Population increase has resulted in an increase in the worldwide demand for alternative fuels due to depleting resources. There is a periodic increase in concern about the engine performance, pollutant emissions, and their predictions, from an engine using biodiesels. The use of intelligent algorithms in modeling and forecasting alternative fuels characteristics and their performance in engines are critically reviewed in this study. The paper aims at demonstrating with artificial intelligence methodologies the main conclusions of the recent research done for the above topic from 2012 to 2020. This article attempted to demonstrate an exploratory examination of the adaptive neuro-fuzzy inference system (ANFIS) soft computing technique used for the exact measurement and analysis of engine performance, emissions of exhaust engines when biodiesel is used as an alternative fuel. Additionally, the yield of biodiesel and their different characteristics predicted using ANFIS are also reviewed. Integration of particle swarm optimization (PSO), genetic algorithm (GA), and response surface methodology (RSM), either for comparison or optimization with ANFIS is presented. The summary of all studies is provided in tabular form. For the demonstration purpose, the ANFIS studies predicting different biodiesel and engine characters are provided with illustrative figures. The ANFIS prediction related to biodiesel used engine and biodiesel self-characteristics is found to be excellent. The ANFIS accuracy reported is better than the artificial neural network (ANN) accuracy. A minimum of 0.9 R 2 value is generally obtained which is around 5% greater than the ANN modeling results reported.However, the ANFIS predictions are much more fitter than the RSM predictions. The integration of ANFIS-PSO and ANFIS-GA provided much more optimized results.

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Current study portrays a novel method for counterfeit prevention and brand security in the FMCG business. The introduced approach consolidates Internet of Things, Cloud, and Mobile advancements with the utilization of specially crafted savvy labels (smart tags) applied to each product to give track and follow capacities. The smart labels join QR code with extra data printed with an imperceptible photochromatic ink. The labels are enacted by spotlight on cell phones during the checking. Prior to checking, clients are provoked to choose the setting of the products (available, sold, and consumed) to give extra data about each product container as it travels through the inventory network. Consumer family types reveals essential information on family types and roles in selecting the product for purchasing. The statistical analysis of family type is 84.7 and 15.2 percentage (out of 354 members) in nuclear and joint family respectively. Awareness percentage on smart tags is 49.15, 32.17 and 18.07 percentage in consumer awareness, unaware and may be respectively. Analysis of fake products identification on smart tags is 53.67, 27.96 and 18.36 percentage in consumer identification, unidentified, and may be respectively. Counterfeit information on products identification on smart tags is 97.74, 2.259 percentage in counterfeit information obtained by consumer is higher than not obtained consumers. Analysis of benefit percentage on products is 94.63, 5.37 percentage in benefit percentage is higher than non-obtained consumers. Likeliness of IoT -Smart Tags on products is 76.28, 23.72 percentage (out of 354 members) in interested percentage is higher than not interested consumers.

In this chapter, the authors discuss the utilization of e-waste in the concrete for civil construction activities. Various tests have been used to investigate the effects of e-waste mixed with concrete. The various percentages of e-waste have been mixed with concrete to improve the strength of buildings. An e-waste concrete beam has a maximum tensile strength of 6.23 MPa under sulfuric curing conditions, and the highest flexural strength at 10% e-waste replacement during the hydrochloride curing process. The compressive strength is at its highest value when e-waste replaces 10% of it. After 28 days of curing, the concrete cylinder's maximum split tensile strength was 15%. Thus, the e-waste could be effectively utilized for civil construction purposes to reduce its environmental impacts.

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