International Journal of Marketing Management
Editor- In-Chief:Dr. Gandolfo Dominici Title proper: International Journal of Marketing Management Subject: Marketing Management Publisher:Unicorn Publications ISSN: 2454-5007 Dates of publication: 2018 Frequency: Quatarly Language: English Country: India Medium: Online Indexed by: CROSSREF Indexed by: ROAD Indexed by: THE KEEPERS Title DOI of the Journal: http://doi.org/10.62652/issn.2454-5007
Theoretical Approaches to Destination Management: A Review of the Literature
This article reviews the principal theoretical approaches to destination management in tourism studies. It examines how the concept of destination management has developed in the academic literature and analyzes the main perspectives through which destinations have been understood, governed, marketed, and coordinated. Methodologically, the study is based on a literature review of scholarly works addressing destination systems, stakeholder relations, governance, destination marketing organizations, competitiveness, and sustainability. The review shows that destination management has evolved from a predominantly marketing- oriented concept into a broader analytical framework that incorporates governance, collaboration, resilience, and strategic coordination. The literature further indicates that destinations should be understood not as isolated tourism products, but as complex systems shaped by multiple actors, institutions, and interdependent functions. The article argues that no single theoretical approach is sufficient on its own. Rather, contemporary destination management research increasingly relies on the interaction of systems thinking, stakeholder theory, governance perspectives, and sustainability-oriented approaches. The study concludes by emphasizing the need for more integrative and context-sensitive research capable of linking theoretical models with the practical challenges of destination development and management.
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The Unemployment Paradox in Tamil Nadu: A Review of Growth Without Job Creation
Tamil Nadu is one of the most industrialized and economically advanced states in India, characterized by steady economic growth and structural transformation. However, despite this growth, unemployment remains a persistent challenge, giving rise to what is often termed as the “unemployment paradox.” This study examines the disconnect between economic growth and employment generation in Tamil Nadu using secondary data and existing literature. The analysis highlights factors such as technological advancements, skill mismatch, sectoral shifts, and informal employment that contribute to this paradox. The study emphasizes the need for inclusive growth strategies that prioritize employment generation alongside economic expansion.
Positioning Central Asia as an international tourism destination: a review of scholarly sources
This article reviews the scholarly literature on the positioning of Central Asia as an international tourism destination. The study focuses on the five countries of the region, namely Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan. Methodologically, the article is based on a literature review of peer-reviewed journal articles, books, book chapters, and related scholarly publications addressing destination positioning, destination image, tourism competitiveness, and tourism development in Central Asia. The review shows that the region possesses substantial competitive assets (e.g., Silk Road heritage, cultural diversity, distinctive landscapes). The literature indicates that Central Asia continues to face several obstacles, such as weak international visibility, fragmented destination identity, infrastructure and accessibility constraints, and limited coordination at the regional level. The findings of this paper suggest that the Silk Road remains the most powerful narrative for the region’s international tourism positioning, but that this framework must be supported by clearer country-level differentiation and more coherent destination strategies. A mechanism for further coordination is required.
international tourism, destination positioning
destination branding, tourism competitiveness,
Smart AI Blind Stick with Voice Feedback
The idea behind the "smart stick" is to provide the visually handicapped access to intelligent technological aids. Walking along the street, those with limited vision or no vision at all have a hard time seeing potential hazards. According to the developers, the system can detect objects, water, fire, and artificial vision using the Arduino UNO and Voice Module. They also claim to provide real-time assistance. Providing the visually impaired with well-founded help is the major objective of our mission. Moving from one place to another is the exclusive emphasis of current gadgets for the sight impaired. With this aid, people who are blind or have low vision can do many of the same tasks that sighted people do. Those who are unable to move about on their own due to vision impairments are the primary focus of our study. The technology is voice-activated and uses ultrasonic sensors for data collection. Our system's overarching objective is to help the visually impaired find their way around and avoid obstacles in an efficient and economical manner via the provision of scenario-based information on both static and moving objects. Making them feel and enabling them to walk on their own are the main goals. Buzzwords: RF module, voice module, ultrasonic sensors, Arduino UNO
A Deep learning voice Assisted System for Intelligent Indoor Navigation of the Visually Impaired
For the visually impaired, navigating inside spaces remains a major challenge, limiting their freedom of movement and autonomy. Guide dogs and white canes are helpful, but they can't convey complex geographical information. Navigational aids have come a long way in the last ten years, thanks to developments in computer vision and artificial intelligence. This research presents an artificial intelligence (AI) system for room and object detection that may help the visually impaired and those who are blind navigate interior spaces more easily. Our system sorts rooms according to the things they include using a deep learning model—specifically, YOLO for object recognition. This study also includes real-time audio output and depth detection to help users navigate better by measuring the distances of things near them. Additionally, the system incorporates voice narration to provide verbal explanations, warn users of difficulties, and show them alternative ways. A well-structured dataset optimized for indoor scene categorization with top-notch accuracy is also presented in this research. The goal of the solution is to provide visually impaired persons with a practical and easy-to-use gadget that bridges the gap between existing navigation help and full autonomous support systems. Object recognition, computer vision, room categorization, and deep learning are some of the terms used to describe assistive technology.
Real-Time Plant Leaf Disease Detection Using Machine Learning – Open CV
There is a major danger to agricultural productivity posed by tomato leaf diseases, which lead to significant drops in production. Accurate and prompt diagnosis of disease is crucial for prompt intervention and effective therapy. This research strengthens a Counting Sort-based feature engineering technique to provide a distinct deep learning method for disease identification in tomato leaves. Redundancy in features and computing complexity are common problems with traditional image-based methods for disease detection. We circumvent these problems by taking use of Counting Sort's efficiency to preprocess tomato leaf pictures and extract relevant features. Specifically, Counting Sort is used to examine the distribution of pixel intensities within certain color channels; this allows for the identification of significant textural and color-based traits associated with different types of sickness. To classify tomato leaves into early blight, late blight, and healthy disease groups, a Convolutional Neural Network (CNN) architecture is trained using these traits. To evaluate the efficacy of the proposed approach, a publicly available collection of tomato leaf photographs is used. Experimental findings show that traditional deep learning methods relying on raw pixel data or general feature extraction techniques are slower and less accurate than the Counting Sort augmented deep learning model. To improve agricultural practices and boost food security, this technology offers a practical means of automatically and efficiently identifying tomato leaf diseases. In this context,
refer to concepts such as deep learning,
automation, and sustainable agriculture.
Through the integration of Aadhaar identification with voter verification, the Aadhaar-based Voting System aims to improve the legitimacy, safety, and openness of the voting process. Duplicate voter entries, impersonation, and difficulties in validating voter identification are common problems with traditional voting techniques. Each voter is individually recognized before they cast their ballot using Aadhaar's biometric and demographic data. The system's goal is to make voting more efficient and less prone to electoral fraud by automating verification and cutting down on human error. Furthermore, it lays the groundwork for potential future developments, such as internet voting, while preserving the confidentiality of personal information and the reliability of the system. Integrating Aadhaar may make current voting systems more accessible and reliable, as this project shows.
Human Face Localization in 3D For Humanoid Robot Vision using ML
Robots, autonomous vehicles, security, and human-computer interaction are just a few of the many computer vision applications that rely on accurate three-dimensional human position data. Improving the Arslan humanoid robot's vision system so it can have a continual connection with humans in its field of view is the major emphasis of this study. Using two identical USB cameras and two pan cameras calibrated with servo motors for precise angle alignment, the system extracts depth information from photographs using human binocular vision principles. Taking pictures, identifying faces, comparing camera counts, and doing 3D localization are all part of the process. The most fundamental objectives are to maintain eye contact with people, to properly determine their exact positions, and to predict their approximate locations. Recent advances in methodology have substantially improved localization accuracy, leading to fewer errors and more reliability. The field of computer vision as a whole benefits from these developments because they pave the way for more natural and quick interactions between humans and computers.
The goal of this study is to create a model that can be used to design and manufacture cost-effective robotic arms by relating desired performances to the components that are necessary, their composition, and their cost. To establish this connection, one must be well-versed in the ideas of performance speed, power, accuracy, and precision. Rotational speed is a common measure of speed when discussing electric motors. The amount of force that a motor can transmit via an arm is called its torque, and it is a measure of its strength. The grouping of rounds determines their precision, whereas accuracy measures how near an average of many bullets is to the target's center. A robotic arm is comprised of many supporting components, the most important of which are the motors, bearings, and frame. All of these components are chosen according to the user's performance requirements. While servos and stepper motors require distinct mechanics and electronics to operate, they are both commonly used electro motors in arms used in residential and professional settings. In general, servos are more costly, have more features (such as internal position feedback, a feedback loop that can be connected to the outside world, and greater running speeds), and produce more torque. Stepper motors are often less expensive than servos, operate at low speeds, lack internal position feedback, and may be equipped with a gearbox to enhance torque. The formula for determining the necessary motor torque and, by extension, the motor's resolution, will be presented in this article. Deep groove ball bearings and angular contact bearings are the most common types of bearings used in robotic arms, however there are many others. Angular contact bearings work well with modest axial and radial stresses, such as those experienced at the arm's rotating base or inside the arm itself when dealing with high inertia. Deep groove ball bearings excel in applications where radial loads predominate, such as in an arm that handles low inertia loads. You may get the internal radial clearance by multiplying the bearing's bore and outer diameter by the arm's length; however, this leads to system imprecision, which is necessary for smooth and consistent bearing functioning. Using tubular material for the robotic arm's frame is recommended when the arm will be exposed to strong forces perpendicular to the arm. The optimal strength-to-weight ratio is achieved by using sheet metal when the arm is mostly used for light lifting loads. In order to improve this ratio, it is advised to use theoretical optimization tools. The displacement under load on the arm may be calculated using other modeling or finite element tools. In order to verify the accuracy of the models discussed in this article, a working prototype was created using the data given here. It was determined that the motor size model was correct. While the model provides a good approximation of the endeffector's accuracy, it fails to account for manufacturing errors.
AI Voice Assistant for Web-Based Smart Home Device Control
Home automation systems, in which a building can intelligently react to environmental changes and the needs of its occupants, have recently emerged with the rise of the Internet of things and smart cities. Some kind of control, ideally wireless, that facilitates communication between the structure and its occupants is required for the establishment of various parameters inside such an ecosystem. Voice controls are only one of several forms of controls. This article details the process of creating a voice assistant that can manage various electrical devices present in a home automation system. Various software and hardware tools have been used in the construction of this system, including but not limited to: Visual Studio Code, Google Firebase, MAMP, jQuery, PHP, and Arduino UNO. A web platform has been established that allows for the control of a number of sensors, including a gas detector, a presence detector, a temperature and humidity sensor (DHT11), and a relay for lighting control. The process of creating the manual connection and how it has progressed to voice control have been detailed.
IoT-Based Voice-Controlled Smart Home Automation System Using Arduino and Firebase
Using a Remote Closed-loop Control System to Assess the Performance of an Embedded Speed Control System for a DC Motor
The purpose of this article is to assess how well an embedded platform for a remote access virtual laboratory functions. In this lab, we'll be focusing on the standard issue of DC motor speed regulation. A single- board computer running a control algorithm based on Python is linked to the embedded platform via USB protocol, and it may be accessed remotely from numerous sources. The embedded platform is thoroughly analyzed in this article, which also includes experimental findings and analysis. The following terms are used in the index: embedded platform, remote access laboratory, DC motor, speed control, and Python.
Utilizing YoLOv8 and LSTM for Worker Mobility Tracker-Based Helmet and Equipment Detection in the Mining Industry
Ensuring staff safety in mining operations requires close monitoring of PPE and compliance with safety regulations. However, due to the unpredictable and hazardous nature of mining circumstances, it is not feasible to send people below. Two essential components of worker safety—real-time detection of helmets and PPE as well as worker mobility—remains a challenge for most current systems. This project's strategy for overcoming these issues is to improve security and monitoring via the use of machine learning technology. A state-of-the-art object identification model called YOLOv8 is used by the system to allow real-time detection of workers' helmets and personal protective equipment (PPE). In addition, the work makes use of LSTM, a subset of RNN, to monitor employee actions both in the present and in the past. Whether it's dangerous motions or ones caused by exhaustion, the system can identify them at 10-millisecond intervals and take preventative actions to ensure the user's safety. By integrating YOLOv8 and LSTM, the proposed approach eliminates room for human mistake and boosts workplace safety to an impressive 95.9%. This cutting-edge technology is revolutionizing mining safety solutions by enhancing operating efficiency and offering continuous monitoring. The following terms are used as keywords: helmet detection, PPE, worker mobility tracking, RNN, LSTM, YOLOv8.
Machine Learning for Optimizing PV/Wind Smart Grids Using an Improved Spider Wasp Optimizer Algorithm for Learning in Order to Reach SDG7
In 2015, the Sustainable Development Goals were established by the United Nations to guide nations in their pursuit of a future free of environmental degradation. The seventh Sustainable Development Goal is to provide universal access to affordable, reliable, clean, and sustainable energy. To meet everyday needs in light of changing energy sources, it is important to modernize power systems. The incorporation of smart grids into the power system simplifies complex energy networks. Smart grids and converter autonomy are two additional benefits of integrating Distributed Generation (DG) with RESs like wind and photovoltaic (PV) systems. Nevertheless, smart grids still have difficulties with frequency fluctuations due to generation and load imbalances, as well as fault breakouts. While most studies have focused on steady-state models employing machine learning (ML), this study intends to examine robust performance optimization and talk about future ML applications that might aid with dynamic behavior during fault breakouts. A smart grid power system model is developed in this study to aid in the achievement of SDG7. The main improvement is the way the gains are adjusted using meta-heuristic optimization procedures. This research takes into account the operating system's constraints and uses machine learning based on the enhanced spider wasp optimizer (SWO). We ran the simulations in MATLAB. The system's use of SWO led to good outcomes. Subjects—intelligent grid, optimization, green power, AI, errors
Detection of AI Generated Text with BERT Model
The major goal of the vast undertaking is to distinguish between AI-generated content and human-crafted writings within a huge database of more than 28,000 compositions that include both sources. An advanced machine learning model that can differentiate between papers written by students and those produced by LLMs is the backbone of the project. This dataset is mostly used for essay text classification tasks because to its binary labeling approach, where 0 represents human-written pieces and 1 represents AI-generated stuff. Working together, we can strengthen the body of models that can detect tiny differences between human and AI written language, which will help protect written material from the increasing demand for its authenticity.
Machine Learning Models based Mental Health Detection
There has never been a century like this one, and the load it will place on students and subsequent generations will be enormous. Massive strain, societal problems, rivalry, and stress will be experienced by generations to come. A race will be imposed on their life. Negative effects on pupils' mental health might have far-reaching consequences. Mood disorders (bipolar illness), anxiety disorders, depression, eating disorders, and sleep problems are five of the most prevalent forms of disorders, particularly among young people. In keeping with machine learning's central role in making people's lives easier, this study use ML algorithms to screen for mental health issues using the Mental Disorder Questionnaire (MDQ).
Leveraging Machine Learning Algorithms for Threat Detection Using AI-Enhanced Cyber security Datasets
To determine how well Logistic Regression, Decision Tree, and Random Forest, three machine learning algorithms, work for threat identification, this research used an AI-enhanced cybersecurity events dataset. In order to maximize the model's performance, the dataset is preprocessed with cyber incidents. With higher scores for accuracy, precision, recall, and F1 score, Random Forest was the top algorithm in the test. Compared to Logistic Regression and Decision Trees, it is better able to handle complicated, high-dimensional data, which means it can catch nuanced patterns of cyber threats.
AI-Based Multimodal Resume Ranking Web Application for Large Scale Job Recruitment
In this work, we provide a web tool that ranks resumes and uses sophisticated deep-learning algorithms to make the recruiting process better. Segment identification on resumes of different structures is handled by the YOLOv9 model that has been fine-tuned using our newly-created custom dataset. Easy OCR is used for text recognition, mBERTfine-tuned for text classification, and GLiNER is used for named entity recognition with regular expressions.
Verifying Bank Checks Using Deep Learning and Image Processing
By combining deep learning with image processing, our novel method utterly transforms the way bank checks are verified. We drastically cut down on human intervention to improve the efficiency of the check truncation process. Our method quickly extracts crucial information from the checkbook, such as the bank's branch code, the check's number, the exact amount, the account number, and the distinctive signature patterns. Our research made use of the IDRBT cheque dataset and deep learning convolutional neural networks.
Explore how generative AI may improve cyber security and threat intelligence.
More and more, businesses are relying on generative AI to fortify their threat intelligence and cyber defenses. A kind of artificial intelligence known as "generative AI" is able to generate new data independently of preexisting data and human expertise. Decision support systems may now swiftly and automatically detect hacker or malicious actor risks by using this technology, which considers several sources and data points. Generative AI may also aid in the detection of infrastructure weaknesses, which further lessens the likelihood of an assault succeeding. Because of the need of quickly identifying threats and implementing defensive measures, this technology is ideal for use in security operations centers (SOCs). Generative AI may serve as an extra line of defense for companies in the face of increasingly sophisticated assaults by include significant and intriguing data elements that were previously overlooked.
An Exploration of Emotions Through Visual Content Tracking Using Machine Learning — EmoEyes
Dealing with the complex problem of emotion detection via eye expression analysis is the main emphasis of this research. Everybody knows that your eyes are like windows into your head; they reveal a lot about your true feelings. By capitalizing on this strong association, we provide a novel approach to emotion detection that makes use of careful observation and interpretation of the nuanced details shown in eye expressions.
Sustainable Agriculture with Deep Learning-Powered Pest Classification
This research presents a method for determining the helpful or detrimental effects of pests on crops. The first part of the study is an analysis of the strengths and weaknesses of several pest detection techniques. A classification strategy that uses convolutional neural networks (CNN) to construct the model for pest detection and classification is suggested as a solution to the issue. It was trained using a dataset that included pictures of several pest species. The system was verified against many conventional classification methods after undergoing extensive testing on a big dataset.
The Telugu-Speech Bot for Agriculture and Crop Prediction using Machine Learning
To aid farmers, the "Agriculture Voice Bot Integrated with Crop Recommendation using ML in Telugu" project presents a novel method of voice-based interactions that include both broad questions and tailored crop suggestions. This system uses conversational chatbot technology so farmers can ask questions and get answers much like popular voice assistants like Alexa, Siri, Cortana, and Watson. Improve your farming skills, boost your agricultural production, and get access to crop forecasting aid with the help of this voice-enabled assistant that was made for the agricultural sector. The system uses Natural Language Processing (NLP) to examine user queries, pull relevant keywords, and obtain accurate answers from a vast Knowledge Base. The system also uses Google Text-to-Speech (gTTS) technology, which allows for the production of voice answers in Telugu, to help in Telugu communication. This project is set to change the game when it comes to agricultural help by using Machine Learning (ML) methods and delivering solutions in Telugu. It will be a user-friendly and effective solution that is suited to the requirements of farmers. Subjects: Voice Bot, Machine Learning Algorithms, Natural Language Processing, Telugu, Speech-to- text Translation, Google Text-to-Speech.
FINITE ELEMENT–BASED STRUCTURAL ANALYSIS AND DESIGN OPTIMIZATION OF AN RCC SUSPENSION BRIDGE DECK USING STAAD.PRO
Bridges are vital components of transportation infrastructure, ensuring safe and efficient movement of vehicles and pedestrians across obstacles such as rivers, valleys, and roadways. This project presents the comprehensive analysis and design of a Reinforced Cement Concrete (RCC) bridge using STAAD
PERFORMANCE-BASED STRUCTURAL ANALYSIS AND DESIGN OF A G+4 RESIDENTIAL REINFORCED CONCRETE BUILDING UNDER GRAVITY AND LATERAL LOADS
Rapid urbanization and population growth have increased the demand for multi-storeyed residential buildings, especially in urban and semi-urban regions where land availability is limited. The present project deals with the analysis and design of a G+4 residential building using both manual calculations and STAAD.Pro V8i software
PERFORMANCE-BASED ANALYSIS OF A G+6 REINFORCED CONCRETE SHEAR WALL STRUCTURE UNDER EARTHQUAKE LOADS
The principle objective of this project is to analyse and design a multi-storeyed residential building [G + 6 (3 dimensional frame)] using STAAD Pro
PERFORMANCE-BASED STRUCTURAL ANALYSIS AND OPTIMIZED DESIGN OF AN AUDITORIUM BUILDING USING STAAD.PRO
The analysis and design of reinforced cement concrete (RCC) structural elements require a clear understanding of fundamental design parameters and engineering notations used in structural calculations.
A Study on Marketing Techniques for Highlighting CSR Achievements in Global Markets
Corporate Social Responsibility (CSR) is now a core part of business plans. It affects how consumers act, how people view a brand, and how well a company does in the market. This study looks at different ways global companies communicate about CSR and market it. It also examines how these methods affect consumer trust, sales, keeping customers, and company standing. The results show that CSR messages work better when they fit the local culture, are clear, and involve different groups. This is true in both established and newer markets. Also, CSR actions related to the environment and ethics affect customer loyalty and brand trust more than just giving to charity. The study stresses that companies need to measure CSR results. They can use things like how happy customers are, how people see the brand, and how much money they get back from their investment. This helps make sure CSR plans work well and are believable over time.
A Study on Skills-Based Hiring and Workforce Transformation
The evolving nature of work, driven by digitalization and artificial intelligence, has accelerated the shift from traditional qualification-based recruitment to skills-based hiring. This study explores the concept of skills-based hiring and its role in workforce transformation, with a focus on how organizations adapt talent management practices to meet emerging skill demands. The paper examines the impact of digital tools and AI on recruitment processes, internal mobility, and workforce planning, highlighting their contribution to organizational agility and performance. It also discusses the challenges and ethical considerations associated with skills-based hiring, including bias, transparency, and data privacy. The study concludes that skills-based hiring, when supported by continuous learning and ethical governance, serves as a sustainable strategy for building an inclusive, adaptable, and future-ready workforce.
RESTAURANT BOOKING SYSTEM USING MERN FULL STACK
The Restaurant Booking System using the MERN full stack is a web-based application designed to simplify and automate the process of reserving tables in restaurants. The system enables customers to view restaurant details, check real-time availability, and make reservations online. It reduces manual booking errors and enhances customer convenience. Restaurant owners can manage bookings, menus, and customer data efficiently. The application leverages MongoDB for scalable data storage, Express.js and Node.js for backend services, and React.js for an interactive frontend. Secure authentication ensures user data protection. Real-time updates improve booking accuracy. The system supports multiple restaurants and time slots. Automated confirmations enhance reliability. It minimizes waiting times for customers. The system improves operational efficiency for restaurants. Cloud deployment ensures accessibility. The application is responsive across devices. Data analytics support business insights. Role-based access improves control. Notifications enhance communication. The system is modular and scalable. It supports future integrations. Overall, it modernizes restaurant reservation management.