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The purpose of this paper is to study the fault diagnosis of internal combustion (IC) engine gearbox using vibration signals with signal processing and machine learning (ML) techniques.

Vibration signals from the gearbox are acquired for healthy and induced faulty conditions of the gear. In this study, 50% tooth fault and 100% tooth fault are chosen as gear faults in the driver gear. The acquired signals are processed and analyzed using signal processing and ML techniques.

The obtained results show that variation in the amplitude of the crankshaft rotational frequency (CRF) and gear mesh frequency (GMF) for different conditions of the gearbox with various load conditions. ML techniques were also employed in developing the fault diagnosis system using statistical features. J48 decision tree provides better classification accuracy about 85.1852% in identifying gearbox conditions.

The proposed approach can be used effectively for fault diagnosis of IC engine gearbox. Spectrum and continuous wavelet transform (CWT) provide better information about gear fault conditions using time–frequency characteristics.

In this paper, experiments are conducted on real-time running condition of IC engine gearbox while considering combustion. Eddy current dynamometer is attached to output shaft of the engine for applying load. Spectrum, cepstrum, short-time Fourier transform (STFT) and wavelet analysis are performed. Spectrum, cepstrum and CWT provide better information about gear fault conditions using time–frequency characteristics. ML techniques were used in analyzing classification accuracy of the experimental data to detect the gearbox conditions using various classifiers. Hence, these techniques can be used for detection of faults in the IC engine gearbox and other reciprocating/rotating machineries.

Bearings and gears are major components in any rotatory machines and, thus, gained interest for condition monitoring. The failure of such critical components may cause an increase in down time and maintenance cost. Condition monitoring using the machine learning approach is a conceivable solution for the problem raised during the operation of the machinery system. The paper aims to discuss these issues.

This paper aims engine gearbox fault diagnosis based on a decision tree and artificial neural network algorithm.

The experimental result (classification accuracy 85.55 percent) validates that the proposed approach is an effective method for engine gearbox fault diagnosis.

This paper attempts to diagnose the faults in engine gearbox based on the machine learning approach with the combination of statistical features of vibration signals, decision tree and multi-layer perceptron neural network techniques.

The purpose of this paper is to develop a text extraction tool for scanned documents that would extract text and build the keywords corpus and key phrases corpus for the document without manual intervention.

For text extraction from scanned documents, a Web-based optical character recognition (OCR) tool was developed. OCR is a well-established technology, so to develop the OCR, Microsoft Office document imaging tools were used. To account for the commonly encountered problem of skew being introduced, a method to detect and correct the skew introduced in the scanned documents was developed and integrated with the tool. The OCR tool was customized to build keywords and key phrases corpus for every document.

The developed tool was evaluated using a 100 document corpus to test the various properties of OCR. The tool had above 99 per cent word read accuracy for text only image documents. The customization of the OCR was tested with samples of Microfiches, sample of Journal pages from back volumes and samples from newspaper clips and the results are discussed in the summary. The tool was found to be useful for text extraction and processing.

The scanned documents are converted to keywords and key phrases corpus. The tool could be used to build metadata for scanned documents without manual intervention.

The tool is used to convert unstructured data (in the form of image documents) to structured data (the document is converted into keywords, and key phrases database). In addition, the image document is converted to editable and searchable document.

In the realm of tourism and sustainability, transportation and mobility hold a crucial position. Among the green product categories, three-wheeled electric vehicles have gained significant attention due to their environmental benefits. However, research on consumers’ intentions to purchase these vehicles is limited, particularly in Asian destinations like Sri Lanka. This study aims to bridge this gap by examining the direct and indirect impacts of green brand awareness, green brand associations and green perceived quality of local Sri Lankans’ intention to choose branded electric three-wheelers in future purchase decisions.

A questionnaire survey garnered 400 usable responses, and structural equation modeling was used to test hypotheses.

The results indicate that green brand awareness, green brand associations and green perceived quality have a direct positive and significant effect on consumers’ intention to choose branded electric three-wheelers in future purchase decisions and green trust. Also, green trust has a positive and significant direct effect on consumers’ intention to choose branded electric three-wheelers in future purchase decisions. Furthermore, the results indicate that only green brand associations and green perceived quality have an indirect positive and significant effect on consumers’ intention to choose branded electric three-wheelers in future purchase decisions through green trust.

This research contributes to the tourism industry and other sectors involved in sustainability efforts in several ways. First, it emphasizes the importance of fostering positive associations with eco-friendly attributes and perceived product quality to build consumer trust and influence their purchase intentions for green products. Second, the study underscores the relevance of highlighting eco-friendly product attributes to stimulate consumer interest and adoption of green products. Finally, it theoretically underscores the significance of building trust through transparent and credible sustainability initiatives.

Pattern recognition systems often have to handle problem of large volume of training data sets including duplicate and similar training samples. This problem leads to large memory requirement for saving and processing data, and the time complexity for training algorithms. The purpose of the paper is to reduce the volume of training part of a data set – in order to increase the system speed, without any significant decrease in system accuracy.

A new technique for data set size reduction – using a version of modified frequency diagram approach – is presented. In order to reduce processing time, the proposed method compares the samples of a class to other samples in the same class, instead of comparing samples from different classes. It only removes patterns that are similar to the generated class template in each class. To achieve this aim, no feature extraction operation was carried out, in order to produce more precise assessment on the proposed data size reduction technique.

The results from the experiments, and according to one of the biggest handwritten numeral standard optical character recognition (OCR) data sets, Hoda, show a 14.88 percent decrease in data set volume without significant decrease in performance.

The proposed technique is effective for size reduction for all pictorial databases such as OCR data sets.

State-of-the-art algorithms currently used for data set size reduction usually remove samples near to class's centers, or support vector (SV) samples between different classes. However, the samples near to a class center have valuable information about class characteristics, and they are necessary to build a system model. Also, SV s are important samples to evaluate the system efficiency. The proposed technique, unlike the other available methods, keeps both outlier samples, as well as the samples close to the class centers.

Very large-scale integration (VLSI) digital signal processing became very popular and is predominantly used in several emerging applications. The optimal design of multirate filter with improvement in performance parameters such as less area, high speed and less power is the challenging task in most of the signal processing applications. This study aims to propose several effective multirate filter structures to accomplish sampling rate conversion.

The multirate filter structures considered in this work are polyphase filter and coefficient symmetry-based finite impulse response filter. The symmetry scheme particularly brings down the complexity to significant extent. To bring improvement in speed, delay registers are inserted at appropriate path with the help of pipelining and retiming scheme.

In this paper, the three tasks have been considered. First, the polyphase coefficient symmetry and modified polyphase (MP) structure is designed. Second, the pipelining is applied to the polyphase structure and the obtained results are compared with the polyphase structure. In third, retiming is applied to the polyphase structure and the performance comparison is carried out. The structures are realized for various orders, and the comparative analysis is carried out with the filter order N = 12, 30, 42, 8, 11 and 24 and the results are stated. The performance of all the accomplished structures is analyzed using Altera Quartus with the family cyclone II, device EP2C70F672C6. The results show that the multirate filter using pipelining and retiming offers better performance when examining with the conventional structures. Retimed and pipelined MP structure achieves a speed enhancement of about 33.81% when examining with the conventional polyphase (CP) structure with retiming and pipelining for N = 24 and M = 5. Likewise, the 2/3 structure of pipelined coefficient symmetry approach offers area reduction of about 54.76% over 2/3 structure of pipelined polyphase approach for N = 30 with little reduction in power. The fine grain pipelined and retimed MP structure with N = 11 and M = 3 avails critical path delay reduction of about 28.15% when examining with the corresponding fine grain pipelined and retimed CP structure.

The proposed distinct structures offer better alternative to conventional structures because of the symmetric coefficients, performance enhancement using pipelining and retiming based rate conversion structures. The suggested structures can be used for achieving different rates in software radios.

Small and ignorant investors have had very unpleasant experiences in the stock market. They should be alert and have proper knowledge and understanding of the various problems that can arise in their dealings and how these can be resolved. This paper aims to analyse the investors’ probable solutions to their investment-related problems by using descriptives and factor analysis technique. Only Securities and Exchange Board of India (SEBI) can ensure a free and fair market and take India into league of major global capital markets in the next round of reforms.

By personally visiting the offices of the stockbrokers, 1,000 questionnaires have been distributed among retail equity investors of Punjab, i.e. Amritsar, Jalandhar, Ludhiana and Mohali. Stockbrokers have been selected using simple random sampling technique because of their large number. In total, 373 questionnaires have been filled up by the respondents, and 45 questionnaires have been found to be incomplete and thus have been excluded from the analysis. Remaining 328 questionnaires have been used for the analysis. The objective of the research is to study the investors’ probable solutions to their investment-related problems. The collected data have been analysed using descriptives and factor analysis technique.

It has been found that 24.7 per cent retail equity investors have filed complaints while dealing in the securities market; on the other hand, 75.3 per cent retail equity investors have not filed any complaint neither against the company nor against the intermediaries. It has been found that the authorities have taken 12-90 days and even four-five months in providing first reply to their complainants. Moreover, it has been found that in some of the cases, SEBI has written to the concerned companies to resolve the complaints, and some issues have been still pending with SEBI. It has been revealed that SEBI has taken quite long time to resolve the complaints, and equity investors have not been satisfied with the decisions of the SEBI. This study has further highlighted the importance of variables considered by investors as probable solutions to their problems while dealing with securities. The highest mean score has been found for the variable grievance redressal mechanism has been slow, followed by investors have been exploited by the malpractices of companies, merchant bankers and auditors, stronger regulations have been required to strengthen investor protection, investor has yet not educated enough to discriminate between good and not-so-good scrips, etc. These 22 variables measuring the construct of investors’ probable solutions to their problems have been analysed with the help of factor analysis. Six factors have been identified with the help of factor analysis, i.e. stability measures for stock market, investor awareness and education norms, measures to impart knowledge to investors, measures to protect investor rights, audit of companies and investor grievance redressal, and these factors have together explained 68.441 per cent of the variance in data.

Based on the study done by the researcher, the following suggestions are identified for further research. As the present study is at a state level, it could be extended to national level. The impact of retail investment in capital market may be studied in view of rural investors. The study may further be carried out to analyse the impact of reforms on the functioning of stock exchanges. A study on the awareness of women investors about retail investment pattern could be attempted. Implications of internet stock trading in India can be taken up for study. Impact of technological innovation in capital markets can be studied.

This study would be of great use for investors who make decisions regarding investment. This study will help policymakers in formulating strategies and will also help credit rating agencies in rating the investment instruments.

This study is of great help for investors and SEBI. This study guides the investors regarding various laws that have been formulated for their protection and guides the SEBI in making strict regulations for the protection of the investors.

This task is 100 per cent original and some authors have been quoted.

Scams are indeed malicious attempts to influence people and can take many forms, including online scams. With the increasing availability of technology, scammers have more tools at their disposal to create convincing and sophisticated communications that appear to come from legitimate sources, such as emails, text messages or social media posts. These scams can be designed to trick individuals into clicking on malicious links, downloading malware or ransomware or providing sensitive information such as login information, financial information or personal details. Scammers often use social engineering techniques to persuade their targets to take specific actions, such as creating a sense of urgency, offering a reward or prize or posing as a trusted authority. These tactics can be highly effective, particularly if the target is unaware of the warning signs of a scam or is unfamiliar with how to protect themselves from online fraud. This paper aims to explore the extent and nature of this problem and evokes the concern that the general public is vulnerable and susceptible to scams if they are not resilient and aware. This paper also explores why victims fall for online scams and uncovers preventive measures to enable a direction in tacitly strategizing ways to create more impactful and effective awareness campaigns.

This study explores these aspects through a holistic qualitative approach. Using in-depth interview techniques with six victims, six non-victims, four law enforcement officers, four scammers and seven stakeholders from various agencies such as banks, telecommunication agencies and the Malaysian Communications and Multimedia Commission involved in combating the issue of scams.

The findings generally revealed that participants who were victims of scams felt Malaysians were susceptible to scams, easily fooled and had a nonchalant attitude. Most participants also highlighted that public safety is important for ensuring a high quality of life for citizens that should work closely between the government and non-government agencies, including effective law enforcement and crime prevention strategies.

The uniqueness of this study is the feedback from scammers themselves and their input towards authority and victims. Overall, the respondents provide their views drawing strength from the ever-changing technological background as well as the susceptibility of security features and vulnerability of human engagement.