VOLUME 13, ISSUE 1, JANUARY 2024

Abstract: The devices applicable in the Internet Of Things (IoT) have developed to comprise embedded systems and sensors which has the ability to connect, collect, and transmit data over the Internet. Although there exist solutions to secure IoT systems, but the resources to support such solution are insufficient, and considered constrained in terms of secure communication. The aim of the study is to secure the entire path in an IoT environment into two segment, which comprises of securing data through encryption and decryption, using Cryptography and Steganography techniques. The approach introduced in this project makes use of both steganographic and cryptographic techniques. In Cryptography Rivest, Shamir, Adleman (RSA) is used, while in Steganography Image Steganography for hiding the data is applied. Also, the Mutual Authentication process to satisfy all services in Cryptography which include; Access Control, Confidentiality, Integrity, Authentication were employed to maintain the data more security. Having use RSA algorithm for securing the data and again on this we perform Steganography to hide the data in an image, such that any other person in the network cannot access the data present in the network. Only the sender and receiver can retrieve the message from the data.

Keywords: Design, Internet of Things (IoT), implementation, cryptography, steganography, security, communication, environment. Cite: Gloria Obiageli Obuseh, Ikechukwu Innocent Umeh, Ikechukwu Udoka Onwuegbuzie,"DESIGN AND IMPLEMENTATION OF SECURITY FRAMEWORK TO ENHANCE INTERNET OF THINGS ENVIRONMENTS USING CRYPTO MODEL", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 1, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13101.

Abstract: Using creative approaches, this paper focuses on (a) evolution of the world from the beginning up to the birth of the solar system, (b) condition of the early earth before the beginning of life on it, (c) before Alcmaeon’s discovery of brain, emotions and intelligence were believed to be in the heart, (d) development of stress-anxiety controlled LEGO robot started in 2023, and (e) current development of MUSE-2-controlled LEGO robot and (f) use of diamond neural probes to study complex life forms and (g) possible creation of new life forms. Cite: Dean M. Aslam,"Big Bang, Life, Humans, Brain/Mind Roles, Life Creation, and Mind-controlled Robots", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 1, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13102.

Abstract: The Internet of Things (IoT) has revolutionized healthcare by enabling the collection of vital patient symptoms and data from various devices. This data-driven approach forms the basis for a multi-agent model called Smart Patient-Centered Health Consultancy Model (SPCC-Model). This paper explores the constructs and sub-constructs of the SPCC-Model, encompassing IoT technology, technological infrastructure, government policies, multi-agent systems, analysis of patient data, classification analysis, and cause-effect analysis. This model integrates IoT technology, data analysis, healthcare providers, and patient preferences to deliver personalized and timely healthcare services. The model incorporates data collection, preprocessing, feature extraction, predictive analysis, continuous monitoring, and user interaction to provide personalized healthcare services. By leveraging IoT data, the SPCC-Model aims to enhance patient-centered care, improve healthcare outcomes, and ensure compliance with healthcare regulations.

Keywords: Data Analysis, IoT Devices, Healthcare, Multi-Agent Model, Patient-Centered Care, Predictive Analysis, Healthcare Technology, model validation. Cite: Daniel Khaoya Muyobo, Paul Oduor Oyile, "DEVELOPMENT OF SMART PATIENT-CENTERED HEALTH CONSULTANCY MODEL: USING INTERNET OF THINGS BASED MULTI-AGENT", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 1, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13104.

Abstract: The convergence of the Industrial Internet of Things (IIoT) and Artificial Intelligence (AI) has given rise to Industry 4.0, creating a wealth of opportunities for manufacturing companies. Nevertheless, the adoption of this paradigm shift, particularly in smart factories and production, is still in its early stages and faces several obstacles, such as substandard data quality and fragmentation, resulting in limited insight driven IIoT innovation. To overcome these challenges, this article highlights a decentralized architecture that utilizes emerging multi-party technologies, privacy-enhancing techniques like Federated Learning, and AI approaches. The proposed approach strives to establish a cross-company collaboration platform and a federated data space that addresses the fragmented data landscape. Federated Learning is one way to enable the sharing of confidential data generated from various IIoT devices. However, traditional Federated Learning raises privacy concerns. This review introduces the basics of FL, describing its underlying implementation of technologies, advantages and disadvantages, and recommendations, along with privacy-preserving methods. Most importantly, this work contributes to comprehending a broad range of FL current applications and future trends in technology and markets today.

Keywords: Federated Learning, Cybersecurity, IoT, Edge Computing. Cite: Deepak Kumar, Priyanka Pramod Pawar , Hari Gonaygunta , Shoumya Singh, "Impact of Federated Learning on Industrial IoT - A Review", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 1, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13105.

Abstract: "Machine Learning Cryptocurrency Prediction for Bitcoin and Ethereum" aims to develop a predictive model using historical data to predict the future prices of Bitcoin and Ethereum. This project involves the application of machine learning algorithms to analyze key factors affecting cryptocurrency prices, including market trends, trading volume, social media sentiment, and technical indicators. Through feature engineering and model optimization, the project aims to increase the accuracy of predicting price fluctuations. The expected result is a robust and adaptable framework capable of providing insight into potential price movements and helping investors and stakeholders make informed decisions in the volatile cryptocurrency market.

Keywords: Machine Learning, Time Series Analysis, Sentiment Analysis, Regression Analysis, Deep Learning and more. Cite: Prof. Pravin M. Tambe, Siddhi D. Rasal, Pooja A. Sangle, Kaveri B. Gorane, "Review on: Machine Learning Cryptocurrency Price Prediction", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 1, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13106.

Abstract: Financial crimes pose a significant threat to the stability and integrity of financial systems, necessitating advanced technologies to mitigate risks. It can be challenging to identify financial cybercrime-related activity because, for instance, an extremely restrictive algorithm might prevent any suspicious activity that would impede legitimate customer transactions. Financial institutions face challenges beyond just navigating and identifying legitimate illicit transactions. Customers and regulators are increasingly demanding transparency, fairness, and privacy, which places special restrictions on the use of AI techniques to identify fraud-related activity. This research paper aims to investigate the pivotal role of Artificial Intelligence (AI) in the early detection and prevention of financial frauds within the global banking sector. The study delves into the background of financial crimes, reviews relevant literature, explores AI technologies used in intelligent banking, provides recommendations for enhanced prevention strategies, and concludes with the potential impact of AI on global banking.

Keywords: Machine Learning Algorithms, Natural Learning Processing, Predictive Analysis, Blockchain Technology, Pattern Recognition, Data Analytics. Cite: Waheeduddin Khadri Syed, Kavitha Reddy Janamolla,"Fight Against Financial Crimes – Early Detection and Prevention of Financial Frauds in the Financial Sector with Application of Enhanced AI", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 1, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13107.

Abstract: In the transportation industry, a significant challenge pertains to the assessment of vehicle damage. The conventional manual inspection process is time-consuming and inefficient. This creates an opportunity for automation in the vehicle insurance sector, particularly in the utilization of image-based methods for expediting claims processing. Leveraging photographs taken at the scene of accidents can streamline the entire process, resulting in cost savings, enhanced driver convenience, and improved overall efficiency. In the realm of the automobile insurance industry, a substantial financial resource is currently allocated to address claims leakage, which is the disparity between the most favourable and the real settlement of insurance claims. Traditional practices predominantly rely on visual examination and validation methods to mitigate claims leakage. However, these inspection processes often prove to be time-consuming and contribute to the delay in claims processing. The implementation of an automated system for inspection and validation presents a valuable opportunity to expedite this crucial process, thereby enhancing overall operational efficiency and ensuring a more streamlined claims settlement procedure.

Keywords: Vehicle damage assessment, Claims processing, Transfer learning, Pre-trained VGG, Deep Learning. Cite: Prof. Ravindra Mule, Sushain Gupta, Abhishek Thakare, Sahil Savardekar, Jigar Sable, "Advancements in Computer Vision for Car Damage Detection and Assessment: A Comprehensive Study", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 1, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13108.

Abstract: Cyberbullying has arisen as an unavoidable and concerning issue via virtual entertainment stages, influencing the psychological well-being and prosperity of people around the world. To resolve this issue, this study proposes a cyberbullying recognition framework utilizing the K-SVM calculation. Utilizing the force of AI, the framework means to consequently distinguish and signal occurrences of cyberbullying progressively web-based entertainment content. The improvement of the location framework starts with the assortment and naming of a thorough dataset containing instances of cyberbullying and non-cyberbullying posts or remarks. After pre-handling the text information by eliminating unessential data, changing message over completely to lowercase, and tokenizing it, significant highlights are removed utilizing the pack of-words or TF-IDF methods. These changed element vectors act as contributions for preparing the K-SVM classifier, which tries to find the ideal hyper plane for successfully recognizing cyberbullying from non-cyberbullying content. The K-SVM model's performance is evaluated using a distinct testing dataset, with metrics such as exactness, accuracy, review, F1-score, and ROC-AUC broken down to determine its feasibility in identifying cyberbullying situations. Model calibrating is led through trial and error with different K-SVM hyper boundaries and cross-approval methods to upgrade the framework's exhibition.

Keywords: Cyberbullying, Support vector machine, Machine learning, social media, Classification. Cite: Dr.E. Mohanraj, Eniyavan N, Sidarth S, Sridharan S, "Detection of Cyber Bullying on Social Media Using Machine learning", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 1, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13109.

Abstract: One of the necessities for daily life is now the internet. Through the internet, knowledge and information are broadly accessible to all people. However, blind individuals have trouble reading these text resources and utilizing any online service. The development of computer-based accessible solutions has greatly expanded the opportunities available to the blind worldwide. Blind persons have benefited greatly from using audio feedback-based virtual environments such as screen readers in accessing online apps. We outline the architecture of the voicemail system so that a blind person can effectively and readily access emails. Audio-based environments, screen readers, and many other features have helped blind individuals to take advantage of the workspace. Private information must now be sent via email. One technological tool that aids in corporate facilitating communication and enabling users to send emails to one another. The main objective is to develop an email system that is voice-based for those who are visually challenged or blind, enabling them to send and get emails on a computer. It will leverage the latest features to produce an atmosphere that is advantageous for visually pushed individuals to work without assistance from outside sources. Keywords: Accessibility, Computer-based accessible solutions, Audio feedback-based virtual environments, Audio-based environments. Cite: Prof. Vikas Nandgaonkar, Siddhant Mane, Soniya Bhoite, Mansi Kakade, Prajwal Bhosale, "A Review on Voice-Based Email System for Visually Impaired", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 1, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13110.

Abstract: In recent years, the animation industry has undergone a paradigm shift, with the infusion of Artificial Intelligence (AI) revolutionizing the creative process. This research paper endeavors to provide a thorough investigation into the application of AI in animation generation, offering insights into the technological advancements that have reshaped traditional animation techniques. By exploring various AI algorithms and techniques, this study aims to unveil the potential impact and implications of AI on the creative landscape of animation Cite: Vignesh J R, B S Mahalakshmi,"Exploring the Transformative Integration of Artificial Intelligence in Animation Generation: A Comprehensive Analysis", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 1, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13111.

Abstract: The advent of the Internet of Things (IoT) has revolutionized various sectors, including safety and emergency response systems. Among these, the development of advanced fire detection systems using IoT technology has emerged as a critical innovation in ensuring safety and mitigating fire-related hazards. This paper presents an IoT-based fire detection system designed to provide rapid, accurate, and automated responses to fire incidents. The system integrates various sensors, including flame, smoke, and heat detectors, to identify potential fires swiftly and accurately. These sensors are interconnected through an IoT network, facilitating real-time monitoring and instant alerting mechanisms. Upon detection of a fire, the system triggers immediate alerts to property owners and emergency services through calls, SMS, or applications, ensuring prompt awareness and response. Moreover, the system incorporates automated response features like water sprinklers, which activate upon fire detection to control or extinguish the flames, thereby preventing the spread of fire and minimizing damage. The integration of GPS technology enhances the system's effectiveness by providing precise location data, ensuring that emergency responders can quickly locate and address the fire source. The IoT infrastructure allows for remote monitoring capabilities, making it possible to maintain vigilance over properties even when unattended. Furthermore, the system's expandability means it can adapt to include new sensors and devices, continually enhancing its safety features. Data collected over time can be analyzed to identify common fire hazards and refine preventive measures, making the system an invaluable tool for safety management. The IoT-based fire detection system represents a significant advancement in fire safety, offering rapid detection, real-time alerts, automated responses, and data-driven insights, all of which contribute to protecting lives and properties from the devastating impact of fires.

Keywords: Fire Detection, Smart, analysis, Automated. Cite: Dr. Shobha T, Soumya Ranjan Sahoo, "Fire Detection Using IoT", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 1, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13112.

Abstract: Advancements in technology and globalization have made digital cameras widely accessible and affordable. As a result, people capture and collect numerous images using various camera sensors, often in soft copy for online documents and sharing on social media. Following the explosive use of social networking services, there has been an exponential increase in the volume of data of image. Moreover, the development of image processing software such as Adobe Photoshop has given rise to doctored images. These manipulated images can be used for malicious purposes, such as spreading false information or inciting violence. This image spam detection program allows users to detect even the smallest signs of fraud in images. With the rise in crime, image fraud has become a major problem that needs attention. Moreover, the main goal of forgery detection in the digital age is to ensure immaculacy and validity. As research progresses, many deep learning methods are being implemented to identify fraud in images. Deep learning approaches have shown much better results for image manipulation compared to traditional methods. In this study, we have also aimed to determine the detection of image forgery using a deep learning approach. We propose a novel image forgery detection system based on Convolutional Neural Networks (CNNs) that can detect various types of image modifications, such as copy-move, splicing, and resampling. Our proposed system integrates Error Level Analysis (ELA) with deep learning techniques to provide an accuracy of 93% for detected images. Our proposed system even integrates Visual Geometry Group; it is a standard deep Convolutional Neural Network (CNN) architecture with multiple layers. After evaluating the proposed system on a database of real-world images and achieving a high detection VGG16's training accuracy of 93.21% and a training accuracy of 95.12% for VGG19. VGG16 is the first VGG network and VGG19 is the last hence we decided to use both of them as they give better accuracy than any other networks.

Keywords: digital cameras, doctored images, malicious purposes, authenticity, integrity, image forgery, deep learning, CNN, ELA, VGG16, VGG19 Cite: Prof. Swati Dronkar, Mauli Bhalotiya, Yashshree Bidkar, Mayank Futane, Umesh Amru, "Image forgery Detection", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 1, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13113.

Abstract: This research explores the integration of GPT-based language models in the healthcare sector, focusing on Adaptive Intelligence. It delves into the transformative possibilities and profound implications of incorporating these models into critical healthcare domains, such as clinical decision-making, medical imaging, and personalized medicine. Demonstrating remarkable adaptability, these models offer innovative solutions to dynamic medical challenges. However, adopting adaptive intelligence requires careful consideration of ethical boundaries, including patient data privacy, transparency, and legal compliance. The outlined strategies encompass dynamic adaptation, cross-domain knowledge transfer, and robust validation processes, laying the foundation for deploying GPT-based models in diverse healthcare settings. Looking forward, imminent advancements in medical research and shifts in clinical practice demand solid policy frameworks to address emerging challenges. Collaboration among ethicists, clinicians, data scientists, and policymakers is paramount to establishing guidelines ensuring the appropriate and responsible use of adaptive science. As the healthcare landscape evolves, the research emphasizes the critical role of interdisciplinary collaboration in unlocking the full potential of GPT, promising advancements in patient care and healthcare delivery. The study anticipates a future marked by transformative changes in medical research paradigms and underscores the need for comprehensive policy frameworks to navigate forthcoming challenges.

Keywords: Adaptive Intelligence, GPT models, Large Language Models, Healthcare Integration, collaboration. Cite: Karthik Meduri, Hari Gonaygunta, Geeta Sandeep Nadella, Priyanka Pramod Pawar, Deepak Kumar,"Adaptive Intelligence: GPT-Powered Language Models for Dynamic Responses to Emerging Healthcare Challenges", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 1, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13114.

Abstract: Room Dhundo is a website that helps students find budget-friendly and mid-range rooms in any area in the city. It also provides a mess service, where students can get tiffin services or mess services located within the area. Additionally, Room Dhundo provides students with information about the best street food locations in each area. Room Dhundo is a user-friendly website where students can search for budget-friendly and mid-range rooms by area, location, price, and other criteria. They can view photos of each room before renting and share rooms with other students to save money on rent and utilities. Room Dhundo also helps students find compatible room partners. Students can create a profile that includes their personal preferences, and then search for other students who share their interests. This can help students find room partners who are compatible with their lifestyle. The mess service is a convenient way for students to get their meals delivered to their rooms. This can help students find room partners who are compatible with their lifestyle. The mess service is a convenient way for students to get their meals delivered to their rooms. Students can also set a delivery time for their tiffin or they can visit the mess. The street food information is a great way for students to discover new and delicious food options. The website lists the best street food locations in each area, along with photos and reviews. Room Dhundo is a valuable resource for any student looking for a place to stay in the city. The website is easy to use, affordable, and provides students with all the information they need to find the perfect place to stay. Room Dhundo is also a great way for students to connect with other students who are looking for a place to stay. The website has a forum where students can post questions and answers, and there is also a chat feature that allows students to connect with each other in real-time. Overall, Room Dhundo is a valuable resource for students who are looking for a place to stay in India. The website is easy to use, affordable, and offers a number of features that are specifically designed for students. Cite: Aniket Kumre, Bhuvanesh Dongarwar, Himanshu Parate, Tomeshwar Selukar, Prajwal Ganvir,"Room Searching Application", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 1, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13115.

Abstract: During the conventional agricultural era, farmers are not keen on increasing production due to the lack of effective approaches for diagnosing diseases in different crops. Early identification of crop diseases is vital as it significantly influences the growth of plant species. While a variety of Machine Learning (ML) models have been employed to organize and identify agricultural diseases, recent advancements in Deep Learning (DL) provide substantial promise for enhancing precision in this domain. The suggested approach accurately and efficiently detects crop disease symptoms by using a neural network based on convolution (CNN). Model performance is evaluated using a variety of efficiency signs, which show how effective it is in early disease identification. The report fills research gaps for reliable disease detection approaches and offers a thorough investigation of deep learning frameworks for crop disease visualization. The suggested convolutional neural network approach seeks to transform the plant leaf diseases identification, including those that do not yet exhibit symptoms. Expanding on prior work emphasizing the significance of plant disease identification, this research introduces an advanced solution. With the support of an automated system and the deep learning algorithm Convolutional Neural Network (CNN), people can identify illnesses with cell phones. Furthermore, a stunning 99.81% classification accuracy is achieved by integrating DenseNet-121 into the framework, demonstrating its superiority over other models. This method has the potential to transform crop disease detection and boost food security and agricultural output.

Keywords: Convolutional Neural network, DenseNet- 121, Alternaria solani, Phytophthora infestans, Machine Learning, early bright, late blight. Cite: Prof. Himanshu V. Taiwade, Rakesh Nagrikar, Diksha Narekar, Prashik Nagrale, "MONITORING PLANT DISEASES USING A DEEP LEARNING – BASED APPROACH", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 1, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13116.

Abstract: Rainfall prediction is a crucial aspect of meteorology and environmental planning. This abstract presents an overview of the subject, highlighting its significance and methods. Rainfall prediction involves the use of various meteorological data sources, including historical weather records, satellite imagery, and atmospheric parameters, to forecast when, where, and how much precipitation will occur in a specific region. Machine learning models and statistical techniques play a vital role in this process, enabling the development of accurate predictive models. These models consider factors such as temperature, humidity, wind patterns, and geographical features. Accurate rainfall prediction is essential for agricultural planning, disaster management, and water resource allocation. Additionally, it aids in mitigating the impact of extreme weather events and climate change. Advances in technology and data collection methods continue to enhance the precision and reliability of rainfall predictions, contributing to more informed decision-making and the overall resilience of societies and ecosystems.

Keywords: Rainfall, Rainfall prediction, Geographical feature, Classification, Random forest, Decision tree, Machine Learning Cite: Prof. Swati Dronkar, Vaishali Bisne, Shrutika Kumbhare, Vaidehi Gotmare,Shikhar Pathak, Shreyash Rahangdale, "Rainfall Prediction", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 1, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13117.

Abstract: In today's digital generation, we have access to an enormous amount of information. However, it can be a time-consuming task for users to read, sort, and summarise all this information. To address this challenge, a method called multi-document summarisation has been developed using a rule engine. Multi-document summarisation involves the extraction of valuable and relevant information from a collection of uploaded documents. It aims to create concise and coherent summaries of the content within these documents. This approach is essential in various applications, including market reviews, search engines, and business analysis. Summarising multiple documents in this manner enables users to quickly obtain the necessary information from the entire set of referenced documents. The specific approach used in multi-document summarisation is extractive, which means it selects and combines existing content from the source documents to create a summary. The goal of the research paper mentioned is to employ a rule engine to summarise text from multiple documents using an extractive approach. This will result in a coherent and meaningful output, benefiting users in managing and extracting information from a large volume of documents.

Keywords: Document summarization, Extractive approach, Rule Engine, domain oriented Cite: Virendra Yadav, Anshul Gedam, Sanskar Korekar, Pranay There, Sujal Bitle, Tarang Bhaisare, "Multi Document Summarisation Using Rule Engine", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 1, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13118.

Abstract: This study focuses primarily on plagiarism, which is prevalent in schools and colleges. Many students have been found to have copied assignments from their classmates. A system could be developed for the convenience of teachers that could check the amount of plagiarism in students' assignments. This system could be mentioned as an improvement from the old manual way as it eliminates the ability to steal someone else's ideas or work and passes it off as their own. Plagiarism has been classified as a moral rights infringement in a number of countries. It has become increasingly common in today's environment of changing technology and ever-increasing Internet usage.

Keywords: Plagiarism, data mining, stop word, hash tag, cosine similarity, cleaning, and stemming. Cite: Aryan Patil, Harsh Bagde, Prathmesh Kharwade, Tanmay Khobragde, Yash Shembe,Prof.M.Gotaphode, "ONLINE PLAGIARISM CHECKER WITH OCR", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 1, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13119.

Abstract: Neural Cryptography represents an innovative intersection of cryptography and neural networks, particularly in the realms of cryptanalysis and encryption. This paper aims to showcase the capacity of Neural Networks to perform symmetric encryption even in adversarial scenarios, drawing inspiration from previous works in this domain. The fundamental goal of cryptography is to create a cypher that is resistant to deciphering without the corresponding key, thus safeguarding the plaintext. Messages are encrypted using robust cryptography, rendering brute-force attacks against the algorithm or key nearly insurmountable. Robust cryptography achieves this by utilizing exceptionally lengthy encryption keys and encryption algorithms resistant to various forms of attacks. The integration of neural networks marks the next evolutionary phase in the evolution of secure encryption. This paper delves into the practical application of neural networks in cryptography, exploring the development of neural networks tailored for cryptographic purposes.

Keywords: Cryptography key, encryption system, encryption algorithm, artificial neural network, chaos maps, logistic encryption. Cite: Asst. Prof. Jyotsna Nanajkar, Sakshi Shinde, Piyush Mishra, Sanjeev Pandey, Ankit Tiwari, "Neural Network based Message Concealment Scheme", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 1, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13120.

Abstract: The sentiment analysis system presented in this project employs a methodology rooted in manual keyword analysis, capitalizing on the inherent associations between specific words and emotional sentiments. For instance, in movie reviews, positive expressions are characterized by terms like "great" and "love," while negative sentiments are often conveyed through words such as "hate" and "awful." By quantifying the frequency of these selected keywords, comprehensive feature vectors are constructed to capture the nuanced sentiment of input data. Cite: Anuj Pund, Tanmay Harde, Atul Awasarmol, Siddhant bodele, Prachee Meshram,Dr. P. M. Chaudhari, "Sentiment Analysis of Students Reviews using Natural Language Process", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 1, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13122.

Abstract: Agricultural productivity and sustainability are vital for global food security, but challenges like soil degradation, crop diseases, and inefficient fertilizer use hinder crop quality and yield. To address these, advanced technologies like machine learning and AI are increasingly used in agriculture. This review focuses on recent advances in soil prediction, crop disease prediction, and fertilizer recommendation systems. Soil prediction models assess nutrient content and pH levels using data sources like satellite imagery and historical records, aiding precise soil management. Crop disease prediction systems use AI to identify and forecast disease outbreaks, leading to early warnings and reduced agrochemical use. Fertilizer recommendation systems employ machine learning to suggest optimized fertilizer usage, enhancing efficiency and reducing environmental impact and costs. AI integration has the potential to transform agriculture, promoting sustainability and higher yields. Ongoing research and interdisciplinary collaboration are needed to overcome challenges related to data accessibility and technology integration. Harnessing AI-driven solutions can lead to a more resilient and sustainable agricultural ecosystem, combining advanced technology with traditional agricultural wisdom to ensure a food-secure future.

Keywords: Agricultural productivity, Crop diseases, Crop yields, Fertilizer, Soil , Crop disease. Cite: Ayushi Gajbhiye,Madhav Murkute, Najuka Anjankar, Ayush Hedaoo,Prof.Virendra Yadav, "Soil, Disease Prediction & Fertilizer Recommendation", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 1, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13123.

Abstract: The online banking environment has evolved significantly in recent years and will continue to do so as financial institutions strive to provide their customers with the ability to transfer money, pay bills and access important information online. During this period, online banking has been plagued by criminals and internet fraudsters attempting to steal customer information. Phishing, pharming and other types of attacks are now common knowledge and are frequently used by fraudsters to obtain customer information and gain access to online banking accounts. Therefore, the authentication of customers logging into their online banking has become a key concern for financial institutions. This study paints a clear picture of the need for stronger authentication in online banking. It highlights the key security concerns and criminal activities driving the need for stronger authentication, as well as the growth of the online channel driven by consumers and financial institutions. In this study, we have proposed a new authentication system for online banking that uses the mobile WBS in combination with the QR code (a variant of the 2D barcode) to address all these vices of the Internet. 

Keywords: E-Authentication, QR code, OTP, Pathway, Security, Cryptographic Key, Barcodes, USB keys, Interoperable. Cite: Ebem, D. U., Chukwu, E. G., Ekwe, O. P., Anozie E. L., Achi, I. K.,"DEVELOPMENT OF AN ONLINE BANKING AUTHENTICATION SYSTEM USING SINGLE-USE MOBILE PASSWORDS AND RAPID RESPONSE CODES", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 1, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13124.

Abstract: In today's interconnected world, communication technologies play a pivotal role in enabling social interactions and fostering connections. The integration of these technologies with the internet has revolutionized communication, expanding its reach and capabilities. However, individuals with physical disabilities often encounter challenges in utilizing these technologies due to visual and motor impairments. Despite technological advancements, many solutions remain inaccessible to a significant portion of the population. Our system aims to bridge this gap by simplifying communication for novice and physically disabled users, eliminating the need for prior training. The system's innovative approach utilizes voice conversion as the sole input method, eliminating the need for keyboard interaction. This feature caters to individuals with limited digital literacy, enabling them to send emails with ease and confidence. The system's user-centric design is further enhanced by its responsive voice interaction approach, ensuring a seamless and intuitive user experience. The system encompasses all the functionalities required for email composition and management, providing a comprehensive solution for effective communication. Upon successful login, the system engages in an interactive dialogue with the user, prompting them to select from available actions such as composing, reading, or deleting emails. For email composition, the system guides the user through the process, requesting the recipient's email address and the desired subject line.

Keywords:  Internet, Voice, Speech recognition, read email, delete email, compose email. Cite: Mr. Vishnu Parsewar, Mr. karan kakade, Mr. Aditya Ubale, Ms.Radhika Malpani,"VOICE-OPERATED APPLICATIONS LIKE GMAIL, FOR VISUALLY IMPAIRED PERSON", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 1, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13125.

Abstract: This survey paper explores various techniques and methodologies used in the field of document processing and natural language processing. The paper examines different research studies and their contributions in addressing specific issues related to document processing and language understanding. The techniques discussed include template matching, image processing, deep learning algorithms such as YOLOv5 and BERT, optical character recognition (OCR), convolutional neural networks (CNNs), named entity recognition (NER), and machine translation. The survey paper highlights the challenges faced in manual invoice processing and proposes an automatic system based on key fields extraction from invoices. It also addresses the complexities of handling diverse document layouts, including invoices, purchase orders, and newspaper articles, using template-based, rule-based, and OCR techniques. Handwritten text recognition in South Indian languages is explored, considering the cursive and complex structure of handwriting and the unavailability of temporal information. The paper also focuses on the need for annotated datasets and the application of AI approaches in processing unstructured invoice documents. It discusses the utilization of image segmentation, OCR, and NLP for summarizing newspaper articles and efficient processing of unstructured documents using AI techniques. Additionally, the challenges of OCR performance in low-quality images and intelligent handwritten recognition are examined. Furthermore, the paper explores the application of NLP techniques such as named entity recognition, coreference resolution, relation extraction, and knowledge base reasoning for information extraction. It discusses the challenges and applications of NER in finance and biomedicine. The survey also investigates the use of deep learning models like BERT and transformers for semantic keyphrase extraction and presents a comprehensive overview of Indian language speech synthesis techniques. Finally, the paper explores the challenges in text-to-speech training, machine translation, and Indian regional language processing. It discusses the limitations of parallel training data for voice conversion and the lack of linguistic grounding in autoencoder-based voice conversion methods. The survey paper provides a comprehensive overview of the techniques, challenges, and advancements in the field of document processing and language understanding, paving the way for future research and development.

Keywords: Natural Language Processing (NLP), Convolutional neural networks (CNNs), non-native speakers, Optical Character Recognition (OCR), key insights, inclusivity, marginalized communities. Cite: S R Suresh, Shraddha C, Sai kiran, Sharmila Chidaravalli,"A Systematic Survey of Techniques for Document Processing and Natural Language Understanding", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 1, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13126.

Abstract: The project "Population Dynamics Unveiled" delves into the intricate interplay between public health, available resources, and economic ramifications in the context of population dynamics. Employing a comprehensive data-driven approach, this exploration seeks to unravel the complex relationships that govern the growth, distribution, and sustainability of populations worldwide. The study begins by leveraging extensive datasets encompassing demographic information, healthcare metrics, resource availability, and economic indicators. By employing advanced analytical tools and machine learning algorithms, the project aims to identify patterns, correlations, and trends within the data. These insights will contribute to a nuanced understanding of the factors influencing population dynamics, including birth rates, mortality rates, disease prevalence, and resource utilization. Through the integration of public health data, the project aims to discern the impact of healthcare infrastructure, preventive measures, and access to medical services on population trends. Additionally, the analysis will investigate the role of education and awareness in shaping health outcomes and demographic patterns. Resource availability and utilization represent another focal point of the study. By examining factors such as water, energy, and food resources, the project seeks to assess how population dynamics interact with resource constraints and how sustainable practices can be integrated into population management strategies. Economic implications will be explored in terms of labour force dynamics, productivity, and the distribution of wealth. The project aims to elucidate how population changes influence economic development, income inequality, and the overall stability of societies.

Keywords: Nuanced, Hierarchical framework, DEMATEL(Decision-Making Trial and Evaluation Laboratory), Correlative, Natural resource stock depletion, BI&A(Business Intelligence and Administration), Decision-making quality, Methodological framework. Cite: Souparnika BM, Umme Haani, Mahendra MK, "Population Dynamics Unveiled: A Data-Driven Exploration of Public Health, Resources, and Economic Implications", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 1, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13127.

Abstract: The concept and development of a multipurpose robot car that avoids obstacles is presented in this project, which is built on the Arduino platform. The project's goal is to build an adaptable, autonomous robot that can go through many types of surroundings and instantly avoid obstacles. The robot uses a mix of motor control and sophisticated algorithms to assure obstacle avoidance, and it uses ultrasonic sensors to identify impediments. The main elements, the creation procedure, and the possibilities for future growth of this experimental and instructional platform are described in the abstract. In addition to providing a fantastic introduction to robotics, the multifunctional obstacle avoidance robot car lays the groundwork for upcoming robotic applications such as autonomous navigation, surveillance, and more. In addition to ultrasonic sensors for obstacle detection, the robot integrates an ESP32 camera coupled with YOLO (You Only Look Once) object recognition. The incorporation of the ESP32 camera enhances the robot's perception capabilities, allowing it to capture real-time visual data from its surroundings. The YOLO object recognition system further enables the robot to identify and categorize objects swiftly and accurately. This synergy between motor control, algorithms, ultrasonic sensors, ESP32 camera, and YOLO contributes to a robust obstacle avoidance mechanism. Keywords: Arduino microcontroller, ultrasonic sensor, DC Motor, servo motor, esp32 camera, yolo, cnn. Cite: Divyanshu Bisen, Ayushi Makode, Janvi Itiwale, Samiksha Warade, Prof.Trupti.Malewar,"Multi-functional Obstacle Avoidance Arduino Robot Car", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 1, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13128.

Abstract: Human Body Communication (HBC) is an innovative form of communication that offers enhanced safety compared to other technologies. RedTacton is a user-friendly pervasive technology that enables the­ human body to establish communication with nearby devices [7]. This paper introduces a model for a human area networking technology that facilitates communication through touch, and demonstrates how this technology can benefit both patients and doctors [6]. With HBC, doctors no longer need to be constantly present beside the patient, as the technology operates through contact. Patients can simply touch the device to access information about their condition and consult with a doctor. By utilizing the body as a transmission medium, HBC seamlessly integrates with various devices, particularly wireless biomedical monitoring devices [4]. The use of on-body sensor nodes allows for the monitoring of vital signs in individuals by utilizing the body as a transmission medium. This technology provides numerous benefits for long term clinical monitoring, offering users increased freedom. Biotelemetry is employed to achieve remote observation, measurement, and preservation of an organism's activity, condition, or function. Human Body Area Networks (HBANs) consist of sensor nodes that are either attached to or implanted into a subject's body [2]. The implementation of RedTacton technology has effectively addressed challenges associated with radio transmission limitations, data rates, and potential security threats from unauthorized signal interceptions.

Keywords: Human body communication, RedTacton, Human Body Area Network (HAN), Biotelemetry, Body Coupled Communication (BCC), IoT, Notification [1]. Cite: Partho Kumer Nonda, Md. Inzamul Haque, Md. Shadman Rafid Khan, Al- Ispahani Arif Jahan Jiko, Md. Maruf Hasan, "IoT Based Human Area Networking Implementation of Biotelemetry Utilizing RedTacton", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 1, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13129.

Abstract: One of the most serious issues in recent years has been traffic congestion. The number of cars on the road is increasing as the population and the number of cars per family grow. Despite technological advancements, there has been no adequate solution to this problem. It has emerged as the most difficult problem for urban developers in terms of planning sustainable cities. A road divider is essentially used as a barrier to separate vehicles traveling in opposite directions on the road. The road dividers which we have observed around us, are static, meaning they cannot be shifted or moved from one location to another. During peak/rush hours, we experience extremely heavy traffic on only one side of the road. So, the primary goal is to reduce it by providing an effective solution. In this paper we are discussing the methods to solve this problem .The “Automatic Movable Smart Road Dividers” aims to address traffic congestion by dynamically adjusting the road divider to accommodate traffic flow, potentially leading to improved traffic conditions and reduced travel times. Additionally, the system prioritizes providing clearance for emergency vehicles, reflecting a socially responsible aspect that could enhance emergency response times.

Keywords: Automatic Movable Smart Road Dividers, Static road divider, Prevent traffic congestion, IR sensor, RF module Cite: Amrutha A Kulkarni, Anees Fathima, G D Harshitha, G Kavya, Sarvar Begum,"Methods To Control The Traffic Using Movable Road Divider", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 1, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13130.

Abstract: This research proposes a novel image splicing detection and localization approach based on the deep convolutional neural network (CNN) learned local feature descriptor. Presented and used to automatically learn hierarchical representations from the input RGB colour or grayscale test images is a two-branch CNN that functions as an expressive local descriptor. The suggested CNN model's first layer, which is specifically made for picture splicing detection applications, is used to extract expressive and varied residual features while also suppressing the impacts of the image contents. Specifically, an optimised combination of the 30 linear high-pass filters employed in the computation of residual maps in the spatial rich model (SRM) is utilised to initialise and fine-tune the kernels of the first convolutional layer.The advancement of digital splicing technology has significantly impacted the progress of digital photo manipulation. This is particularly evident in industries such as newspaper and magazine publication, as well as companies that rely on the verification of photograph authenticity for their publications. Previously, these businesses faced substantial challenges in pre-publication due to the complexities of digital forensics in image processing. However, with the latest developments, the authentication process can now be swiftly addressed with just a few keystrokes. This review is intended to familiarize the reader with various types of digital image splicing forgeries, focusing on the current trend of passive techniques employed to confirm the authenticity of images before they are published

Keywords: Digital image forensics, image forgery detection, Image authentication, , Image Splicing, Passive techniques. Image splicing detection. Cite: Dr. Jayenesh H Desai, "A depth analysis of Image Splicing forgery detection", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 1, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13131.

Abstract: Piezoelectric energy harvesting has garnered significant interest as a sustainable method for generating electrical power from mechanical vibrations. In this study, we compare the performance of three commonly used piezoelectric materials—Lead Zirconate Titanate (PZT), Aluminum Nitride (AlN), and Polyvinylidene Fluoride (PVDF)—for vibrational energy harvesting applications. Using computational simulations, we analyze the average harvested power, peak power, power density, and power-to-acceleration ratio for varying thicknesses of each material. Our results reveal distinct performance characteristics among the materials, with PZT exhibiting superior power output and efficiency compared to AlN and PVDF. AlN demonstrates moderate performance across the parameters studied, while PVDF exhibits comparatively lower power output and efficiency. These findings provide valuable insights for engineers and researchers in selecting the most suitable piezoelectric material for energy harvesting applications based on specific performance requirements and constraints.

Keywords: Energy, Power, Harvesting, PZT, PVDF, AlN. Cite: Atul Sharma, Dr. Ajay Kumar, Dr. Rajat Arora, "Comparative Analysis of PZT, AlN, and PVDF Piezoelectric Materials for Vibrational Energy Harvesting: A Parameter-Focused Study", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 1, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13132.

Abstract: Biological Cell images can be produced using microscopy imaging, which is then utilized for cell studies in computational biology research and clinical illness detection. Quantitative analysis of biological cells is helpful for understanding biological activity at the cellular level and is performed by biological cell image analysis techniques such as cell segmentation, classification, tracking, etc.  In this article we detail microscopy image processing for biological cell segmentation and tracking from recent literature.

Keywords: Microscopy, Deep Learning, Cell Segmentation, Cell Tracking.