ICT: Applications and Social Interfaces

We are here proposing a sub-system for the main system the company is working on, which must produce an 84 MHz RF output from a 56 MHz RF input. This is done by using a mixer, harmonic filters, and an external local oscillator input to the system, which is provided to the system in varied steps of 10 kHz frequency to achieve the desired results. A regulated oscillator is crucial to a low-frequency receiver’s operation. To modify a signal’s frequency to that required by a radio frequency receiver, an oscillator is utilized with a mixer. The effectiveness of a radio frequency receiver is increased by signal processing at a certain frequency.

Biometrics plays a significant role in many sectors of modern society, like banking transactions, person identification, and surveillance. The ear is used as a biometric trait because the human ear shape is stable between the ages of 8 and 70 and has a uniform color distribution. Ear is visible even after wearing a face mask. Ear biometrics is an example of passive biometrics as it can be used for person identification without their knowledge. Hence, it can be used to develop a completely automatic biometric authentication system. In this paper, the state-of-the-art literature on ear biometrics from 2010 is critically reviewed. This literature review also covers various studies on ear detection, feature extraction, and classification techniques, as well as the various benchmark datasets available for ear biometric research. The intuition, methodology, limitations, and future scope of these articles have been summarized. Finally, major challenges and future scopes of ear biometrics have also been discussed. Hence, this review will provide an overview for researchers interested in further research in this domain.

Elections are a cornerstone of democracy, and ensuring the credibility of the voting process becomes essential to maintain the legitimacy of democratic institutions. In recent years, blockchain technology has been suggested as an alternative solution for improving the transparency and security of the voting process. However, existing digital voting systems running on blockchain face several challenges, including user accessibility, security vulnerabilities, and the potential for manipulation or control by centralized authorities. The proposed paper proposes a novel digital voting system based on the Ethereum blockchain that addresses these challenges through a hybrid approach to security and a user-friendly and east-to-navigate interface. The proposed method combines the security benefits of blockchain technology with the accessibility and reliability of traditional paper ballots, creating a more robust and reliable electoral process. The proposed system uses cryptographic techniques, biometric identification, and several authentication methods to protect voter privacy and security. In addition, the proposed work has designed an interface that is clean, simple to use, and open to voters of all ages and abilities, including those with disabilities.

A spectacular office life is a perfect dream for anyone who resides on the planet. This dream, however, comes at the expense, that is health, because it entails a busy and stressful, energy-draining, and fatigue-causing daily routine. The technological impact on humans has given them barely any time to unwind for yogic practices. The paper represents the real-time impact of ancient yogic theory called Surya Namaskar or “Sun Salutation” in English on today’s ultra-modern working professionals. The empirical bivariate research analysis demonstrated that sparing a 10 min per day for Surya Namaskar can cause a dramatic change in the human healthy life system. The study's findings showed a strong correlation between yoga practices and human health.

Network on Chip (NoC) is a contemporary signaling method that enhances the efficiency of interconnections, design, and testing processes within current System on Chip architectures. Routers provide streamlined routing with minimal complexity and exceptional performance, contributing to effective global on-chip communication. In a multiprocessor system with resource-constrained processes, streaming applications seek strict bandwidth and throughput guarantees. Label Switching Network on Chip (LS-NoC) is used that offers bandwidth reservation which drives the throughput assurances. Traffic is engineered to Quality of Service (QoS) guaranteed routes using a centralized LS-NoC Management architecture. The existing NoC Manager is modified to improve throughput. Area utilization and power consumption are to be minimized to have a good design. Xilinx Vivado Design Suite is used to model the design in Verilog HDL. The implemented router design is realized on Artix-7, and an assessment of resource utilization and power consumption is conducted for the five port router. In comparison to the other router architecture, synthesis reveals that 29% of less registers are used and a power reduction of 28% that is 12.08 mW is observed.

Due to COVID-19, many educational institutes throughout the world shifted their means of teaching from offline to online. Online examination became more challenging while testing the academic skills or depth of knowledge of a student. The alarming situation ascends when a student practices unfair means during an online exam. In this paper, a survey is carried out based on recent research works where initially the reason behind cheating in exams is analyzed. Subsequently, detection measures for cheating during and after exams have also been discussed. Finally, various prevention measures for cheating have been studied to identify new research challenges in this field.

The timely identification of mental health issues enables experts to more effectively provide treatment and enhance the well-being of patients. Mental health pertains to an individual’s emotional, mental, and interpersonal state, influencing their thoughts, emotions, and behaviors. It remains crucial across all life phases, spanning childhood, adolescence, and adulthood. Historically, categorizing mental health problems among college students demanded significant effort and time from psychologists. This research engaged five machine learning methods to classify such issues swiftly. The effectiveness of these methods was evaluated based on different standards. The five methods included logistic regression, KNN classification, decision tree classification, random forest, and stacking. A comparison and implementation of these methods revealed that stacking yielded the highest accuracy, predicting 82.20.

This research article delves into the creation of a real-time system for detecting motion, particularly for surveillance purposes. The proposed approach relies on computer vision algorithms to analyze live video streams captured by a camera. By scrutinizing these feeds, the algorithms can discern alterations within the scene, specifically identifying objects in motion. The primary aim is to ensure that this system functions in real time, thereby furnishing instantaneous insights to security personnel. The paper expounds on the different steps integral to motion detection, encompassing procedures like background subtraction, foreground segmentation, and object tracking. To validate the system’s effectiveness, an evaluation is conducted using a collection of actual surveillance videos. The findings underscore the system’s capability to accurately detect moving objects across diverse scenarios. The article concludes by contemplating the system’s potential applications, which span security setups, traffic supervision, and ecological monitoring. The focal technique utilized in this study is Frame Differencing, a means of detecting object motion. This approach is adept at distinguishing moving objects within a given environment. In conjunction with this method, the paper emphasizes the viability of background subtraction to further enhance Frame Differencing, thereby augmenting its precision and efficacy. Upon applying this method to a camera, it is discerned that luminosity has a significant impact on the minimal value. Notably, a minimal value of 35 is identified as the most favorable choice. Here, threshold value 35 is selected because we have seen in our experiment that 35 gives the optimal result with our algorithm.

This paper investigates the education and employment status of people with disabilities in Bangladesh, utilizing a mixed-methods approach, including a survey. The findings reveal benefits and significant barriers to education and employment, emphasizing the need for more inclusive policies and practices. The study underscores the importance of addressing the specific needs of people with disabilities and calls for increased efforts to promote accessibility and opportunities in both education and employment. Further research is recommended to develop effective strategies for supporting people with disabilities in Bangladesh.

All Natural Language Processing (NLP) activities are impossible without the data corpus. We can get the data corpus in two ways either we can download it from the Internet or we can develop our own corpus. There are five stages for corpus development such as data collection, tokenization, stop word removal, linguistic annotation, and corpus encoding. There are various problems/challenges with the Low-Resource Language (LRL) such as Limited availability of linguistic resources, lack of Annotated Corpora, limited research and Development Efforts, Insufficient Language Technology Tools, and Endangered or Indigenous languages. Data corpus and stop word list are not available because Awadhi is LRL. This paper shows the corpus development stages and validates the stop word list. Once the Awadhi data corpus and Awadhi stop word list are ready, other researchers can use them for their research work.

Across their lifetimes, humans predominantly inhabit artificial built environments, as opposed to natural ones. Over the course of history, human curiosity and the pursuit of novelty have driven the continuous transformation of these built environments, a pace that has accelerated significantly in recent times. Each societal innovation leaves its mark on the design of these spaces, and today, humanity is experiencing a rapid surge in technological advancements, particularly in the realm of digital technologies. The ever-evolving landscape of built environments presents an ongoing challenge for individuals, businesses, organizations, and society at large to adapt. This study aims to explore the present-day advancements and potential future applications of the Internet of Things (IoT), a pivotal innovation of our time, and to envision the research agenda necessary for businesses and organizations to navigate these dynamic challenges effectively.

This research paper has examined the factors which contribute to consumers’ acceptance of Mobile Pharmacy applications (M-Pharmacy apps) as well as the factors which inhibit their adoption by application of behavioral reasoning theory (BRT). The research model was based on the BRT framework and empirically tested by taking responses from 426 respondents. PLS-SEM was used to aid in the data analysis. The acceptance of M-Pharmacy apps by consumers is positively impacted by factors such as usefulness, convenience, and monetary benefits, whereas adoption is negatively impacted by security barriers. Furthermore, it is found that perceived need impacts both, positive as well as negative factors as a person who needs wellness products is more likely to adopt M-Pharmacy apps. The research is limited to the Indian context, respondents are from Urban India, and only demographic factors such as age and gender are being considered. The findings on adoption factors for M-Pharmacy apps would help the product development team to build more user-friendly apps, the marketing team to formulate better product positioning strategies, and the pharmacists and drug retailers to improve the product offerings for online sales. This is the first such research paper that has employed BRT to explore both “reasons for” as well as “reasons against” the acceptance of M-Pharmacy applications by consumers.

Accurate groundwater level (GWL) estimation is significant to attain sustainable developmental goals and management of integrated water resources. But, its all-time accessibility is of serious concern. Hence, it is crucial to understand the groundwater potential for utilisation of water resources. There has been a significant decline in urban groundwater resources in past few decades because of over-exploitation, climatic change, urbanisation, and population growth. For understanding the effect of climate change variables on the fluctuation of GWL, a machine learning-based model is developed integrating sparrow search algorithm (SSA) and support vector machine (SVM). The developed model is utilised for predicting GWLs in Cuttack, a heavily inhabited city with decreasing groundwater resources. Precipitation, minimum relative humidity, and GWL of current, one-month lag, and two-month are taken as input parameters to predict the GWL on monthly basis. Three statistical indices, namely root mean square error (RMSE), coefficient of determination (R2), and Nash–Sutcliffe efficiency (NS), are applied for performance evaluation of SVM-SSA method. The results reveal that SVM-SSA with RMSE of 0.5876 (m), R2 of 0.9698, and NS of 0.968 significantly outperforms SVM with RMSE of 9.9891 (m), R2 of 0.9345, and NS of 0.9326. The study’s findings indicate that SVM-SSA model is an acceptable data-driven method for predicting skewed and nonstationary monthly GWL time series, demonstrating a suitable tool for monthly GWL prediction.

One of the most effective methods for increasing the capacity and reducing the bit error rate (BER) of 5G and other next-generation networks is to implement non-orthogonal multiple access, often known as NOMA. The objective of this paper is to propose two innovative methodologies that, when integrated with uncooperative Underlay (UU) and cooperative Interweave (CI) cognitive radio network (CRN), will provide great potential for decreasing BER and increasing average capacity in the downlink (DL) NOMA power domain (PD). Three different network architectures have been suggested to operate on different transmission powers, utilizing 8 × 8, 16 × 16, and 32 × 32 multiple-input multiple-output (MIMO) configurations. MATLAB is used to calculate the proposed model's average capacity and BER. Using the UUCR-NOMA model improves average capacity performance by 74.7, 89, and 95.6%, respectively, and using the CICR-NOMA model improves average capacity performance by 75.1, 89.2, and 95.7%. MIMO boosts average capacity and BER performance substantially. The provided Monte Carlo simulation results, which validate our work, are in agreement with the derived equation.

The constant transformations in Business Intelligence (BI) systems, driven by technological innovations, have contributed to increasing competitiveness and agility in strategic and operational decisions. In this context, it is crucial to be aware of corporate data and ensure user access to analysis, familiarizing them with BI systems. The aim of this work is to present the platforms that allow the collection and processing of data in the Data Warehouse environment (DW), which meet user requirements, presenting indicators through dashboards and reports. These solutions aim to improve the organization's business management and facilitate decision-making, offering valuable insights.

Technology has impacted the global economy to society tremendously. Technology adoption among corporations has also been studied in past literature. Artificial Intelligence is wide wide-ranging tool that enables organizations to facilitate its processes. This study emphasizes the use of technology, i.e., Artificial Intelligence for improved employee engagement. Engagement of employees is the most crucial task that all companies are struggling to achieve nowadays. At one place there is a boom in technological advancement and on the other side human behavior is becoming extremely complicated to comprehend and manage. Present research works on the use of technology in enhancing employee engagement through the use of artificial intelligence. This article highlights the use of artificial intelligence in augmenting the process of HR and the future directions thereof in keeping employees engaged. The authors in this research have mentioned the various uses of artificial intelligence in various HR processes for keeping employees engaged. Taking the gaps from the past research, where missing links were found related to artificial intelligence and its use through various human resource functions and its impact on employee engagement, this research focuses on how this can be implemented with various examples.

When the body organ pancreas are not capable enough of producing sufficient amount of insulin in the body, sugar level in the bloodstream increases. This results in serious health-related issues. The deregulation of glucose in the body gives rise to chronic disease denoted as Diabetes. Diabetes complications cause kidney problems, heart strokes, nerve issues, and so on and so forth. Getting an aid or a precaution at an early level can reduce the chance of Diabetes. This work on diabetes prediction can help in the improvement of human life style. Our motive is to predict the outcome whether or not the patient is suffering from diabetes on the basis of basic human features such as glucose level, blood pressure, insulin, BMI (body mass index), number of pregnancies, skin thickness, diabetes pedigree function, and age. To achieve our goal, we have used the famous Pima Indian type-2 Diabetes Mellitus Classification Dataset. In this paper, we did an analysis using 12 machine learning algorithms and compared the results with others.

In data acquisition, satellite images are easily affected by speckle noise, which degrades image quality. An edge-aware guided filtering technique for removing speckle noise has been proposed in this paper. In this technique, initially, the satellite images are applied on the joint bilateral filter. The non-additive noise was then transformed into additive noise using the log transform on the filtered image. The filtered image is split into low-frequency and high-frequency sub-bands using a 2D-DWT. The soft threshold eliminates additive noise from horizontal and the vertical sub-band images, in contrast to IGF, which only eliminates additive noise from low-frequency sub-band images. The simulation has been performed on real-time SAR image. The simulation outcomes show that the edge-aware guided filtering technique removes speckle noise more effectively than existing methods while conserving edges and minimizing computational complexity.

The Internet of Things (IoT) has remained a buzz for the last few years. This article attempts to evaluate the application of IoT and its challenges through a literature review. Various articles were selected based on the keywords selection, citation received, and IoT-related literature review presented. IoT has the potential to be implemented in aircraft, traffic to agriculture. Many automobile industries are going to rely heavily on IoT. All countries, including India, may face opportunities and challenges while implementing it. This article will be helpful for future studies since it gives a ready reference for the empirical work.

Vehicular Ad hoc Networks (VANETs) are highly mobile wireless networks that play a crucial role in public safety dispatches and commercial operations. Recent advancements in VANETs have enabled the integration of data generated from these networks into smart operations, providing quality of life services. Data mining is a process that involves extracting valuable patterns and information from data. One promising area of research involves applying data mining techniques to VANETs to extract useful patterns. This paper presents an overview of basic data mining techniques, including pre-processing, outlier detection, clustering, and data ordering. Additionally, this paper describes the most commonly used classification and clustering techniques, comparing them based on their strengths and weaknesses.

Today's leading cause of death worldwide is cardiovascular disease, which has risen to the top of the list of diseases in terms of diagnostic difficulty. Cardiovascular disease is more likely to occur in a person with chest pain, depression, hypertension, smoking, women with early menopause, diabetes, high cholesterol, and over drinking. Early prediction of cardiovascular disease is needed to save more lives. Here comes the saviour Machine Learning algorithms that are less expensive with more accuracy. Some of the common machine learning algorithms are implemented to predict the disease. Different techniques provide different accuracies depending on the attributes, dataset, and tools used for implementation. Using the ECG dataset, we create an 11-layer Convolutional Neural Network 2D in this study. We have proposed two models namely Cardiovascular Disease Detection—Machine Learning (CVD-ML) that can predict Cardiovascular Disease using real-time numerical data and Cardiovascular Disease Detection—Deep Learning (CVD-DL) using the ECG Image. By using ensembling technique, we have attained the highest accuracy of 94.6% for real-time numerical data and by using Convolutional Neural Network we have attained the accuracy of 99.9% for ECG data. Therefore, Artificial Intelligence techniques used are highly reliable and effective in providing accuracy for cardiovascular disease prediction.

Dedicated Short-Range Communication (DSRC) is a wireless communication technology specifically designed for V2V communication, enabling vehicles to share crucial data such as speed, location, and other relevant information within a range of up to 1,000 m. This paper presents an in-depth investigation of the application of DSRC for V2V communication, aiming to improve road safety, traffic management and overall efficiency. The study analyses the benefits, challenges and potential applications of DSRC for V2V communication. Through this research, we seek to provide valuable insights into the implementation of DSRC as a crucial component of intelligent transportation systems.

The tourism industry has witnessed substantial technological advancements in recent years because of the rapid growth of information and communication technologies (ICT). Previous literature indicates that most of the research has been focused on comprehending the application of various technologies in the tourism sector and their impact on consumer behavior. However, no bibliometric studies are undertaken to analyze the relevant publications, the effect of technology and the emerging trends in technology within the tourism industry. This study aims to conduct a bibliometric analysis to explore and evaluate these developments. Therefore, the study performed a bibliometric analysis of 1430 published research articles from the Scopus database for the period 2018–2022. The bibliometric analysis involved a performance analysis and science mapping of the publications using the Bibliometrix R package software. The results indicate an increasing trend in publications and the year 2022 recorded the highest number of articles published. The study found that Law R., Buhalis D., and Gretzel were the most productive authors in the last five years. Moreover, the analysis showed that the top ten journals accounted for one-third of the total publications. The conceptual structure indicated that the well-developed topics are represented by keywords such as innovation, Covid-19, tourism development, and destinations. Furthermore, the intellectual structure revealed three unique clusters indicating the theoretical foundations within tourism research, smart tourism and value co-creation in tourism. Finally, this study will benefit academicians and tourism marketers to gain insights into technological advancements and emerging trends in tourism research.

A method for displaying huge amounts of data is known as a big data algorithm. Security, storage, searching, sharing, exposure, and transferring are among the difficulties. Due to its straightforward user interface, high scalability, and fault-tolerance capability, MapReduce is currently required for data server applications (Niemenmaa et al. in Bioinformatics 28:876–877, 2012). It results in a fresh indication of caution regarding sophisticated algorithms for data analysis. The easiest way to manage the enormous amount of data while speeding up processing is to use MapReduce. This paper discusses MapReduce applications and optimization techniques, as well as their similarities and differences, and offers some recommendations for future research projects. Hadoop is a well-known MapReduce success, and MapReduce is currently the Hadoop programming style for massive data processing (Uma Maheswara Rao, S., & Lakshmanan, L. (2023). Security and scalability issues in big data analytics in heterogeneous networks. Soft Computing, 1–7.). We suggest a MapReduce programming methodology for managing enormous data sets in Distributed Systems to address the aforementioned issues.

Over the past two decades, the growth of non-governmental organizations (NGOs) in India has been substantial. However, many NGOs still struggle with financial stability, often oscillating between periods of independence and survival on the brink of bankruptcy. They frequently face challenges related to inadequate human and financial resources. In order to address these issues, there is a pressing need for a transparent system that ensures privacy for NGO members. This system would enable effective tracking of member performance, foster enthusiasm among stakeholders, facilitate collaboration, and promote interaction with various stakeholders. Beyond Kind aims to deliver a website that embodies the highest levels of transparency and accessibility, thereby assisting NGOs in their development and flourishing.

In the age of digital technology, computer- and networking-based technologies have increased the enormous demand for multimedia data transmission through the Internet. In the context of multimedia data, images are widely shared and transferred because these provide more content, and sensitive and confidential information, which are also accompanied by several security risks. This paper presents a novel hiding approach for a triple image using a wavelet transform with a phase-truncated Fourier transform (PTFT) process. In the scheme, the involvement of wavelet transform helps to create watermarking to hide three images that also work as a pre-processing layer which are then undergone for encryption combining processes of phase truncation and phase reservation. This encryption process generates a real-value ciphertext and two decryption keys for retrieving the images. Additionally, the parameters of wavelet and pixel scrambling orders are required at the decryption stage. The effectiveness of the proposed idea for triple image hiding is assessed by determining the mean square error and the correlation coefficient for the decryption steps. The simulation experiments present higher security and robustness against unauthorized attempts.

The transition toward renewable energy sources, particularly wind energy, has become increasingly crucial for sustainable development. This research study presents a comprehensive investigation into the daily wind energy output patterns of the top wind energy producers, including Hertz, TenneT, Amprion, and Transnet. By examining the mean and median daily wind energy outputs, the study uncovers distinct patterns, including morning drops, mid-day peaks, and evening surges, which have significant implications for energy optimization strategies. Furthermore, the research explores the impact of storms on wind energy generation, identifying their pivotal role in influencing daily outputs. This research study presents crucial insights that can revolutionize wind energy production and enhance the utilization of renewable resources. By analyzing the impact of storms on energy generation, the study provides a foundation for devising improved strategies to ensure grid stability. Ultimately, this research contributes to advancing sustainable energy solutions and propels the global transition toward a greener and more resilient future.

The construction industry plays a pivotal role in global infrastructure development; however, it faces challenges related to efficiency, safety, and environmental impact. The advent of robotics and automation has opened new avenues for addressing these challenges through human-robot collaboration and sustainable automation. This research article explores the evolving trends in human-robot collaboration within the construction sector, focusing on the integration of advanced robotics, artificial intelligence, and sustainable practices. The article delves into case studies, technological advancements, and their impact on construction efficiency, safety, and environmental sustainability. Through comprehensive analysis, this article sheds light on the potential benefits, challenges, and future directions of human-robot collaboration and sustainable automation in the construction industry.

The contemporary practice of externally retrofitting deteriorated reinforced concrete elements to restore their strength is widely recognized. Various methods are employed for externally retrofitting structural members, including section enlargement, external plate bonding, external post-tensioning, grouting, and the utilization of fiber-reinforced polymer (FRP) composites, based on the extent of damage. This study focuses on the application of FRP composites, specifically fabric/tapes adhered with adhesive, to enhance the integrity of structural elements exhibiting crack-like defects. Beyond just retrofitting, this technique also offers the potential to augment strength and enhance load-bearing capacity without the need for increased section dimensions. Fabrics composed of carbon, glass, and basalt fibers are employed for this external reinforcement, each yielding distinct outcomes according to their inherent characteristics, load magnitude and type, fiber fabric type, and wrapping patterns. This investigation specifically examines the application of Basalt Fiber Reinforced Polymer wrapping in different patterns to structural reinforced concrete beams. These beams are subjected to a four-point loading configuration to analyze the performance of basalt fibers as a retrofitting material. Additionally, numerical models of these beams are generated using Abaqus software for Finite Element Analysis. A subsequent comparison is conducted between experimental outcomes and FEA results, involving Load-Deflection graphs and the assessment of tensile damage in the concrete beams.

This study aims to examine digital technology skills in developing professional development. The following enhancement came from the structured programs and activities related to giving insight into improving the quality of instruction performance among the public school educators. With the object of the study conducted among twenty-five teachers, the further detail of this study revealed that the initiative of developing digital technology skills in enhancing professional development could provide quality instruction performance. The contribution of this study pointed out the need to improve the strategic activities on empowering digital technology skills should come up with building knowledge comprehension, followed by the continued development of practices and processes. Moreover, the basis of developing digital technology skills has to be optimized through upgrading both textual and contextual basis in adapting and adopting the newly technical training arrangement for professional improvement with moral enhancement.

This paper aims to examine the arrangement of strategic planning for teaching performance through enhancing the readiness on employing the twenty-first century learning. The approach of this study was conducted at the public elementary school, Kuala Kangsar, Perak. Through quantitative approach, the respondent of this study was among 140 teachers. The empirical data were examined with the descriptive analysis in order to assist in dealing with the form of readiness reflected into building the beliefs and practices. The finding revealed that managing technology integration for teaching strategy is reflected into four main features of this study. Those are strategic planning of technology integration for curriculum material content, strategic planning of technology integration for instructional design, strategic planning of technology integration for responsive enhancement, and strategic planning of technology integration for instructional performance. The contribution of this paper aims to give insight into building the twenty-first century learning reflected into the strategic planning arranged into building the teaching performance. The further elaboration pointed out the critical insights into building the strategic planning for teaching performance reflected to improve the effectivity and efficiency of management and implementation scenario in improving the quality practice of teaching process.

IoT cloud platforms are used to put IoT systems into place and offer standard features and services. The paper includes a thorough case study featuring the integration of a DHT11 temperature and humidity sensor, a buzzer for auditory input, and the ThingSpeak Internet of Things (IoT) platform. The study intends to illustrate the usefulness of using ThingSpeak for real-time data monitoring and control in IoT applications. The DHT11 sensor is used to gather environmental temperature and humidity data, which is subsequently sent to the ThingSpeak platform. When predefined temperature or humidity thresholds are exceeded, the system triggers the buzzer to emit an audible alert. This functionality showcases the capability of ThingSpeak to enable real-time event-based responses. It demonstrates the adaptability and promise of ThingSpeak for such implementations while emphasising the value of real-time data collecting, visualisation, and responsive control in IoT applications.

Business Process Management (BPM) automates the business processes, improves the overall performance of automation by optimizing the business processes, and further aligns them with the business goals. Recently, Robotic Process Automation (RPA), which is based on software bots has gained huge popularity for business process automation due to its fast processing speed, low development cost, and less implementation knowledge required. Nowadays, researchers are focusing on combining BPM and RPA to gain the potential of both approaches. Initially, the present paper introduces the concepts of BPM and RPA and discusses the synergy between BPM and RPA. Then it provides an extensive review study of existing approaches, which are based on the integration of RPA-BPM. Further, it identifies the major research challenges associated with the existing approaches and defines a roadmap for future research directions in the said areas based on the identified issues. Furthermore, it redesigns the business process automation lifecycle while considering future perspectives.

‘Rural women’s entrepreneurship in the digital age’ aims to find out the opportunities for rural women and the problems faced by rural women entrepreneurs while accessing technology. Furthermore, this study wants to explore more deeply how these challenges can be managed by rural women entrepreneurs and their satisfaction with assistance from the government and financial support institutions formed to promote rural entrepreneurship. This study utilized a systematic literature review. The digital revolution in rural women’s entrepreneurship helps to maintain social relationships, survive competitors, gain information relating to the supply of available products, deliver services to customers, marketing of products through digital platforms, sell goods, acquisition of goods by customers on time, after-sales service and their valuable feedback or suggestions for improvement, etc. But many factors affect the non-usage of technology in their enterprise, such as lack of technical knowledge, need to depend on an expert for easy understanding, lack of family support, harassment through social networking platforms like social media, etc., higher cost for installation and maintenance, traditional mindset, being reluctant to change, poor management performance, etc. However, studies show that the use of digitalization by rural women entrepreneurs is increasing at an increasing rate. The study’s originality lies in presenting a structured, systematic, exhaustive, and in-depth literature review that defines comprehensive evaluation, and the information is provided to determine sustainability.

Creating a pendant for sending emergency 911 messages is a concept that involves both hardware and software components. Below, I'll outline a general idea of how such a pendant could work. Keep in mind that designing and implementing such a device requires careful consideration of technical, legal, and ethical aspects. Before proceeding with the development of such a device, it's crucial to consult with legal experts, emergency services, and relevant authorities to ensure compliance with regulations and ethical considerations.

A wireless prescription recommender for doctors would be a valuable tool that leverages technology to assist healthcare professionals in making informed and accurate prescription decisions. Such a system could help streamline the prescription process, reduce errors, and enhance patient care.

Software businesses are increasingly dependent on supply chains from several providers to receivers, like traditional business. Real-world software systems of today contain hundreds (perhaps thousands) of smaller programs and modules from various world-wide sources. During a cyberattack, these software supply chains get disrupted. Industry-wide standards that offer guidance to ensure supply chain security and integrity are yet to mature and are still evolving. In this paper, we address the need for a structured, organized approach to compile and automate the decisions related to software supply chain vulnerabilities and pave the way to simultaneous enable organizational knowledge capture and reuse. Specifically, this paper addresses broadly classifying supply chain vulnerabilities to define a scalable solution for software supply chain vulnerabilities, its modeling and evaluation, related metrics, and possible detection and response.

Due to the increasing number of investors in the past decade, the financial markets are now accountable for any country’s economic stability. Stock markets have grown increasingly unpredictable in recent years, yet they continue to be the most crucial for both investors and industries. There are several stock trading advice systems on the market that claim to be able to accurately predict future trends. Recommendation systems for stock trading are of great significance to a layperson who wants to benefit from stock trading despite not having a seasoned trader’s capacity or experience. The present study proposed a three-fold function. First, it provides a comparative analysis of the existing recommendation systems. It then applies and compares the results of the four Machine Learning models for price prediction on a real dataset. Finally, six sentiment analysis models are compared for the analysis of stock-based tweets. The best of the two are lastly integrated to arrive at a stock buy or sell recommendation.

This paper presents an innovative approach based on Federated Learning (FL) for detecting Elephant/Mouse Flows within distributed Software-Defined Networking (SDN) environments. In such networks, multiple domains are overseen by individual SDN controllers. Traditional centralized Machine Learning (ML) methods for detecting these flows involve training separate local models on data specific to each domain. This can lead to biased local models due to their dependence on context during data preparation. Consequently, these local models may struggle to adapt to different contexts and may not effectively handle the complexities of modern networks. To address these challenges, the proposed approach leverages the power of FL by fostering collaboration among controllers from various domains. This collaborative process aims to alleviate issues of local bias and, in turn, introduce greater adaptability and generalization to the detection process. Through experiments, we demonstrated the impact of data heterogeneity on the performance of local models and how the FL approach effectively mitigates these issues.