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Internet of Things (IoT) is a network, which provides the connection with various physical objects such as smart machines, smart home appliance and so on. The physical objects are allocated with a unique internet address, namely, Internet Protocol, which is used to perform the data broadcasting with the external objects using the internet. The sudden increment in the number of attacks generated by intruders, causes security-related problems in IoT devices while performing the communication. The main purpose of this paper is to develop an effective attack detection to enhance the robustness against the attackers in IoT.

In this research, the lasso regression algorithm is proposed along with ensemble classifier for identifying the IoT attacks. The lasso algorithm is used for the process of feature selection that modeled fewer parameters for the sparse models. The type of regression is analyzed for showing higher levels when certain parts of model selection is needed for parameter elimination. The lasso regression obtains the subset for predictors to lower the prediction error with respect to the quantitative response variable. The lasso does not impose a constraint for modeling the parameters caused the coefficients with some variables shrink as zero. The selected features are classified by using an ensemble classifier, that is important for linear and nonlinear types of data in the dataset, and the models are combined for handling these data types.

The lasso regression with ensemble classifier–based attack classification comprises distributed denial-of-service and Mirai botnet attacks which achieved an improved accuracy of 99.981% than the conventional deep neural network (DNN) methods.

Here, an efficient lasso regression algorithm is developed for extracting the features to perform the network anomaly detection using ensemble classifier.

India’s construction sector faces substantial challenges with project delays and cost overruns. According to the Ministry of Statistics and Programme Implementation, an astounding 431 out of 1,820 projects reported cost overruns, whereas 848 projects experienced delays in 2023. These delays and overruns often push costs 30%–40% beyond initial estimates. Blockchain technology (BCT) offers a promising solution by enhancing transparency, streamlining processes through automation and optimising supply chains in the construction industry. This study aims to explore the adoption landscape of BCT within the construction sector, the drivers behind its acceptance and future directions for its implementation.

This study uses a mixed-methods’ research design that combines both quantitative and qualitative data collection and analysis techniques to investigate the current use of blockchain, the perceived rationale and the future direction of blockchain in construction. A survey of construction professionals with substantial blockchain experience was conducted. In addition, an exhaustive literature review and content analysis were conducted using reputable electronic databases such as Scopus, Web of Science and IEEE Xplore, reinforcing the study’s empirical foundation. The survey data was analysed using descriptive and inferential statistics to determine relative importance items using MS-Excel and SPSS.

Despite regulatory challenges, blockchain adoption is on the rise, with 90% of respondents expressing strong interest. Stakeholders see improved transparency and accountability as key drivers for adoption, emphasising blockchain’s capacity to support veracity and trust in construction projects. Notable benefits include enhanced communication, cost reduction, supply chain management and precise execution and monitoring. Overcoming regulatory constraints and bridging knowledge gaps will be essential to fully realising blockchain’s benefits for all players in the construction industry.

The study’s findings provide valuable insights for construction professionals, researchers and policymakers interested in adopting BCT. By clarifying the transformative potential of blockchain within the construction sector, this research study emphasises the imperatives of further study, innovation and collaborative endeavours aimed at addressing construction industry challenges and facilitating digital integration.

This paper explores entrepreneurs' expectation from the new provincial government to protect sociocultural values for promoting touristic city – Pokhara, Nepal.

The purposive sampling technique was applied to source the information from the respondents resided at Pokhara. Structured questionnaire techniques and cross-sectional descriptive method were used to collect data from 393 tourism entrepreneurs to explore their understanding and existing situations.

About 85.5% of the respondents argue that the new government system has affected their business and 58.27% feel procedural complexities due to new political administrative system. Natural beauty, growing pollution, unmanaged urbanizations, good flow of the tourists and sustainability of the touristic city resulted as main aspects of Strength, Weakness, Opportunity and Threats. The entrepreneurs expect that newly formed provincial government should provide business security, formulate appropriate tax policy and provision for business insurance scheme for smooth operation, growth and sustainability of their tourism entrepreneurship.

The research was taken in Pokhara, only the entrepreneurs mostly resided at Lakeside were taken and wider research across the whole city would give a more balanced perspective.

Recommendations are made with the aim of uplifting entrepreneurship milieu in the touristic city Pokhara in order to promote tourism business of Pokhara.

This research can help local authority to take local residents' and entrepreneurs' experience into consideration for creating better plan and policies for the well-being of Pokhara.

This is the first paper to provide data from the perspective of entrepreneurs' expectation from newly formed Gandaki provincial government in Pokhara, Nepal.

This study aims to investigate the optimal process parameters of the wire-cut electrical discharge machining (WCEDM) for the machining of the GZR-AA7475 hybrid metal matrix composite (HMMC). HMMCs are prepared with 2 Wt.% graphite and 4 Wt.% zirconium dioxide reinforced with aluminium alloy 7475 (GZR-AA7475) composite by using the stir casting method. The objective is to enhance the mechanical properties of the material while preserving its unique features. WCEDM with a 0.18 mm molybdenum wire electrode is used for machining the composite.

To conduct experimental studies, a Taguchi L27 orthogonal array was adopted. Input variables such as peak current (I_p), pulse-on-time (T_ON) and flushing pressure (P_F) were used. The effect of process parameters on the output responses, such as material removal rate (MRR), surface roughness rate (SRR) and wire wear ratio (WWR), were investigated. The grey relational analysis (GRA) is used to obtain the optimal combination of the process parameters. Analysis of variance (ANOVA) was also used to identify the significant process parameters affecting the output responses.

Results from the current study concluded that the optimal condition for grey relational grade is obtained at T_ON = 105 µs, I_p = 100 A and P_F = 90 kg/cm². Peak current is the most prominent parameter influencing the MRR, whereas SRR and WRR are highly influenced by flushing pressure.

Identifying the optimal process parameters in WCEDM for machining of GZR-AA7475 HMMC. ANOVA and GRA are used to obtain the optimal combination of the process parameters.