Intelligent Strategies for ICT

This research analyzes the sound narrative of the video game Grand Theft Auto V and focuses on the missions of the character Franklin Clinton. For this reason, it was developed under the qualitative methodology and the observation method. In addition, an observation sheet was applied to detect the components of the sound narrative, such as diegetic and extradiegetic sound. In addition, we sought to identify the incidental and diegetic music that is presented within the video game. The study made it possible to analyze the 31 missions presented in this installment. The results were analyzed using descriptive statistics. The research showed that sound storytelling is important for player or multiplayer development in video games. Finally, it was identified that 42% of the sequences use voice as an immersive resource and 58% apply sound effects. Likewise, the use of music favors dramatic action (38%) and musical interaction in 62%.

The main objective of this research is to analyze the elements of audiovisual language in the video game Max Payne 1. The qualitative approach was used, applying the content analysis method. For the analysis of the two subcategories: image and sound, observation cards were used. The results were systematized and divided into nine indicators. The findings proved that open shots, such as the wide shot, wide shot, among others, show a greater presence in the cinematics and that noise (IN) is explicit in the diegesis of the video game. In conclusion, the audiovisual elements presented meet the objective of showing the player a broad vision of the story and contribute to providing an immersive experience in the video game.

Big data is a massive dataset, through which statistics, behavioral studies and trends are generated. In the journalistic field, it has been fundamental for the automation of texts, creation of new computer content and new technological tools. In order to document the available material on big data in the field of journalism, a systematic literature review was conducted in the Scopus database. We applied filters to narrow down the findings. From a preliminary result of 47 publications, 11 were chosen that met the purposes of the research. It is specified that most of the selected texts use qualitative methodology to document the experiences of using big data and artificial intelligence. According to the studies analyzed, it was found that artificial intelligence (AI) and the application of big data in journalism enable the creation of content, the implementation of platforms and fact-checking. It is concluded that big data and AI are increasingly used in journalistic work, so it is important to use them with a critical and ethical approach.

Few-shot learning is an area within the domain of machine learning that focuses on the challenge of training models capable of effectively performing new tasks using only a limited number of labeled instances. This contrasts standard machine learning approaches, wherein models are trained using large datasets of labeled instances. The problem of few-shot learning represents a significant challenge, yet its significance keeps rising due to the expanding volume of labeled data accessible for training machine learning models. This limitation arises from the fact that in numerous practical situations, it is often impossible to collect a large dataset comprising labeled instances for each specific task that requires resolution. The typical procedure involves two distinct stages: pre-training and fine-tuning. During the pre-training phase, a language model undergoes training using an extensive collection of textual data, which may consist of web pages or books. This approach aims for the model to understand language patterns and subsequently encode this knowledge within its parameters. The subsequent stage in the process is known as fine-tuning, which involves further training the pre-trained model using a smaller labeled dataset specific to the desired task. This process allows the model to adapt to the target domain or classification problem. This paper reviews recent few-shot learning (FSL) advances for natural language processing (NLP). It defines the procedure of few-shot learning in NLP, explores its associated challenges, describes the problem of few-shot learning, and discusses relevant learning problems.

Since the indigenous people have been the mutual chance to access the emerging technology in order to sustain their daily life needs, the essential to have the sufficient arrangement on examining in detail about the significance of the supporting technology should be taken into consideration in the attempts to give insights into the knowledge acquisition process. This article aims to examine the necessary support for the technology arranged to sustain the process of knowledge acquisition within the instruction process among the indigenous people. The literature analysis was conducted from the recent work related to the topic on knowledge acquisition process. The finding revealed that technology enhancement in supporting the process of knowledge acquisition process among the indigenous people refers to main features. Those are digital technology for proper knowledge acquisition process; digital technology for helping knowledge thinking process; and digital technology for facilitating positive life improvement process. The value of this study pointed out organizing the empirical literature analysis needs to address particularly in sustaining the digital technology in enhancing the indigenous people’s knowledge acquisition process from both theoretical and practical contributions.

This study aims to examine the strategic approach on adapting and adopting the teaching aids with the support of digital multimedia to help in teaching process amongst Islamic education teachers. The method of this study was through the qualitative approach with an interview design, observation and document review was used in this study. Through using a purposive sampling method, the sample of this study consisted of five Islamic education teachers and three trainee teachers. The results of research data from interviews were analysed using thematic methods and grouped according to themes to achieve the objectives of the study. The findings of the study show that all teachers know what is meant by the teaching aids as well as their function in the teaching of Islamic education and know how to use the digital media-based teaching aids and also the factors that support and prevent the use of media-based teaching aids in the teaching. The researcher in the observation section has included the method and use of each of the teaching aids while the factors that support the use are to attract students’ interest and facilitate the teacher's teaching. The hindering factor is more the lack of internet achievement and the lack of time which results in some teachers using other skills. In conclusion, this study shows the advantages and methods of using teaching aid based on digital media in the teaching of Islamic education. The contribution of this study shows that a qualitative approach using the interview method provides more in-depth data and can contribute to stakeholders more clearly.

Patients with knee osteoarthritis (KOA) have a significant reduction in their quality of life. Since the mid-twentieth century, the prevalence of KOA, a degenerative joint disease, has increased. The importance of early detection of longitudinal KOA grading for efficient monitoring and remediation has grown in recent years. Detecting and tracking the progression of KOA at an early stage is essential for preventative therapy. Kellgren and Lawrence (KL) developed a grading system used in clinical settings to categorize the severity of KOA; grades range from 0 (no) to 4 (severe). The human expert's evaluation of low-resolution images (i.e., X-ray images) leads to the wrong identification of disease. To solve this issue, in this research, we employed an optimized feature selection method to extract critical information from X-rays and built a deep learning (DL) model to accurately determine the degree of KOA from the radiometric images alone. There are four primary sections in the suggested approach. First, we developed certain processing methods for making X-ray images free from noise. Second, to retrieve the features from the images, we create texture- and color-based feature extraction methods. Third, an optimization method called the firefly approach is used to pick the most correlated features. Finally, to categorize the severity of KOA, we develop a convolutional neural network (CNN). The results of feature extraction with and without feature selection are used to train and validate the CNN. The performance of CNN by applying two different inputs is validated using the metrics. The results of the experiments demonstrate that the accuracy of the CNN model is improved by 2.5% thanks to the optimization technique in feature selection.

In this research paper, we propose a novel method for collecting data from ground-level sensors by using a fly-by drone that creates a Wi-Fi hotspot. The proposed approach involves deploying sensors such as DHT11, turbidity, and soil moisture on the ground and using a fly-by drone equipped with a Wi-Fi hotspot to collect data from these sensors. The drone will fly over the sensors and collect data through the Wi-Fi hotspot, which will be stored in a database for further analysis. The proposed approach has several advantages over traditional data collection methods, such as reducing the time and cost associated with manual data collection.

The current trends in the VLSI sector are low-power and high-frequency designs. Technology is getting smaller, design complexity is rising, and leakage power has significantly increased as a factor in total power dissipation. Optimization is necessary for lowering power and area requirements and improving performance. It can be done at several stages of a design flow, including synthesis, floorplan, placement and route, and clock tree synthesis (CTS). This paper primarily focuses on methods that optimize the design in terms of power and performance at synthesis stage. The type of VT cells utilized in the experiments greatly affected power and performance of the design. It draws attention toward the techniques employed to accomplish PPA goals, including ungrouping, boundary optimization, and clock gating.

This paper focuses on customer segmentation using the recency, frequency, and monetary (RFM) model and implements strategies to reward and notify loyal customers through email notifications. We analyze customer data using the RFM model, which evaluates customer behavior based on three factors: recency (how recently a customer made a purchase), frequency (how often they make purchases), and monetary value (the amount spent). By segmenting customers based on these metrics, we can identify loyal customers who have made recent, frequent, and high-value purchases. Once loyal customers are identified, the proposed work implements a reward system to encourage their continued loyalty. Rewards include giving reward points to loyal customers based on their position. To notify loyal customers about their rewards and keep them informed, the paper integrates an email notification system. Personalized emails are sent to loyal customers, notifying them of their rewards and expressing gratitude for their loyalty. By utilizing the RFM model for customer segmentation, businesses can identify and target their most loyal customers effectively. The paper provides a framework to leverage customer data, implement reward strategies, and utilize email notifications to optimize customer loyalty management.

In this problem, we predict the mechanism of action (MoA) of different drugs, which refers to how a drug produces its effect at molecular, cellular or physiological level. The aim is to predict multiple targets of MoA responses for thousands of drugs, based on features such as gene expression, cell viability, control perturbation type, time and dose, respectively. The dataset that we have used consists of over 200 targets of enzymes and receptors and over 5000 drugs. This is a multi-label classification problem. We have used various algorithms like random forest, XGBoost and neural network. We have evaluated the performance of different algorithms based on their log loss metric to determine the optimal model for our dataset.

Relational database management system was a popular choice for any kind of software development and design projects from its invention. The future of RDBMS is definitely graph databases with a NOSQL approach that goes beyond the relational model. Relational databases were extensively used for different kinds of data storage purposes. However, the increase in complex and interrelated data has revealed the limitations of relational database models. This paper examines the reconstruction process of transforming relational data into a graph database to overcome these limitations. We discuss the different types of graph databases evolved and explain how to migrate data in different models and methodologies. Because of the features of SQL and the powerful products of RDBMS, it is necessary to re-engineer traditional databases to NOSQL (non-SQL or non-relational) methods. Many software companies and research centres are trying to rebuild their legacy of RDBMS systems in NOSQL which can store different data as nodes, edges and relationships. Therefore, systems developed earlier in legacy systems need to be integrated with some methodologies in NOSQL which is a challenge for every business and organization.

As the time is passing, our dependency on intelligent machines is continually growing, which demands for more interpretable and transparent models. So, in any specific department, the real standard of artificial intelligence is judged by only if artificial intelligence is capable of interpret the model’s working which will generate users’ trust. Basically, explainable artificial intelligence or XAI targets to give a proper explanation of any particular machine-learning system, known as ML also, which empowers users (which are basically humans) to think, completely trust, and generate explainable models as much as possible. Choosing a suitable method for creating an XAI-enabled application needs a proper and deep perceiving of the basic logics within XAI and the associated methods. Among all the methods, black box-based artificial intelligence or AI method, for example, DNN or deep neural networks, has been broadly used for constituting prototype which are predictive and can interpretate typical relationship inside a dataset and can be used for making predictions for new unidentified data items. By using post hoc methods, one can explain inner working of these complex decision logic which is hidden from user. Basically, methods which are based on post hoc, approximate the working of black box nature by fetching rapports between predictions and values having different features. In this article, we discuss different XAI methods, specially post hoc method on different data item set. Using this taxonomy, also explain the various scenario on which post hoc can be applied and also elaborates how Post hoc provides better understandability and interpretability to users. This taxonomy can be used as a reference and comprehensive review of XAI technique qualities and elements for novices, researchers, and practitioners. As a result, it offers the framework for future research that is focused, use-case-oriented, and sensitive to context.

How to create computers that automatically improve with use is a topic that is addressed in machine learning. It is one of the fastest growing technical fields today, straddling the lines of artificial-intelligence, data-science, informatics, and statistical science. Machine learning has recently advanced both through the creation of new. The continued expansion of the amount of available Internet data makes it easier to study algorithms and theory and affordable computing. Using machine-learning techniques that require a lot of data can be enhanced by utilizing case-based decision-making in myriad regions viz. health, production, education, finance modelling, law enforcement, and marketing.

With the increase in working from home and other flexible working options working in virtual teams is almost imperative. As per Gartner’s recent survey, by 2020, there will be a 30% increase in working remotely and in virtual teams. In the present scenario, building an efficient virtual team is a dream for many organizations worldwide because of the tough challenges. Many organizations know about the challenges but cannot overcome them as they don’t have a simple solution. Many organizations fail miserably as they cannot find a proper solution. Many papers and research are being made, but authors mostly discuss the challenges without giving a suitable solution. The main objective of writing this chapter is to suggest a simple model with solutions, which has already been explained above. The model is very simple and user-friendly, which when followed as can yield excellent results leading to a hassle-free virtual team that is as good as an offline team and is equipped to handle any challenges and would be able to contribute to smooth running and profit maximization in the virtual mode.

This study has developed and empirically tested a proposed model to foresee the predictors influencing behavioural intention of GenZ to use ChatGPT. The study has attempted to explore the impact of behavioural biases viz. herding bias and optimism bias influencing behavioural intention to use ChatGPT of GenZ. In addition to this, the study has also tried to investigate if Gen Z individual’s profession moderates the relationship between their biases and intention to use ChatGPT. A cross-section approach was deployed, as a result of which 295 responses were gathered and chosen for further analysis. Smart PLS V4 was used for depicting the causal relationships and hypotheses validation. Our findings unveiled that both the biases positively and significantly influence the behavioural intention to use ChatGPT, however, profession does not moderate the relationship between the two. The results and findings of the study correspond with various technology adoption studies where in biases significantly impact the behavioural intention to use the technology. The findings of the study will certainly help ChatGPT developers, designers to inculcate these facts which would help them to come up with a more robust and better version of ChatGPT.

In recent years, digital publishing is replacing the concepts of conventional paper-based publishing. It is becoming popular due to the immense benefits that its offers as compared to the conventional publishing methods. Mistakes are an inevitable part of every publishing process. Even the most experienced editors miss an error or two while manual proofing. Despite adopting various smart automation systems in digital publishing, automation of quality checking still remains the grey area. This paper presents the development and implementation of an automated bot for checking the quality of all outgoing deliveries. The architecture of the AI-based system is discussed. The significant improvement in the throughput time is observed by adopting the proposed technology. Moreover, the systems offer better quality checking thereby reducing the mistakes while publishing the content digitally.

The Internet is a dangerous terrain! We hear all the time about websites going down due to denial-of-service attacks or dangerous information on the homepages. Multiple watchwords, dispatch addresses, and credit card data have been blurted into the social sphere in other high-profile cases, vulnerable website druggies to both embarrassment and fiscal threat. Anomaly detection plays a major role in this. It is a step of mining data that detect data points, events, and compliances that diverge from a dataset’s normal geste. Anomalous data indicate critical incidents, similar as specialized glitches, or implicit openings, in case of a change in consumer behavior. Involved dashboards, precisely tuned alert rules and quests through logs, making it a stoner experience. Machine learning has been proposed to improve detection technology for anomalies in recent years, especially in the field of anomalies detection. Here, the authors provide a survey/comparative analysis of published research articles on anomaly detection in correlation with web log analysis. The approach varies, from using traditional machine learning algorithms to two-step algorithms, from neural networks to unsupervised learning and natural language processing to modern methods.

The Internet of things expanded quickly, giving rise to numerous services, apps, sensor-integrated electronic devices, and related protocols, many of which are still under development. Improvements to network longevity and energy efficiency are the main goals of IoT-based WSNs, hence several clustering techniques are suggested to reduce energy consumption. To boost capacity in a multi-path channel environment, IoT devices are currently incorporated via several communication interfaces, often known as multiple-in and multiple-out (MIMO) in 5G networks. Every interface has unique qualities that could be desired as well as helpful in certain user contexts. A robust clustering approach for highly active IoT systems is lacking and must be developed in order to support a variety of user applications, especially as MIMO becomes more readily accessible on IoT devices. The energy efficiency and network longevity are enhanced in the proposed system, MIMO-based energy-centric routing protocol (MIMO-ECRP), for usage on the IoT in the 5G context as well as later on. The MIMO-ECRP method uses both types of topologies (i.e., a multi-hop configuration enabling inter-cluster communication or a single-hop configuration with intra-cluster communication) and the K-means-based network partition technique to cluster the nodes. In order to avoid redundant information transfer, threshold-based criteria have been provided in simulations as well as practical applications whilst minimal changes get observed. Also, the suggested MIMO-ECRP costs 20–30% less energy per cluster head than the prevailing approach. The simulation results reveal that when it comes to energy efficiency with longevity of networks, the proposed MIMO-ECRP method performs more effectively compared to previous advanced techniques.

Metropolises are increasingly challenged in moment’s times to ameliorate their competitiveness due to different reasons. Different strategic sweats are bandied about in planning lore, and new approaches and instruments are developed and applied, steering the positioning of metropolises in a competitive civic world. Still, there's some substantiation that public attention to megacity rankings is substantially concentrated simply on the species themselves, completely neglecting its meaning as an instrument for strategic planning. In order to unfold this implicit meaning of rankings the design is concerned with ranking different types of metropolises and introduces an own approach called ‘City Ranking’. Grounded on this ranking approach, with a number of megacity ranking systems, this study is an attempt to establish a relation between the megacity ranking and the preference of the people to live there. It'll show how this approach can be used as an effective instrument for detecting strengths and sins and perfecting a megacity’s competitiveness through applicable strategic sweats. The design is concerned with ranking the megacity according to some named parameters similar to demographics, population density, no. of days of extreme rainfall, quality of life, and crime rating index. The ranking of metropolises is to be shown on the dashboard according to each megacity’s scores.

The challenges stemming from crop diseases and a limited grasp of optimal fertilization practices have significantly burdened farmers, leading to reduced crop yields and a ripple effect of interconnected issues. This research paper introduces a novel solution in the form of a web-based application, meticulously crafted to assist farmers in swiftly identifying crop diseases, providing relevant precautions, and offering tailored fertilizer recommendations for specific crops. Notably, the application boasts a user-friendly interface, seamlessly catering to intuitive navigation and content comprehension, presented in the user's preferred language. Of notable importance, the application serves as a conduit for disseminating valuable insights concerning government initiatives, and effectively acquainting farmers with available incentives and authoritative helpline resources. By harnessing the power of this application, farmers can cultivate a comprehensive understanding of pivotal strategies for mitigating crop infections, potentially culminating in a significant upswing in crop production rates. We had achieved an accuracy of 98.98% in classifying the crop diseases. This innovative tool not only promises to effectively tackle longstanding agricultural challenges but also to equip farmers with actionable insights, thereby underpinning the advancement of agricultural productivity. In doing so, it lays the groundwork for the adoption of sustainable and prosperous farming practices, promising a transformative trajectory for farming communities.

This article focuses on advanced technologies and digital processes in dental implant surgery, in line with the principles of Industry 4.0. The human factors that are directly present in dentistry are taken into account. This gives reason to pay serious attention to the application of surgical guides in practice in order to support human activity. Surgical guides play a critical role in ensuring accurate implant positioning, supported by comprehensive examinations using dental impressions and CT scans. Using specialized software, digitally personalized surgical guides are designed to take into account the optimal implant position, angle and depth. These guides, created using CAD/CAM techniques, act as precise templates during surgery, guiding the dentist to place the implants precisely according to the pre-planned design. The surgical procedure involves careful site preparation and implant placement, followed by post-operative care to ensure successful healing and integration. By incorporating technological advances such as CT scanning, digital planning and 3D printing, the use of surgical guides exemplifies the potential of Industry 4.0 to improve the accuracy, efficiency and outcomes of dental implant surgery.

Generating images from text prompts is a fascinating and versatile area of research within the domains of artificial intelligence and computer vision. It involves using algorithms and models to convert textual descriptions into corresponding pictures. This technology has various practical applications, from creative content generation to aid in data visualization. In spite of recent developments in generative models, there are still several challenges that need to be addressed to improve the quality and diversity of the images generated through text prompts. These challenges can include issues related to realism, diversity, and the faithful representation of the textual descriptions. One specific challenge is dealing with complex input text descriptions. Text descriptions can vary greatly in complexity, and generating accurate and coherent images based on these descriptions can be difficult. Complex descriptions may involve multiple objects, detailed scenes, specific styles, and intricate color schemes. This paper presents a novel generative adversarial network (GAN) designed to generate high-quality pictures from text prompts. The primary goal of this new GAN model is to enhance the generation of coherent and contextually relevant images. In other words, it aims to generate images that make sense in the context of the given textual descriptions and are visually convincing. A key aspect of this framework is the concept of adaptive attribute modulation. The generator within the GAN has the ability to dynamically adjust various image features, including color, style, and object proportions. These adjustments are guided by semantic cues extracted from the input text. This enables the model to generate images that align with the textual descriptions in terms of visual attributes. To validate the efficiency of proposed model, we conducted experimental evaluations. They likely generated images from a variety of textual descriptions using our model and compared the results with existing methods. This comparison is essential to demonstrate the superiority or uniqueness of our approach.

This paper is on the realm of Real-Time Data and Analytics in the context of Building Management Systems for Energy Consumption Control. Given the escalating household electricity consumption and the integration of decentralized energy sources, the optimization of electricity procurement costs assumes paramount significance. An effective solution in this regard is the introduction of a Home Energy Management System (HEMS). HEMS operates autonomously, efficiently adapting to fluctuations in electricity rates and household energy consumption patterns. This paper delves into the architecture and efficiency-driven scheduling algorithms employed by HEMS. Additionally, it explores the integration of HEMS within the broader spectrum of cutting-edge technologies, encompassing smart grids, demand response mechanisms, intelligent home systems, innovative energy generation methods, advanced energy storage solutions, and their interconnections. The framework is built upon an Advanced Metering Infrastructure (AMI) and utilizes localized information management terminals for data storage and optimized scheduling. The objective here is to scrutinize the benefits and potential risks associated with Real-Time Data and Analytics in Building Management Systems for Energy Consumption Control, with the aim of cost reduction, risk mitigation, and the elimination of operational inefficiencies.

This research aims to help bind people using advanced technology for navigation purposes. In today’s tech-savvy world, where it is difficult to live independently or we struggle to live independently, this dissertation provides a way to help visually challenged people gain independence using sticks with effective sensors. The motive is to ease navigation with the help of area markers and location detection. Safety assurance is also provided to the user by enabling him to convey emergency situations to his family and friends. A literature review was conducted to analyze the present status of the product and the existing research available to suggest an updated product for the specific segment that will be intelligent and more user-friendly.

This study has explored factors like financial literacy and trust to predict the perceived usefulness of individual investors for RA services. The study has also attempted to check for the moderating impact of age and gender between financial literacy, trust and perceived usefulness of investors for Robo Advisory (RA) services. To empirically test this, a survey was conducted and in turn 497 responses were collected for further analysis. Smart PLS V4 was used for hypothesis testing. Our findings revealed that both the factors, financial literacy and trust, significantly and positively influence the perceived usefulness of investors for RA services. In addition to this, age and gender are not found to have an impact on PU of investors for RA services. The result and findings of the current study go in line with various other technology adoption studies. The findings of the present study will help designers and developers of RA services to come up with more concrete kind of RA services.

In recent days, students have completed their studies online and find it difficult to get placement. In this VUCA, corporates have stopped conducting campus interviews and direct recruitment throughout India. This leads to huge unemployment, and companies lack in finding the correct person for their job. To overcome this issue, it is proposed to develop an application where recruiters can easily conduct their recruitment process. This app integrates the student’s database and the industry/company database. This model helps the recruiter choose the eligible student from the huge database instead of a group of students from a particular University/college. There are many benefits like a faster recruitment process, many students finding their dream job, HR processing the interview from a remote location, the entire process being online, and no need to travel and accommodate a place for rent. The entire process is recorded and saved as a report; this ensures 100% genuine and no space for malpractice.

Since structural engineers are in charge of developing the structures for secure computer systems, they have the confidence to tackle larger and more complex structures subject to a variety of loading circumstances. To reduce structural engineers’ time, an effort is made to analyses and design beam-column junctions in multi-story buildings using ANSYS software. In order to make sure the building is safe, it is crucial to account for all potential loads while studying a beam-column junction of the building. The objective of the project is to describe the appropriate process for developing specifications and supporting conditions, types of loads, and load combinations, as well as designing geometry, etc. In order to determine deflection at the beam-column junction in the high-rise structure, ANSYS is used to analyses the transient loading. FRP headed bars improve seismic performance in RCC beam-column joints through corrosion resistance, ductility, energy dissipation, enhancing overall earthquake resistance and structural integrity.

This study tries to find out how technologies can be used in the healthcare sector in order to attain sustainable development. The researchers have taken up as a central focus of their study an enterprise started by a woman entrepreneur with two male colleagues in order to ensure good and sustainable services in the healthcare sector. The business venture was found to be a success with the help of technology and to provide not only satisfactory appropriate healthcare services but also provide training and employment to many. The researchers interviewed Ms. Ayesha Rabbi and her two colleagues Dr Kiran Rathod and Uday Rathod to learn about their three online medical services and how they contribute to sustainable development. Secondary research was also carried out by referring to research papers and articles from reputed research databases. The study showed that the enterprise consisting of three verticals, namely online medical retail outlet, online medicine selling and delivery, and medical coding and billing, all operating under the brand name Edox Healthcare Pvt. Ltd. contributes to sustainable development.

It is worth noting that the COVID-19 pandemic has spread at a high pace, and no one can say for sure where it is going to end. This epidemic has had several impacts on consumer behavior and digital marketing usage of businesses to engage customers and drive sales. Researchers have conducted an online survey to find out the impact of COVID-19 on digital marketing use and consumer behavior regarding the use of digital technologies. The study has also explored how customers started depending on their mobile devices and social media for virtually everything and what digital marketing channels companies can use to interact with their target customers. The data was collected from a sample of 114 participants who participated in this empirical study through Google Forms. The result indicated that post-COVID consumer behavior has changed a lot. They have become habitual to online shopping as it is in line with the marketer’s strategies. More E-commerce platforms and applications are in use, and this is going to be a regular behavior or the new normal.

The advancements in technology are witnessing a consistent change that is caused due to the several devices that stay connected to each other through the Internet. The smart device that we use in our day-to-day life is tracking the entire range of information and the activities that we perform have the data through different devices. These are the devices that are connected to each other through the Internet. Hence, the term Internet of things (IoT) has evolved. The devices include smartwatch, smart TV, smart car, the health monitoring system, and voice devices like Alexa which have huge data that is stored. This data could be saved on the local devices or the server or through Cloud. The data is shared from these devices via the Internet. Ensuring that the data is shared and retained safely is an extremely significant step. If the data is not safe and is prone to the attack by the attackers, the data can be leaked and can lead to subsequent challenges. In today’s world, the wearable devices are also utilized for the safety of women. The wearable devices can be worn like a regular fabric beneath the apparel. It is embedded with several inputs of information that would send alerts and notifications to the family and friends when there is an extreme situation.

The rapid advancement of technology has paved the way for innovative approaches to online education, aiming to provide a more engaging and immersive learning experience. In this paper, a college metaverse is built using Three.js and React Three Fiber, offering an interactive and collaborative virtual classroom environment. Students can join the virtual classroom as avatars, enabling them to actively participate in classes and interact with their peers. The synchronized whiteboard fosters real-time collaboration, allowing students to share ideas, contribute to discussions, and visualize concepts. This dynamic environment transforms online classes into interactive and immersive experiences, closely resembling traditional in-person learning. To enhance communication, we integrated Peerjs, enabling real-time audio transmission through WebRTC technology. Students can engage in live discussions, ask questions, and interact with the teacher and fellow classmates, fostering an engaging and interactive classroom atmosphere. In addition to the virtual classroom, metaverse also includes a library section where students and faculty can explore ongoing research projects undertaken by college members. Visitors can support these projects by funding them through a simple click, promoting collaboration and innovation within the college community. The seamless integration of technologies allows for a multi-user, interactive, and immersive learning environment that surpasses the limitations of traditional online classes. Overall, the paper presents an innovative solution to transform online education into a more interactive and engaging experience. It opens up new possibilities for students and faculty to connect, collaborate, and learn in a virtual environment that mirrors the dynamics of a physical classroom.

For the sake of the future of civilization, the water-energy continuum is a crucial and challenging problem that needs to be tackled. Additionally, the creation of freshwater uses a lot of energy. As a result, finding a practical solution to this problem is essential. Solar energy is currently one of the finest options for desalination because it is affordable, ecologically good, and widely available. It takes a lot of human effort and time to generate soft and suitable drinking water using the various sun desalination devices that have been built. In this work, prebuilt solar desalination systems are automated using IoT techniques and sensors. The use of IoT will help to reduce the amount of labour needed to operate the system. Using an Arduino and various sensors like pH, turbidity, and total dissolved solids, the water quality is assessed by eliminating the laboratory process of monitoring water quality parameters. The results obtained using this method are at par with those obtained using laboratory methods (Kedar et al. in Int J Photoenergy 6:1–3, 2021).

This study investigates the transformational potential of smart Governance in the tourism sector to enhance the operational effectiveness, transparency, and efficacy of governmental actions. This research synthesises the body of knowledge regarding the use of technology and data-driven methods in Governance using a literature review methodology. A conceptual framework is suggested to highlight the complex effects of smart Governance on many stakeholders in the travel industry. The study uses a multidimensional paradigm that includes agile leadership, stakeholder alliances, network management, and adaptive Governance. It explains how these complementary components construct a revolutionary ecology that encourages creativity, adaptability, and inclusive growth. Organisations can acquire insights into visitor behaviours, preferences, and traffic patterns by utilising data analytics and digital platforms, which can improve resource allocation, infrastructure construction, and policy formation. Applications that use real-time data enable dynamic crowd control, traffic optimisation, and safety improvements. The report also highlights how local communities may be involved in smart Governance to promote inclusive decision-making. This framework helps promote deeper study into the actual application and outcomes of smart Governance, which has the potential to change the travel sector. This multidisciplinary approach fosters resilience, innovation, and responsible, inclusive development. This study promotes real-world applications that fully utilise this synergy to further the interconnected objectives of sustainable tourism, bioeconomic growth, and efficient Governance.

This SQL injection and cross-site scripting (XSS) extension is a Google chrome-based software program that is designed to detect and prevent SQLi and XSS attacks on web applications. These types of injections or attacks are among the most common and dangerous threats to web applications, as they can allow hackers a loophole to access and avail sensitive data or take control of a website, SQLi, and XSS extension typically works by analyzing incoming data and queries for suspicious patterns and characters, such as SQL commands or HTML code. An SQL based injection and a cross-site scripting XSS extension is an essential tool for developers, security analysts, and administrators who want to ensure the security and integrity of their web applications, prevent data breaches, and protect their users from potential harm. Web applications require rigorous security measures to protect them from various malicious attacks or injections and prevent data breaches.

Implementing a pedestrian traffic light system that adjusts based on the density of people waiting requires a combination of sensors, data processing, and control logic. Developing such a system requires expertise in sensor technology, data processing, control systems, and transportation engineering. It's also important to collaborate with relevant authorities and stakeholders to ensure the system aligns with local regulations and serves the community's needs.

A voice-controlled domestic trolley is a device designed to assist with various household tasks while being operated through voice commands. It combines the convenience of a trolley or cart with smart technology to enhance its functionality.

Artificial Neural Network teaches computers to process data like human brains. ANN uses interconnected and layered nodes or neurons that resemble a human brain. The following eight case studies are reported in this paper: (1) scheduling of tasks in cloud computing , (2) scheduling on heterogeneous platforms, (3) predictive maintenance, (4) production scheduling using ANN, (5) train rescheduling, (6) micro-grid scheduling, (7) detection of obesity, and (8) prediction of falls in older patients. In every case study, the model is described, and the operating method is explained in detail. The recursive ANN is shown to accommodate large data. Neural network employing inhibitor neurons use 50% of neurons used by regular ANN. Genetic algorithm (GA) shows good performance in train rescheduling when the data size is small and ANN performs well when the data size is large. Multi-layer perceptron (MLP) method is found to work with 97% accuracy in predicting falls in older patients. Applications of each case study are highlighted to help the readers apply the right model for a particular application.

This paper proposes a new reliable and technology-based security system that is one of the most sought-after things in order to ensure security in an organization/building. The security of a place can be much better if there can be double-layered authentication of the authorized persons. Also, there are a number of instances where people are supposed to be away from their location and someone else from their organization needs to access the place in their absence. In these kinds of situations, monitoring the visitor and giving access from a remote place are what most people desire. A smart IoT-based security system can provide multi-layered security for authorized users and can also remotely monitor the system to give access to other unauthorized users after proper authentication.

Violence within journalism today has created difficulties in exercising the profession since their rights and physical integrity have been violated. That is why we seek to answer the following research question: What is known about journalism and the various acts of violence it faces documented in the Scopus base during the last five years? Likewise, the Scopus database was used to search for information, in which it was possible to collect 12 scientific articles in the Spanish language. For the present research, the systematic review of the scientific literature has been carried out under the PRISMA method. The research went through several limitations, among them that the information exceeded 10 years of research. On the other hand, potential areas of research were considered to be to seek and document more experiences in the field of journalism through qualitative research. That said, it was concluded that the articles studied indicate that journalism has suffered violence in the Latin American region, in the country of Mexico, Spain and Algeria during the last five years according to the investigations carried out between the years 2017–2022.