataset. The performance of the proposed model is evaluated with the use of Stochastic Gradient Descent with Momentum (SGDM) and Adaptive Moment Estimation (Adam) optimization techniques using several performance matrices. Experimental results from the dataset for the classification of four rice plant diseases show that the model using SGDM optimization gives maximum accuracy of 99.66% and the model using the Adam optimization gives maximum accuracy of 99.83% on the test set in the 7th epoch respectively. However, the model with Adam optimizer performed better than the model with SGDM optimizer when the healthy leaf image dataset is included giving a maximum accuracy of 99.66% and 97.61% in the 7th epoch respectively. [ABSTRACT FROM AUTHOR]

With the increasing hazard to the environment and progressions in renewable energy technologies, hybrid renewable energy systems can meet the energy demand. Total dependability can be attained by adding battery banks to hybrid systems. This paper aims to optimally design a multi-source grid isolated hybrid generation system for future smart cities in the state of Tamil Nadu, India. This off-grid power generation consists of wind turbine generators, photovoltaic panels, inverters, diesel generators, and batteries. The design intents at minimizing Net Present Cost, Unmet Load, and CO2 emissions which are conventionally contradictory to each other. This paper analyzes the optimal off-grid combination of components and control strategies, utilizing improved Hybrid Optimization using the Genetic Algorithm. The results obtained portrays that a mix of hybrid renewable energy generators at off-grid locations without diesel generators can be a cost-effective choice of new smart cities, and it is sustainable, environmentally viable, and techno-economically feasible. Subvention is imperative to take on the large-scale system due to the high capital cost. [ABSTRACT FROM AUTHOR]

The rainfall prediction is one of the major problems in meteorological department since it is related to the economy and human life. In addition, the heavy rainfall is the major problem of natural disasters like drought and flood. The accuracy of the rainfall forecasting has been spotted for countries like India and economy is greatly dependent by agriculture. This paper introduces a new rainfall prediction model consists of Feature extraction and Prediction. The features are extracted by modified Empirical Mean Curve Decomposition namely Empirical Mean Median Curve Decomposition, and then subjected to statistical analysis. The results of the statistical features are processed by the classification process. Neural network (NN) is used for the rainfall prediction. To improve the accuracy of proposed work, an optimal weight is and predicted result are multiplied, then, the weight in NN is optimally selected by a Rider Exploitation based Whale Optimization Algorithm. Finally, the performance of the proposed work is compared and proved over the state‐of‐the‐art models in terms of prediction rate. [ABSTRACT FROM AUTHOR]

This paper examines the application of surface drilling parameters and drilling optimization techniques, such as mechanical specific energy (MSE) and equivalent strength (EST), in detecting hydrate gas-bearing sediments during drilling operations. Gas hydrates are usually detected from 3D seismic imaging and later confirmed with Measurement-While-Drilling (MWD)/Logging-While-Drilling (LWD) data and collected core samples. Here, we describe an analysis of the time-based surface drilling parameters recorded from two wells drilled during the International Ocean Discovery Program (IODP) Expedition 372A offshore of New Zealand and the Indian National Gas Hydrate Program Expedition 02 (NGHP-02) offshore of India. The investigation revealed that drilling parameters, as well as MSE/EST methods, can and should be used to monitor and optimize the drilling process and to detect lithological/tectonic (fractures, fault zones, rock hardness, etc.) changes in the drilled substrata and signs of the dynamic changes in the downhole environment (tool vibration, washouts, pack-offs, etc.). However, surface drilling parameters with MSE models cannot explicitly determine the hydrate gas-bearing sediments. This qualitative analysis of whether the gas-bearing sediments consist of hydrates can only be accomplished with the use of the MWD/LWD suite, preferably located as close as possible to the bit. [ABSTRACT FROM AUTHOR]

This paper proposes a fuzzy interval optimization approach to solve the Environmental/ Economic Dispatch (EED) problem with uncertain parameters in the constraints and the objective functions. The objective functions considered are fuel cost and the gaseous emissions of the generating units. Two different types of fuel cost functions are considered in this study, namely, the conventional quadratic function and the augmented quadratic function to introduce more accurate modeling that incorporates the valve loading effects. The latter model presents non-differentiable and nonconvex regions that challenge most gradient-based optimization algorithms. In the proposed approach, objective functions are fuzzified and integrated to represent the fuzzy decision value. On the other hand, load uncertainties are modeled using fuzzy intervals. This fuzzy EED problem formulation provides a modeling flexibility, relaxation in constraints and allows the method to seek a practical solution. The obtained fuzzy multi-objective optimization problem is solved using non-dominated sorting genetic algorithm-II, known for its global searching capabilities, to get the best compromise among all the objectives. The performance of this solution is examined and applied to the standard IEEE 30-bus six-generator test system and the Indian power network of 82-bus by comparing its results with that of the existing methods. Different cases with different complexities have been considered in the study reported in this paper. [ABSTRACT FROM AUTHOR]

Summary: Distributed energy resources (DERs) are a better option for fulfillment of load demand near the load centers. Optimal location and rating of DERs plays a vital role in the enhancement of distribution system performance in terms of power loss reduction, voltage profile enhancement, environmental friendliness, increasing reliability, and postponement of system upgrading. This paper addresses the Gbest‐guided artificial bee colony (GABC) optimization technique for the optimal integration of renewable energy sources (RESs) and electric vehicles (EVs) in distribution feeder to improve the power loss reduction, voltage level, cost reduction, and emissions reduction under load demand uncertainty. In this work, mathematical models of RESs, that is, wind turbine generation system (WTGS) and photovoltaic generation system (PVGS), are employed to determine the generated output power. The foremost aim of this research work is simultaneous abatement of total active power loss, entire cost, and emissions generation; therefore a multiobjective‐based fitness function is formed. Moreover, the impact of charging/discharging behavior of EVs on the distribution grid has also been considered in this study. The proposed methodology is applied on two test systems: (a) 28‐bus practical rural distribution network located at Kakdwip zone in the province of West Bengal, India, and (b) standard 69‐bus distribution feeders. Finally, from the achieved numerical outcomes, it is analyzed that, GABC algorithm generates better outcomes as compared to other optimization techniques. This indicates the viability and effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

This paper presents optimal power flow management in real time for grid connected photovoltaic (PV)–battery system. The objective is the real time, dynamic and optimal scheduling of battery storage which reduces grid power consumption and achieves the peak load shaving. For this purpose, two strategies are developed; one is rule-based and other is optimization algorithm based. Two strategies are compared, pros and cons of those strategies are established. Though rule-based technique is simple & easy to implement, the optimization technique provides more optimal regulation of battery which provides the lesser electricity cost. Forecasted PV/load powers are used to get optimal value of battery power. In order to handle uncertainties due to forecasted data, fuzzy-based corrective strategy is developed to modify the reference battery power as per the present values of PV/load powers. The proposed analysis is carried out for over one exemplary day. The simulations are presented using typical load/PV data of India and Finland as a part of research collaboration. Experimental results are also carried out using proposed scheme. [ABSTRACT FROM AUTHOR]

The Widening and strengthening of Existing National Highway Development In India has been executed by National Highway Authority of India, Ministry of Road Transport and Highways (MORT&H), Government of India. Existing road condition, traffic volume and composition to predict road deterioration as per the urban and rural road conditions was not so satisfactory in terms of IRI value. The Highway Development & Management (HDM) software simulates total life cycle conditions and costs for an analysis period under a user specified scenario of circumstances. A network economic evaluation is the most challenging use of the model, but the effort is well justified due to the potential savings on transportation costs achieved by comparing various alternatives and performing a budget constraint optimization. The methodology used by the author calls for HDM analysts to first identify the possible road works and then determine which road works to evaluate for each road class as a function of the traffic and other characteristics of the road class. For example, the works selected could be: (i) preventive treatment or surface treatment works for roads in good condition, (ii) surface treatment or asphalt concrete resurfacing works for roads in fair condition, and (iii) strengthening or reconstruction works for road in poor condition. It is important that the selected road works per road class be feasible from a purely technical point of view to produce realistic results.HDM allows for the definition of condition-responsive and scheduled project alternatives, for example, the maintenance schedule can be set up to execute an overlay when roughness reaches 4.0 IRI or execute an overlay every 15 years. [ABSTRACT FROM AUTHOR]

This paper emphasizes the usage of Retired Electric Vehicle Batteries (REVB) to support a system with a Centralized Charging Station (CCS) for charging electric vehicle batteries. This paper also focuses on power flow from the Grid to the Centralized Charging Station (G2CCS), power flow from solar PV to the Centralized Charging Station (PV2CCS), PV to the Grid (PV2G), and power flow from Retired Electric Vehicle Batteries to the Centralized Charging Station (REVB2CCS), Retired EV batteries to the Grid (REVB2G), various schemes for EV's in India, and barriers to the adoption of EV respectively. The review of the modelling and optimization is mainly based on retired EV batteries for EVs charging with recent trends, and future possibilities of retired EV batteries are also discussed. This review paper provides valuable references for researchers and assists them in continuous improvement in this field. [ABSTRACT FROM AUTHOR]

In this study, we focus on ripple effect mitigation capability of the Indian pharmaceutical distribution network during disruptions like COVID-19 pandemic. To study the mitigation capabilities, we conduct a multi-layer analysis (network, process, and control levels) using Bayesian network, mathematical optimisation, and discrete event simulation methodologies. This analysis revealed an associative relationship between ripple effect mitigation capabilities and network design characteristics of upstream supply chain entities. Using stochastic optimisation and Lagrangian relaxation, we then find ideal candidates for regional distribution centres at the downstream level. We then integrate these downstream locations with other supply chain entities for building the network optimisation and simulation model to analyse overall performance of the system. We demonstrate utility of our proposed methodology using a case study involving distribution of N95 masks to ‘Jan Aushadhi’ (peoples’ medicines) stores in India during COVID-19 pandemic. We find that supply chain reconfiguration improves service level to 95.7% and reduces order backlogs by 10.7%. We also find that regional distribution centres and backup supply sources provide overall flexibility and improve occupational health and safety. We further investigate alternate mitigation capabilities through fortification of suppliers’ workforce by vaccination. We offer recommendations for policymakers and managers and implications for academic research. [ABSTRACT FROM AUTHOR]

Electrical energy is one of the key components for the development and sustainability of any nation. India is a developing country and blessed with a huge amount of renewable energy resources still there are various remote areas where the grid supply is rarely available. As electrical energy is the basic requirement, therefore it must be taken up on priority to exploit the available renewable energy resources integrated with storage devices like fuel cells and batteries for power generation and help the planners in providing the energy-efficient and alternative solution. This solution will not only meet electricity demand but also helps reduce greenhouse gas emissions as a result the efficient, sustainable and eco-friendly solution can be achieved which would contribute a lot to the smart grid environment. In this paper, a modified grey wolf optimizer approach is utilized to develop a hybrid microgrid based on available renewable energy resources considering modern power grid interactions. The proposed approach would be able to provide a robust and efficient microgrid that utilizes solar photovoltaic technology and wind energy conversion system. This approach integrates renewable resources with the meta-heuristic optimization algorithm for optimal dispatch of energy in grid-connected hybrid microgrid system. The proposed approach is mainly aimed to provide the optimal sizing of renewable energy-based microgrids based on the load profile according to time of use. To validate the proposed approach, a comparative study is also conducted through a case study and shows a significant savings of 30.88% and 49.99% of the rolling cost in comparison with fuzzy logic and mixed integer linear programming-based energy management system respectively. • The article proposes a hybrid MG with renewable energy sources and energy storage systems (battery and fuel cell). • Develop a novel energy management system using modified grey wolf optimizer (MGWO) with multi objective functions. • Analyzes the load demand and the cost of electricity from the power grid for effective designing of MG at test locations. • A significant savings of 30.88% and 49.99% of the rolling cost in comparison with FL and MILP based EMS respectively. [ABSTRACT FROM AUTHOR]

Several researchers have modified the original Social Force Model (SFM) for pedestrians by including vehicles and simulating interaction behaviors in shared space layouts. The present study is conducted at a signalized intersection where the performance and functionality are defined by spontaneous order. The SFM was modified, and two-wheelers were added along with cars and pedestrians to analyze their interactions. The Hybrid Calibration Method was adopted to calibrate the interaction parameters utilizing an optimization technique and real-time data from the study site. It can be concluded from the parameter values that the strength of interaction reduced as the range between the road agents increased. The findings can aid urban planners in visualizing the interaction behavior of pedestrians, cars, and two-wheelers. Moreover, the feasibility of introducing the shared space concept in India as an alternative to the conventional traffic planning system can also be determined during the designing phase. [ABSTRACT FROM AUTHOR]

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India's expanding population has necessitated the development of alternate transportation methods with electric vehicles (EVs) being the most indigenous and need for the current scenario. The major hindrance is the undue influence on the power distribution system caused by incorrect charging station setup. Renewable Energy Sources (RES) have a lower environmental impact than the non-renewable sources of energy and due to which Plug-in Hybrid Electric Vehicles (PHEV) charging stations are installed in the highest-ranking buses to facilitate their effective placements. Based on meta-heuristic optimization, this study offers an effective PHEV charging stations allocation approach for RES applications. The primary objective of the developed system is to create a charging network at a reasonable cost while maintaining the operational features of the distribution network. These troublesare handled by applying meta-heuristic algorithms and optimum planning based on renewable energy systems to satisfy the outcomes of the variables. As a result, by adding charging station parameters, this research proposes to conceptualize the distribution of optimal charging stationsas multiple-objectives of the problem. Furthermore, the PHEV RES and charging station location problem is handled in this study by deploying a novel hybrid algorithm termed as Atom Search Woven Aquila Optimization Algorithm (AT-AQ) that includes the ideas of both Aquila Optimizer (AO) and Atom Search Optimization (ASO) Algorithms. In reality, Aquila Optimizer is a unique population-based optimization approach energized by Aquila's behaviour when seeking prey and it solves the problems of slow convergence and local optimum trapping. According to the findings of the experiments, the proposed model outperformed the other methods in terms of minimized cost function. [ABSTRACT FROM AUTHOR]

Oral cancer is a frequent and challenging cancer that has a high fatality rate. It is the fifth most common cancer in India, with 130,000 deaths each year. There are various diagnostic techniques for oral cancer, but they are limited to accurately detecting cancer cells. As a result, deep transfer learning algorithms such as ResNet50, MobileNetV2, VGG19, VGG16, and DenseNet were applied to non-malignant and oral cancerous photographs acquired from histopathologic and real-time datasets in this study. The images were first preprocessed with techniques like Gaussian blur to eliminate noise signals. Then, features were extracted with morphological operations to produce extreme points on the images, subsequently cropped. The retrieved pictures were fed into deep transfer learning models along with optimization techniques as ADAM, SGD, and RMSprop optimizers to obtain optimal results. Accuracy, loss, RMSE, precision, and AUC were used to analyze the findings. During the validation phase, the hybrid optimization techniques using ADAM, RMSprop, and SGD on DenseNet produced effective results, with accuracy of 92.41%, loss of 0.70, and RMSE of 0.29 for the real-time dataset, accuracy of 95.41%, loss of 0.10, and RMSE of 0.03 for oral cancer images, and accuracy of 92.41%, loss of 0.30, and RMSE of 0.09 for non-cancerous images taken from the histopathologic dataset. [ABSTRACT FROM AUTHOR]

Summary: Nowadays the whole world is striving to achieve energy security and sustainable development through sustainable utilization of cleaner energy resources. But due to the rapidly rising population caused by the unprecedented urban development, it has created imbalances of energy supply and demand. Hybrid energy systems have emerged as a promising source to augment existing energy infrastructure for sustainable energy supply and to reduce the impact of challenges posed by the energy scarcities. There have been continuous requests from the urban sector for additional energy resources, but the energy generation involves a complex phenomenon. In India also, there are abundances of rising energy demands from various sectors over the regions, and these demands cannot be fulfilled further due to the limitations of the energy generation grids. As a result, an approach is required to offer additional energy resources beyond the use of diesel generator (which increases environmental pollution). Recently due advancement in hybrid energy systems consisting of non‐renewable & renewable or renewable & renewable or non‐renewable & non‐renewable, it provides a scope to understand the various configuration of the hybrid energy system for past trends, present status and future scenarios. The investment in energy technologies is critical to be evaluated with techno‐economic feasibility analysis for resource efficiency optimization. Hence, the main objective of this work is to propose an optimal system configuration for urban residential standalone hybrid energy systems to fulfil the electrical energy requirement, which is technically and economically reliable & feasible. The current work accounts for scaling up the existing energy system with proper implementation. The simulations results show that expected average energy requirements of 5 kWh/day with a peak of 818 W for a typical household can be met with proposed configuration (with components like solar PV, wind turbine, power generator [diesel] and battery backup system). The data simulation and optimization technique for a monthly profile of wind, solar, and excess energy generation over HOMER simulation toolkit offer the potential to generate excess electricity of 19.3%. Techno‐economic assessment of the system shows that the proposed system offers more economical and performance benefits. This study also articulates the need to propose a better energy system structure with basic energy grid extension as a cost‐effective solution for sustainable development. Similar kind of assessment will offer a useful decision support system for tropical countries to guide future researchers in energy studies. [ABSTRACT FROM AUTHOR]

A grid connected renewable energy based hybrid microgrid, incorporating solar PV, biomass generators, and Gravity Energy storage system (GESS) has been proposed in this article. This is done to provide uninterrupted energy to a hilly village of North‐East India. A bi‐directional energy transaction with the central grid is considered for handling uncertainties and to gain monetary benefit through energy sale. The GESS is only charged through solar PV and biomass generators, to reduce the carbon footprint. The outward energy transaction with the central grid helps in reducing the energy cost for consumers inside the microgrid. Transaction price of energy from the central grid is considered from historical data available in the website of Indian Energy Exchange Limited (IEX). For different combinations of loading pattern and GESS initial storage, the Levelized cost of energy (LCOE) is found to vary in between INR 2.71/kWh to INR 3.41/kWh, which is lower than the current Nagaland state government approved tariff of INR 3.55/kWh. Any subsidy from governmental or non‐governmental organizations has not been considered in the present study while calculating the installation cost of Solar PV and Biomass generators to portray the actual scenario. Whale optimization algorithm (WOA) has been used to find the optimal solution of the proposed system. [ABSTRACT FROM AUTHOR]

In this article, a new optimization technique known as Kho-Kho optimization (KKO) algorithm is presented. This proposed technique is a population based meta-heuristic method which is inspired from the strategies used by players in a well known tag-team game played in India, i.e. Kho-Kho. The performance and superiority of the proposed method with respect to other existing methods is evaluated using twenty nine benchmark functions and real-time optimization problems related to power system i.e. combined emission economic dispatch and combined heat and power economic dispatch problem. • This paper presents a new Kho-Kho optimization algorithm (KKO). • KKO is inspired by a well-known tag-team game played in India known as Kho-Kho. • KKO mimics the strategies used by a chasing team to catch the runners of a running team. • Twenty-nine benchmark functions are used to test the performance of the proposed algorithm. • Further, KKO is used to solve real-time optimization problems of power system. [ABSTRACT FROM AUTHOR]

• Objective analysis of psoriasis disease over a larger dataset of 780 images. • Implementation of swarm intelligence algorithms to obtain optimum clusters. • Effective psoriasis lesion detection using four swarm intelligence techniques. • Quantitative analysis of the result using different metrics. • Calculation of computational complexity for each swarm intelligence algorithms. In psoriasis skin disease, psoriatic cells develop rapidly than the normal healthy cells. This speedy growth causes accumulation of dead skin cells on the skin's surface, resulting in thick patches of red, dry, and itchy skin. This patches or psoriatic skin legions may exhibit similar characteristics as healthy skin, which makes lesion detection more challenging. However, for accurate disease diagnosis and severity detection, lesion segmentation has prime importance. In that context, our group had previously performed psoriasis lesion segmentation using the conventional clustering algorithm. However, it suffers from the constraint of falling into the local sub-optimal centroids of the clusters. The main objective of this paper is to implement an optimal lesion segmentation technique with aims at global convergence by reducing the probability of trapping into the local optima. This has been achieved by integrating swarm intelligence based algorithms with conventional K-means and Fuzzy C-means (FCMs) clustering algorithms. There are a total of eight different suitable combinations of conventional clustering (i.e., K-means and Fuzzy C-means (FCMs)) and four swarm intelligence (SI) techniques (i.e., seeker optimization (SO), artificial bee colony (ABC), ant colony optimization (ACO) and particle swarm optimization (PSO)) have been implemented in this study. The experiments are performed on the dataset of 780 psoriasis images from 74 patients collected at Psoriasis Clinic and Research Centre, Psoriatreat, Pune, Maharashtra, India. In this study, we are employing swarm intelligence optimization techniques in combination with the conventional clustering algorithms to increase the probability of convergence to the optimal global solution and hence improved clustering and detection. The performance has been quantified in terms of four indices, namely accuracy (A), sensitivity (SN), specificity (SP), and Jaccard index (JI). Among the eight different combinations of clustering and optimization techniques considered in this study, FCM + SO outperformed with mean JI = 0.83, mean A = 90.89, mean SN = 92.84, and mean SP = 88.27. FCM + SO found statistical significant than other approaches with 96.67 % of the reliability index. The results obtained reflect the superiority of the proposed techniques over conventional clustering techniques. Hence our research development will lead to an objective analysis for automatic, accurate, and quick diagnosis of psoriasis. [ABSTRACT FROM AUTHOR]

The Widening and strengthening of Existing National Highway Development In India has been executed by National Highway Authority of India, Ministry of Road Transport and Highways (MORT&H), Government of India. Existing road condition, traffic volume and composition to predict road deterioration as per the urban and rural road conditions was not so satisfactory in terms of IRI value. The Highway Development & Management (HDM) software simulates total life cycle conditions and costs for an analysis period under a user specified scenario of circumstances. A network economic evaluation is the most challenging use of the model, but the effort is well justified due to the potential savings on transportation costs achieved by comparing various alternatives and performing a budget constraint optimization. The methodology used by the author calls for HDM analysts to first identify the possible road works and then determine which road works to evaluate for each road class as a function of the traffic and other characteristics of the road class. For example, the works selected could be: (i) preventive treatment or surface treatment works for roads in good condition, (ii) surface treatment or asphalt concrete resurfacing works for roads in fair condition, and (iii) strengthening or reconstruction works for road in poor condition. It is important that the selected road works per road class be feasible from a purely technical point of view to produce realistic results.HDM allows for the definition of condition-responsive and scheduled project alternatives, for example, the maintenance schedule can be set up to execute an overlay when roughness reaches 4.0 IRI or execute an overlay every 15 years. [ABSTRACT FROM AUTHOR]

Chhana cake, locally termed chhana podo, is a baked traditional dairy product of India. The present study was undertaken for optimization of process parameters pertaining to production of chhana podo. Independent variables, namely, moisture content of feed-mix: 52.5 - 62.5% (wb), baking temperature: 60 - 180°C, baking time: 1 - 9 h and height of feed-mix: 1 - 5 cm were selected heuristically and their effect on dependent variables, namely, hardness, whiteness index, yellowness index, tint of crust and crumb, moisture content and expansion ratio of chhana podo were studied. Although quadratic models fitted to responses exhibited relative deviation percent (Rd) ranging from 1.214 to 5.406%; lack of fit was significant for all responses except crust yellowness index and crust tint. Neural network modeling was adopted (Rd for training = 1.739%, Rd for validation = 1.845%) and relative importance of factors on responses were found. Optimum conditions obtained from genetic algorithm were: moisture content of feed-mix = 57.43% (wb), baking temperature = 151.4°C, baking time = 4.35 h, height of mix = 2.9 cm. [ABSTRACT FROM AUTHOR]