Volume 12 | Issue 5 |

 Abstract

The widespread adoption of on-demand mobility services has spurred significant advancements in cab management systems. However, existing solutions often exhibit limitations in areas such as real-time optimization, user experience personalization, and integration with sustainable transportation strategies. This review paper critically examines current cab management systems, dissecting their strengths, shortcomings, and underlying technological approaches. Through a comparative analysis, recurrent patterns and potential areas for improvement are identified. Informed by this assessment, the paper proposes a conceptual framework for a next-generation cab management system. This enhanced system aims to leverage advanced optimization algorithms, machine learning techniques, and innovative incentive mechanisms to achieve greater efficiency, rider satisfaction, driver empowerment, and alignment with environmentally conscious transportation goals.

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 Keywords

cab management, ride-sharing, optimization algorithms, machine learning, user experience, sustainable mobility

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Agricultural fruit diseases cause economic losses to far mers. Monitoring the health of the traditional pomegra nate crop and diagnosing disease is highintensity, diffic ult and timeconsuming. However, recent advances in i mage processing and computer vision offer opportuniti es for disease detection in pomegranate plants. In this a rticle, we provide an overview of the imaging technique s used to detect pomegranate disease. In this article, we provide an overview of the methods used to detect pom egranate diseases. We also discuss the challenges of dia gnosing diseases in images and highlight the potential o f deep learning to achieve accurate diagnosis.

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 Keywords

Pomegranate, K-means, SVM (support vector machine), CNN, Softmax layer

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The proliferation in phishing attacks highlights the importance of strong cybersecurity protocols.. In this study, we present an innovative methodology that harnesses machine learning techniques to enhance the detection of phishing websites. Phishing attempts persist as a considerable risk to both individuals and organizations, underscoring the essential requirement for enhanced detection methods.. Leveraging the power of machine learning, our study outlines a systematic methodology for identifying phishing websites. We begin with a thorough data collection process, followed by preprocessing steps to refine the dataset. Feature extraction methods are then utilized to capture pertinent patterns suggestive of phishing endeavors. The core of our approach lies in the application of various machine learning algorithms for classification, enabling the automated identification of phishing websites. By conducting thorough tests and assessments, we showcase the efficiency and resilience of our detection system. By contributing to the advancement of cybersecurity measures, this research aims to empower users and organizations in combating phishing threats, thereby fostering a safer online environment.

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 Keywords

Phishing, Machine Learning, Cybersecurity, Detection Mechanisms, Feature Extraction, Classification Algorithms.

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Through communication, people can engage and share thoughts and feelings. There are several obstacles in the way of the deaf community's social interactions. The individuals use sign language to communicate with one other. In order to communicate with regular people, a technology can convert sign languages into a form that is understandable. The goal of this project is to create a real-time text-to-Indian Sign Language (ISL) translation system. Most of the work is done by hand. In this paper, we present a deep learning technique for classifying signs using a convolutional neural network. Using the numerical signs and the Python-based Keras convolutional neural network implementation, we first build a classifier model. Phase two involved using a second real-time system that located the Region of Interest in the frame that displays the bounding box using skin segmentation. The segmented region is fed into the classifier model in order to forecast the sign. For the identical subject, the system's accuracy rate is 99.56%; in low light, it is 97.26%. The classifier was seen to be becoming better with different background and angle of image capture. Our approach focuses on the RGB camera system.

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 Keywords

Deep learning, convolutional neural networks, regions of interest, and real-time systems.

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Sign language serves as a vital mode of communi- cation for the Deaf and Hard of Hearing (DHH) community, yet barriers persist in its recognition and translation. This project addresses these challenges through innovative technological solu- tions. The primary objective of this study is to develop a robust system for sign language recognition and translation, aiming to enhance communication accessibility for the DHH community. Leveraging advancements in machine learning and computer vision, the project seeks to overcome existing limitations and revolutionize inclusive communication technologies. The project employs a multifaceted approach, integrating Long Short-Term Memory (LSTM) networks and MediaPipe technology to ac- curately detect and interpret sign language gestures. Through extensive training and validation processes, the system is opti- mized to achieve high levels of accuracy and efficiency in real- world scenarios. The developed system demonstrates exceptional performance, achieving a recognition accuracy rate of 98% for sign language gestures. Moreover, it seamlessly translates these gestures into both spoken and written English and Marathi, offering real-time, context-aware translations. This project rep- resents a significant advancement in addressing communication barriers for the DHH community. By providing an accessible and inclusive means of communication, the developed system has the potential to revolutionize interactions and promote equality across diverse domains. The success of this endeavor underscores the importance of leveraging technology to foster inclusivity and enhance the quality of life for individuals with hearing impairments.

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 Keywords

Machine Learning, MediaPipe, OpenCV, LSTM Neu- ral Network, Sign Language, PIL, GoogleTrans

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The research analysis on sign language crosses multiple fields and academic areas. These days, the two primary fields of research in gesture recognition are data glove use and visual sign language recognition. While the latter records the user's hand features with the camera for the purpose of identifying and translating sign language, the former uses the information collected by the sensor for these purposes. Deaf and hard to hearing individuals typically employ sign language as a form to interact both within and outside of their own community. In this language, communication is facilitated through hand gestures, which is particularly essential for individuals who are deaf and mute. The goal of SLR is to recognize these hand signals and translate them into spoken or written language.Within this domain, hand signs are classified into types: dynamic and static. While recognizing static hand gestures is generally easier, the recognition of both dynamic and static gestures is valued by the community. Hand gestures could be recognized using Deep Learning Computer Vision and Deep Neural Network concepts (Convolution Neural Network designs). The model will learn to recognize the hand gesture photos over the course of an epoch.

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 Keywords

Hand gestures, computer vision, text-to-speech, convolution neural networks, and recognition of sign language

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 Abstract

Nowadays the Mastercard blackmail is the best issue and by and by there is need to fight against the Visa deception. "Visa blackmail is the most well-known approach to cleaning untidy money, likewise making the wellspring of resources at this point not conspicuous." On steady timetable, the financial trades are made on tremendous aggregate in overall market and hence recognizing charge card distortion development is trying undertaking. As earlier (Against Mastercard blackmail Suite) is familiar with separate the questionable activities yet it is significant simply on individual trade not for other monetary equilibrium trade. To Vanquishes issues of we propose artificial intelligence method using 'Hidden Closeness', to perceive typical acknowledges and lead for other monetary equilibrium trade. Area of charge card distortion trade from gigantic volume dataset is irksome, so we propose case decline procedures to decreases the data dataset and a while later find sets of trade with other monetary offset with ordinary credits and lead.

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credit card fraud, fraudulent activities, SVM (SUPPORT VECTOR MACHINE), Harr cascade Algorithm, Face Recognition.

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In the digital age, the security and integrity of communi cation and data transfer has become important. Centralized syste ms face challenges in managing data confidentiality, preventing ta mpering, and ensuring authenticity. To solve these problems, this article proposes a new way to create secure communication and data transfer using blockchain technology. Known for its distribution and immutability, blockchain has a unique advantage in increa sing security and trust. By leveraging decentralized blockchain da ta, encryption mechanisms and smart contracts, we can create a strong infrastructure for secure communication and data transmission. Key elements of the process include self-governance using blo ckchainbased digital signatures, data encryption using strong encr yption algorithms, smart contracts automati on, decentralized management for redundancy and fault tolerance,and an immutable audit method for transparency and accountability. . Thanks to the integration of blockchain technology, users can enjoy enhanced security, privacy and accuracy in communicationand data transfer activities. The system reduces the risks with unauthorized access, interception and leakage of information on by ensuring that only authorized individuals access and interact with data. This article provides concept and design considerations for using secure communication and data transfer using blockchain technology. It highlights the benefits and challenges associated with these systems and provides insight into future research directions to improve their effectiveness and application potential.

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 Keywords

blockchain, secure communication, decentralization cryptography, access control, smart contracts, data encryption, authentication, data integrity, trust, privacy protection

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The Recommendation system is an essential part of any E- commerce website. The main aim of the system is to provide effective suggestions to the user. Generally, a lot of methods are used for the filtering but Interactive recommendations is the trending methods. This paper implements the Interactive recommendations based on Neural networks. The IR can be further modified into chatbots. Chatbots are the part of the system where the user can interact by themselves with the system to get what they really expect. Such systems improve the user experience and increase the business profits. Further this way is much faster than other methods. The System Analytics gives the glimpse of how the conclusions are drawn or how the products are selected. The Analytics is another important part of any system as it graphically or pictorially represents the system and makes it easy to understand. This part is essential for the owner as this will help to visualize the highly bought products and the users who bought it which can be used to give more suggestions to the users. The consideration of more than one purchases of the user allows a more detailed and accurate suggestions. This is where the neural networks come into consideration as they store all the previous interactions and not only single interaction.

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 Keywords

Interactive Recommendation (IR), System Analytics, Neural Network, Chatbots

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Machine literacy ways have revolutionized the field of healthcare by enabling accurate and timely complaint vaticination. The capability to prognosticate multiple conditions contemporaneously can significantly ameliorate early opinion and treatment, leading to better case issues and reduced healthcare costs. This exploration paper explores the operation of machine literacy algorithms in prognosticating multiple conditions, fastening on their benefits, challenges, and unborn directions. We present an overview of colorful machine literacy models and data sources generally used for complaint vaticination. Also, we bandy the significance of point selection, model evaluation, and the integration of multiple data modalities for enhanced complaint vaticination. The exploration findings punctuate the eventuality of machine literacy inmulti-disease vaticination and its implicit impact on public health. Once further, I'm applying machine literacy model to identify that a person is affected with many complaint or not. This training model takes a sample data and train itself for prognosticating complaint.

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 Keywords

Machine Learning Disease Prediction, Disease data, Machine Learning.

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