Digital forensics offers organizations more informed decisions and also helps in taking appropriate disciplinary actions. This field is also aids to criminal investigations and dedicated in uncovering vital evidence from digital devices like computers, smartphones, and storage media. With the Digital forensic, investigators are able in extracting data of communication including browsing habits, user activity, helping to build a stronger case or even exonerate the innocent. Because of criminals are now more tech-savvy, the system of digital forensics is dedicated and helps in justice system with complex digital footprints. As far as preventive role is concerned

Building on our previous work that examined intra-individual intercorrelations in differential psychological diagnostics, this paper focuses on the statistical verification of normative facet intercorrelations using

parametric bootstrapping[2]. Traditional methods rely on Pearson correlations and z-scores to compare subject profiles to a normative matrix[6][1]. However, these approaches assume a normal distribution of correlations—a premise that may not hold under all conditions[4]. Here, we describe how parametric bootstrapping, with direct normal approximation and Fisher-z transformation[2][3], can empirically assess the variability and distributional characteristics of correlation coefficients. The insights gained help establish an objective diagnostic threshold for identifying significant deviations in individual profiles. This methodological enhancement is critical to refining personality diagnostics, merging rigorous statistical validation with clinical applicability.

The convergence of fifth-generation (5G) wireless technology and the Internet of Things (IoT) is revolutionizing modern communication networks by enabling ultra-reliable, low-latency, and highthroughput connectivity. However, this rapid evolution raises critical concerns about energy consumption and sustainability, particularly as billions of interconnected devices become active. This paper investigates progressive trends in the integration of energy-efficient strategies within the 5G-IoT ecosystem. The study examines emerging technologies like edge computing, machine learning-based energy optimization, and energy harvesting techniques for their potential to balance performance and power consumption. The paper also discusses standardization efforts and green communication frameworks that guide the development of sustainable 5G-IoT infrastructures. Furthermore, real-world case studies and simulations are analysed to assess the impact of energy-efficient protocols and architectures on system performance. The results highlight that using a multi-layered strategy—mixing new network designs, energy management at the device level, and flexible protocols—is crucial for long-lasting sustainability. This research contributes to a more profound understanding of how next-generation IoT applications can align with global energy goals, paving the way for eco-friendly digital transformation. We also outline future directions for research and deployment, advocating for cross-disciplinary collaboration and policy support to enhance energy efficiency across the 5G-IoT spectrum.

The integration of blockchain technology into marketing strategies holds transformative potential for addressing critical challenges such as data security, consumer trust, and operational inefficiencies. As digital

dependency surged during the pandemic, businesses in e-commerce and related sectors faced the need for

innovative solutions to build stronger connections with their customers while adapting to evolving market

dynamics. Blockchain’s core features—decentralization, immutability, transparency, and secure transaction

enablement—position it as a disruptive force capable of redefining modern marketing approaches. This paper

introduces two conceptual frameworks, the Crypto Cart and Crypto Card, designed to leverage blockchain

for enhancing customer engagement and reshaping marketing paradigms. The Crypto Cart proposes a secure, traceable, and trust-building mechanism for product selection and checkout processes in e-commerce, while the Crypto Card envisions a blockchain-enabled payment solution that allows cryptocurrency transactions in physical retail spaces. These frameworks aim to revolutionize marketing by fostering trust, creating novel branding opportunities, and targeting tech-savvy, cryptocurrency-oriented consumer segments. Additionally, they promise to establish higher standards for transparency and security in digital transactions, providing businesses with a competitive edge. By exploring these concepts, this study highlights the potential of blockchain to drive marketing innovation, enhance customer experiences, and influence business trends in an increasingly digitized global economy. 

Information Technology (IT) has meaningfully transformed the educational landscape by reshaping how knowledge is delivered, received, and managed. It supports both teaching and learning processes, making

them more interactive, inclusive, and efficient. IT tools enhance the educational experience by enabling personalized learning, expanding access, and streamlining administrative and research functions. Educators

benefit from IT through ongoing professional development via MOOCs and webinars, helping them stay updated with global teaching practices. Digital assessment tools automate grading, track student progress, and provide insights through analytics. Learning Management Systems (LMS) like Moodle and Google Classroom streamline course delivery, assignment management, and student communication, enhancing teaching efficiency. IT enriches teaching and learning through interactive tools such as smartboards, multimedia projectors, and educational software, which simplify complex concepts by integrating text, audio, and visuals. Among the latest Information technology some of the emerging concepts are include Artificial Intelligence, Cloud Computing, Big Data, are important and this paper is focused on development of Education Technology with special reference to these three special technologies mentioned. 

The integration of Augmented Reality (AR) in healthcare systems has revolutionized medical practice globally, offering enhanced precision, efficiency, and patient care. In India, AR is emerging as a transformative tool, particularly in the areas of diagnostics, surgical procedures, medical education, and patient management. This paper explores the impact of AR on the digital healthcare landscape in India, analyzing its potential to improve healthcare accessibility, reduce errors in medical procedures, and streamline medical training. The review highlights the benefits of AR in telemedicine, surgical navigation, and remote diagnostics, addressing key challenges such as infrastructural limitations, high implementation costs, and regulatory hurdles that hinder its widespread adoption in the country. Furthermore, the role of AR in bridging the healthcare divide between urban and rural areas is discussed, along with the opportunities it presents for startups and public healthcare institutions. While AR offers immense promise for India’s healthcare future, the need for stronger policy frameworks, better funding, and greater technological awareness is crucial to unlocking its full potential.

AI has potentially altered and fine-tuned the modern intricacies of predicting and planning in business management by augmenting and automating the current systems in place. Because of AI interfaces, business

operations have reached unbelievable heights, but these developments also trigger tremendous financial

and legal problems. Underemployment because of AI, bias in algorithms, regulatory and compliance issues, and many more makes AI governance in management a thorny issue worthy of academic investigation. The purpose of this research is to investigate the phenomena of AI in business management from a legal and economic point of view in terms of innovations, risks, and governance structures. Using a combination of qualitative and quantitative methods, this thesis discusses AI’s impact on corporate governance, corporate finance, business intelligence, strategic planning, and the interrelated issues of regulation and compliance. The results of this study advocate for a regulated approach to the transition and transformation of businesses towards AI based systems to ensure transparency, responsibility, and ethical considerations. This research paper provides propositions for effective governance of AI in business management and highlights areas in which AI can help achieve sustainable business growth.

In the digital age, more technologies and services are entering existing markets than ever before, and new markets are emerging. This does not only mean “creating something new,” but also that by adding a slightly different perspective, a new path can be created. For example, a phenomenon currently taking place in the U.S. is that analog records are once again creating a market in the United States. Despite the age of Napster and Spotify, where music is data, vinyl records in the U.S. are now selling over 12 million copies a year, up more than 120% from the previous year, mainly among enthusiasts and young adults. The market, which was thought to have been completely replaced, is regaining momentum. Considering this, it is important to recognize the value of analog and to view it from a different perspective. Of course, if there is no value in the existence of analog, it will truly disappear, so it is important to be discerning about this, but even so, if we remove the existing concept of “analog” in the digital age (old and outdated), we can gain many different perspectives. This will be the same trend in various fields. It is difficult to understand the effect of looking at analog alone in a straightforward manner, but if we can visualize its value by making full use of digital and look at it from a broader perspective, the value of analog in the digital world will be enhanced

This study is to look into how Artificial Intelligence (AI) is influencing Human Resource (HR) practices at Multi National Company. The focus is on recruitment, employee engagement, and workforce analytics. This research identifies the ways in which AI technologies are being put to use in the HR process to enhance efficiency, decision-making, and overall employee experience. Specifically, it will address benefits and challenges presented by AI adoption in HR, its impact on organizational performance, and employee relations. This Study, therefore, through research, aims at coming up with a full understanding of the way AI is changing HR practices in the multinational company and availing insights that will reflect best practices of how AI can be leveraged to strategically achieve the set HR goals.

This study explores the dynamic role of social media in political marketing, focusing on its growing influence in shaping voter perceptions and campaign strategies. Utilizing a focus group methodology with experienced communication professionals, the research investigates key metrics, strategies, and the effectiveness of social media in political marketing campaigns. The findings provide insights into the impact of social media on voter loyalty, the comparative effectiveness of traditional versus digital platforms, and the significance of strategic communication in modern political discourse.

Ovarian cancer continues to be one of the most lethal cancers affecting the female reproductive system due to late-stage diagnosis and limited effective screening methods. Traditional diagnostic methods for ovarian cancer typically rely on imaging, histopathological analysis, and biomarker detection, which can be slow and prone to human error. Recent developments in the areas of Artificial Intelligence (AI) & Machine Learning (ML) collectively have demonstrated significant role in improving precision as well as efficiency while detecting and classifying ovarian cancer. The study in this paper explores the role of AI models that have been pre-trained, particularly deep learning architectures, to analyze various types of medical data, including histopathological images, radiological scans, and genomic information. By utilizing transfer learning, these models can efficiently extract important features from large datasets and adapt them for ovarian cancer classification with minimal computational demands. The suggested framework integrates Convolutional Neural Networks (CNNs) for analyzing images with conventional machine learning techniques to process genomic and clinical data. Experimental findings show that pretrained models significantly boost diagnostic accuracy, offering improved sensitivity and specificity over traditional approaches. Furthermore, methods like Grad-CAM have been employed so that it can increase the model’s transparency, aiding its acceptance in clinical settings. This study highlights the promise of AI-driven diagnostic tools in supporting detection of the disease early & tailored treatment for ovarian cancer. Findings of this paper highlight importance of integrating deep learning with specialized domain knowledge to improve diagnostic precision while reducing false positives and negatives. Future research will focus on expanding datasets,