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The internet has completely merged into contemporary life. People are addicted to using internet services for everyday activities. Consequently, an abundance of information about people and organizations is available online, which encourages the proliferation of cybercrimes. Cybercriminals often use malicious links for large-scale cyberattacks, which are disseminated via email, SMS and social media. Recognizing malicious links online can be exceedingly challenging. The purpose of this paper is to present a strong security system that can detect malicious links in the cyberspace using natural language processing technique.

The researcher recommends a variety of approaches, including blacklisting and rules-based machine/deep learning, for automatically recognizing malicious links. But the approaches generally necessitate the generation of a set of features to generalize the detection process. Most of the features are generated by processing URLs and content of the web page, as well as some external features such as the ranking of the web page and domain name system information. This process of feature extraction and selection typically takes more time and demands a high level of expertise in the domain. Sometimes the generated features may not leverage the full potentials of the data set. In addition, the majority of the currently deployed systems make use of a single classifier for the classification of malicious links. However, prediction accuracy may vary widely depending on the data set and the classifier used.

To address the issue of generating feature sets, the proposed method uses natural language processing techniques (term frequency and inverse document frequency) that vectorize URLs. To build a robust system for the classification of malicious links, the proposed system implements weighted soft voting classifier, an ensemble classifier that combines predictions of base classifiers. The ability or skill of each classifier serves as the base for the weight that is assigned to it.

The proposed method performs better when the optimal weights are assigned. The performance of the proposed method was assessed by using two different data sets (D1 and D2) and compared performance against base machine learning classifiers and previous research results. The outcome accuracy shows that the proposed method is superior to the existing methods, offering 91.4% and 98.8% accuracy for data sets D1 and D2, respectively.

As artificial intelligence and machine learning become increasingly integrated into daily life, both individuals and institutions are growing dependent on these technologies. However, it's crucial to acknowledge that such advancements can introduce potential flaws or vulnerabilities. A case in point is the investigation conducted by the non-profit organization ProPublica into the COMPAS (Correctional Offender Management Profiling for Alternative Sanctions) risk assessment tool – a tool widely used by US courts to assess the likelihood of a defendant reoffending. To address the issue of underlying biases, including racial biases, which can lead to inaccurate predictions and significant social harm, we are delving into the current literature on algorithmic bias in decision systems. We are also exploring the evolving considerations of fairness and accountability in machine learning. Specifically, within the realm of predictive policing algorithms employed in the criminal justice system, our focus is on recent studies aimed at mitigating biases in algorithmic decision-making. This involves reassessing recidivism rates and implementing adversarial debiasing in conjunction with fairness metrics.

Aluminium silicon carbide reinforced metal matrix composite (Al/SiC‐MMC) materials are rapidly replacing conventional materials in various automotive, aerospace and other industries. Accordingly, the need for accurate machining of composites has increased enormously. The present work analyzes the machining of Al/SiC composites for surface roughness. An empirical model has been developed to correlate the machining parameters and their interactions with surface roughness. Response surface regression and analysis of variance are used for making the model. The developed model can be effectively used to predict the surface roughness in machining Al/SiC‐MMC composites. The influences of different parameters in machining Al/SiC particulate composites have been analyzed through contour graphs and 3D plots.

Cloud computing (CC) refers to the usage of virtualization technology to share computing resources through the internet. Task scheduling (TS) is used to assign computational resources to requests that have a high volume of pending processing. CC relies on load balancing to ensure that resources like servers and virtual machines (VMs) running on real servers share the same amount of load. VMs are an important part of virtualization, where physical servers are transformed into VM and act as physical servers during the process. It is possible that a user’s request or data transmission in a cloud data centre may be the reason for the VM to be under or overloaded with data.

VMs are an important part of virtualization, where physical servers are transformed into VM and act as physical servers during the process. It is possible that a user’s request or data transmission in a cloud data centre may be the reason for the VM to be under or overloaded with data. With a large number of VM or jobs, this method has a long makespan and is very difficult. A new idea to cloud loads without decreasing implementation time or resource consumption is therefore encouraged. Equilibrium optimization is used to cluster the VM into underloaded and overloaded VMs initially in this research. Underloading VMs is used to improve load balance and resource utilization in the second stage. The hybrid algorithm of BAT and the artificial bee colony (ABC) helps with TS using a multi-objective-based system. The VM manager performs VM migration decisions to provide load balance among physical machines (PMs). When a PM is overburdened and another PM is underburdened, the decision to migrate VMs is made based on the appropriate conditions. Balanced load and reduced energy usage in PMs are achieved in the former case. Manta ray foraging (MRF) is used to migrate VMs, and its decisions are based on a variety of factors.

The proposed approach provides the best possible scheduling for both VMs and PMs. To complete the task, improved whale optimization algorithm for Cloud TS has 42 s of completion time, enhanced multi-verse optimizer has 48 s, hybrid electro search with a genetic algorithm has 50 s, adaptive benefit factor-based symbiotic organisms search has 38 s and, finally, the proposed model has 30 s, which shows better performance of the proposed model.

User’s request or data transmission in a cloud data centre may cause the VMs to be under or overloaded with data. To identify the load on VM, initially EQ algorithm is used for clustering process. To figure out how well the proposed method works when the system is very busy by implementing hybrid algorithm called BAT–ABC. After the TS process, VM migration is occurred at the final stage, where optimal VM is identified by using MRF algorithm. The experimental analysis is carried out by using various metrics such as execution time, transmission time, makespan for various iterations, resource utilization and load fairness. With its system load, the metric gives load fairness. How load fairness is worked out depends on how long each task takes to do. It has been added that a cloud system may be able to achieve more load fairness if tasks take less time to finish.

Dissimilar joining of austenitic stainless steels and ferritic steels is a challenging task and has a wide range of applications due to its excellent mechanical and thermal characteristics. They are joined mostly by using conventional modes. In the current investigation, the study and optimization of hot wire TIG welding parameters was carried out.

These parameters will govern the desired characteristics of the joint. Solutions were found out through multi-response optimization by using response surface methodology and single response optimization using particle swarm optimization.

Optimized input welding parameters that were achieved are electrode current 180 amps, wire feed rate 1870 mm/min and hot wire current 98 amps and the optimized UTS is 665.45 MPa. The results from PSO were compared with RSM and the optimized input welding parameters for the electrode current, hot wire current and wire feed rate exhibited maximum ultimate tensile strength which were also confirmed from response and contour plots.

Sensitivity analysis was also performed to understand the effect of each individual parameters on the response. Microstructure features were evaluated for the joints and was found that the characteristics are within the desired criteria.

This study is a response to the paucity of research into early internationalising firms based in India. We seek to explore the internationalisation of small and new Indian firms and the decision-making process of their entrepreneurs/managers.

The study uses original, primary data gathered from in-depth, semi-structured interviews conducted with the managers of six such firms to explore the factors that might facilitate, motivate, or impede the efforts undertaken by young Indian firms to embark upon a process of early internationalisation.

Our findings suggest that, in line with their counterparts from other countries, the early internationalisation of small firms from India is driven primarily by the search for more favourable demand conditions overseas and is facilitated by new technologies. However, we find no evidence suggesting that the emergence of early internationalising firms from India is driven by the search for more favourable production conditions or by the direct international experience and exposure of their founders. In line with prior scholarly work, our research suggests that government support is an important facilitator of early internationalisation of small firms.

The study provides insights into the internationalisation process of INVs from India and contributes to broadening our understanding of the behaviour of firms under a set of specific institutional conditions. Based on our findings, we develop a conceptual framework which can be useful for further empirical testing. Our study is also one of the few to be conducted on a sample of INVs from India.

This paper aims to consolidate the results of various researchers focusing the different applications, so that this paper could become the torch bearer for the futuristic researchers working in the domain of cold gas dynamics spray coating.

A study on the cold spray coating is presented by summarizing the data present in literature. Important factors such as coating temperature, pressure, coating thickness, particle size, which affect the erosion-corrosion (E-C) resistance, physical and mechanical properties of boiler steel are stated. This paper also addresses the use of cold spray coating and compares it with other different thermal spray processes.

From the literature review, it was noticed that cold spray technology is best as compare to other thermal spray processes to reduce porosity, increase hardness, adhesion strength and retention in properties of feedstock powders.

Cold spray coating technology has a great potential in almost every field especially in restoration of surfaces, generation of complex surface, biomedical application, resist hot corrosion, wear, oxidation and erosion corrosion.

Real-time visibility and traceability in warehousing could be accomplished by implementing the internet-of-things (IoT) technology. The purpose of this paper is to develop a roadmap for designing an IoT-based smart warehouse infrastructure and, respectively, design and apply the IoT-based smart warehouse infrastructure using a developed roadmap. More specifically, this study first identifies critical components to design an IoT-based smart warehouse infrastructure. Second, the study at hand identifies essential factors that contribute to the successful implementation of IoT-based smart warehouse infrastructure.

A qualitative-descriptive method, through a comprehensive review of the relevant studies, was used in this study to develop a roadmap. A prototype system was then designed to simulate a case company’s actual warehouse operations in one of the manufacturing companies in Indonesia.

A framework was proposed which is viable for designing an IoT-based smart warehouse infrastructure. Based on the data collected from a case company, the proposed smart warehouse infrastructure design successfully implemented real-time visibility and traceability and improved overall warehouse efficiency.

While the framework in this research was carried out in one of the developing counties, the study could be used as the basis for future research in a smart warehouse, IoT and related topics.

This research enhances the limited knowledge to establish the IoT infrastructure for a smart warehouse to enable real-time visibility and traceability. This study is also the first to specifically propose a framework for designing an IoT-based smart warehouse infrastructure. The proposed framework can motivate companies in developing countries to deploy efficient and effective smart warehouses using IoT to drive the countries’ economic growth.