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In construction and heavy equipment sites, tower cranes are extensively used to hoist and move materials over a high-rise height. At present, there are some imperfections in the tower crane protection device. For the purpose of monitoring the time running state and eliminating the overloaded security issues of the tower crane, this paper proposes a way to implement a tower crane robot that consists of a load monitoring system using a load sensor, the objective of the system was to read weight carried by the tower crane in traditional analog to digital conversion, and also to attain high accuracy in measuring and calibrating the weight of the object. The components used for this research are a tower crane robot, an HX711 load cell amplifier, an Arduino-Uno microcontroller, an OLED display, a NEMA17 Stepper motor, and a DC motor. The load cell used in this research weighs 40kg. It sends an analog output signal to the HX711 module of the weight of the object, which converts it to digital, amplifies it, and sends it to the Arduino-Uno microcontroller and finally, the digital signal is sent to the OLED display. The robotic crane is made of the ‘MAST’, the main supporting tower of the crane. The ‘JIB’ is the operating arm of the crane, the ‘COUNTER JIB’ will be two-thirds the length of the jib used to carry the counter load, and the counter load will be movable along the axis of the counter jib in other to facilitate the process of counter-balancing.

Solar-powered load-controlled tower crane, an Arduino micro-controller programmed in C++, an HX711 module load cell

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[3] H.-H. Kim and G. Lee, “A quantitative analysis of fatal accidents related to cranes using the fmea method,” Journal of the Korean Institute of Building Construction, Vol.7, Issue.3, pp.115–122, 2007.
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[7] U.-K. Lee, K.-I. Kang, G.-H. Kim, and H.-H. Cho, “Improving tower crane productivity using wireless technology,” Computer-Aided Civil and Infrastructure Engineering, Vol.21, Issue.8, pp.594–604, 2006.
[8] M. Z. Othman, “A new approach for controlling overhead travelling crane using rough controller” International Journal of Intelligent technology, Vol.1, Issue.3, pp.23 - 44, 2006.
[9] Rubio, Jose de Jesus & Alcantara Ramirez, Roberto & Ponce, J. & Siller-Alcalá, Irma. (2007). Design, construction, and control of a novel tower crane. International Journal of Mathematics and Computers in Simulation. Vol.1, Issue.7, pp.119-126, 2015.
[10] B. Andonovski, L. Jianqiang, S. Jeyaraj, A. Z. Quan, X. Yonggao and A. W. Tech, "Towards a Development of Robotics Tower Crane System," 2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV), pp.345-350, 2020.
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[12] Paipetis, S. A.; Ceccarelli, Marco (2010). The Genius of Archimedes -- 23 Centuries of Influence on Mathematics, Science and Engineering: Proceedings of an International Conference held at Syracuse, Italy, pp.416 - 417, 2010.
[13] A. -A. POP and E. -D. MAER, "Control technique for unipolar and bipolar step motor using Arduino and LabVIEW," XVIII International Scientific-Technical Conference Alternating Current Electric Drives (ACED), Delhi, India, pp. 1-5, 2021
[14] Sandip N. Rikame, Pradip W. Kulkarmi. “Digital Electronic weighing machine operate on solar energy with emergency LED light,” International Journal of Emerging Technology and Advanced Engineering. Vol.4 Issue.7, pp.117 – 125, 2014.
[15] A. Suryana, R. Ananda, T. R. Maulana and M. Rizal, "Rice Controller Using Half Bridge Load Cell and NodeMCU ESP8266 In Rice Dispenser," 2019 5th International Conference on Computing Engineering and Design (ICCED), California, USA, pp. 1-6, 2019 doi: 10.1109/ICCED46541.2019.9161142.
[16] P. Schlott, F. Rauscher and O. Sawodny, "Modelling the structural dynamics of a tower crane," 2016 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), Alberta, Canada, pp.763-768, 2016 doi: 10.1109/AIM.2016.7576860.

Abubakar Aliyu Badawi, Ibrahim Nasir Said, Abdulkadir Ayo Adewale, "Design and Implementation of a Solar-Powered Load-Controlled Tower Crane Robot," International Journal of Computer Sciences and Engineering, Vol.10, Issue.12, pp.1-7, 2022.

About 58% of Indians rely on agriculture as their main source of income, and this sector contributes significantly to the nation`s Gross Domestic Product (GDP). As the agricultural process evolves, it is increasingly crucial to disseminate information about it when it becomes available. Improvements in the crop production and weather (rainfall) prediction can be increased with the right data and prediction responses obtained in relation to weather and regions. This can be accomplished with a farmer-chatbot, which is a conversational software that uses pre-programmed responses or artificial intelligence to answer the queries without the intervention of a human. The proposed Farmer-chatbot system, allows farmers to ask questions and receive precise replies in common language. The query is processed using the Ensemble Learning Algorithm and the Natural Language Processing to anticipate solutions to the users.

Ensemble Learning, Bagging Method, Natural language processing, Data Cleaning, Prediction.

[1] Yashaswini. D. K, Hemalatha. R, Niveditha. G, “Smart Chatbot for Agriculture”, International Journal of Engineering Science and Computing, Vol.9, Issue.5, 2019.
[2] Prashant Y. Niranjan, Vijay S. Rajpurohit, Rasika Malgi, “Development of Agriculture Chatbot using Machine Learning Techniques”, International Journal of Innovative Technology and Exploring Engineering, Vol.9, Issue.2S, pp.24-28, 2019.
[3] Gustavo Marques Mostaco, Icaro Ramires Costa Desouza, Leonaordo Barreto Campos, Carlos Edurardo, “AgronomoBot: a smart answering Chatbot applied to agricultural sensor networks”, Proceedings of the 14th International Conference on Precision Agriculture 2018.
[4] R. Sandrilla, M. Savitha Devi, “A ROBUST TECHNIQUE OF FAKE NEWS IDENTIFICATION USING ENSEMBLE FEATURE SELECTION”,Indian Journal of Computer Science and Engineering (IJCSE), Vol.12,No.6, 2021.
[5] P. Sreelakshmi, Gaggara Harika, Kavya Karat, R. Madhumitha, K. Vijith, “Automated Agrobot”, Indian Journal of Science and Technology, Vol.9, Issue.30, 2016.
[6] Munira Ansari, Mohammed Saad Parbulkar, Saali Shaikh, Talha Khan, Anupam Singh, “Intelligent Chatbot”, International Journal of Engineering Research & Technology (IJERT), Special Issue – 79-82, 2021.
[7] Brahmananda Reddy D, 2 Gurubasava “Agriculture Chatbot Application Using Python” Vol.5, Issue.1, 2020.
[8] D. Sawant, A. Jaiswal, J. Singh and P. Shah, "AgriBot - An intelligent interactive interface to assist farmers in agricultural activities," 2019 IEEE Bombay Section Signature Conference (IBSSC), pp.1-6, 2019.
[9] Ekanayake, J. and Saputhanthri, L. “E-AGRO: Intelligent Chat-Bot. IoT and Artificial Intelligence to Enhance Farming Industry", AGRIS on-line Papers in Economics and Informatics, Vol.12, No.1, pp.15-21, 2020.
[10] J. Vijayalakshmi, K. PandiMeena, “Agriculture TalkBot Using AI”, Vol.8, Issue-2S5, 2019.
[11] L. Kannagi, Ramya C., Shreya R., Sowmiya R. “Virtual Conversational Assistant–The FARMBOT”-International Journal of Engineering Technology Science and Research- Vol.5, 2018.
[12] P. Kaviya , M. Bhavyashree , M. Deepak Krishnan , M. Sugacini, “Artificial Intelligence Based Farmer Assistant Chatbot”, International Journal of Research in Engineering, Science and Management, Vol.4, Issue.4, pp.26-29, 2021.
[13] H Manoj T Gadiyar, Dr. Thyagaraju G S, Soubhagya K- “Online based Agriculture Monitoring System Using AI” - ICRADL – 2021.
[14] Tran Ngoc Viet,Hoang Le Minh,Le Cong Hieu, Tong Hung Anh "THE NAÏVE BAYES ALGORITHM FOR LEARNING DATA ANALYTICS"- International Journal of Computer Science Engineering Vol.12, No.4, 2021.
[15] Tina Elizabeth Mathew, K S Anil Kumar "A LOGISTIC REGRESSION BASED HYBRID MODEL FOR BREAST CANCER CLASSIFICATION,"International Journal of Computer Science Engineering Vol.11 No.6, 2020.
[16] A. Bhattacharjee , J. Kharade, "Cluster-Then-Predict and Predictive Algorithms (Logistic Regression) "International Journal of Computer Science Engineering Vol.6, Issue.2, 2018.
[17] Dr.S. Geetha, Dr. S. Balaji, Santhiya. A, Subashri. C, and Subicsha. S - “Farm’s SmartBOT”, Vol.12, No.10, 2021.
[18] R. Gunawan, I. Taufik, E. Mulyana, O. T. Kurahman, M.A. Ramdhani and Mahmud, "Chatbot Application on Internet Of Things (IoT) to Support Smart Urban Agriculture," IEEE 5th International Conference on Wireless and Telematics (ICWT), pp.1-6, 2019.

Uddhav Patil, Nikhil Kinikar, Madanu Amisha, Aishwarya Chouthe, Rajeshwari Goudar, "Smart Interactive Farmer-Bot Using Bagging & Natural Language Processing," International Journal of Computer Sciences and Engineering, Vol.10, Issue.12, pp.8-13, 2022.

Story construction from deep learning is a naive methodology suitable for the digital forensics, smart video surveillance, and intelligent robotics applications. So far deep learning has been utilized for image recognition and classifications. The sequence of those images and classification of them on a temporal basis leads to the development of knowledge on the entire changes in the scenes and finally end up with a story in which the identified scenes are connected with unambiguous changes. This paper retrieves the pros and cons of the research work on recurrent topic transition GAN for Visual Paragraph Generation and relation pair visual paragraph generation. The Existing algorithms proposed for constructing the stories are RTT GAN, RP GAN and BF GAN. These are implemented with respect to different applications human computer interaction, intelligence robotics, digital forensics etc. The survey of the subjected algorithms gives the transparency of their working principles. The current paper presented the visual representation, description, generation of paragraphs with various methodologies along with the comparison.

Semantic Region, Attention Module, Discriminator, Scene recognition, Visual features, Generative Adversarial Network

[1] X. Liang, Z. Hu, H. Zhang, C. Gan and E. P. Xing, "Recurrent Topic-Transition GAN for Visual Paragraph Generation," IEEE International Conference on Computer Vision (ICCV), pp.3382-3391, 2017.
[2] W. Che, X. Fan, R. Xiong and D. Zhao, "Visual Relationship Embedding Network for Image Paragraph Generation," in IEEE Transactions on Multimedia, Vol.22, no.9, pp.2307-2320, 2020. doi: 10.1109/TMM.2019.2954750.
[3] Z. -J. Zha, D. Liu, H. Zhang, Y. Zhang and F. Wu, "Context-Aware Visual Policy Network for Fine-Grained Image Captioning," in IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.44, no.2, pp.710-722, 2022. doi: 10.1109/TPAMI.2019.2909864.
[4] D. Liu, J. Fu, Q. Qu and J. Lv, "BFGAN: Backward and Forward Generative Adversarial Networks for Lexically Constrained Sentence Generation," in IEEE/ACM Transactions on Audio, Speech, and Language Processing, Vol 27, no.12, pp.2350-2361, 2019. doi: 10.1109/TASLP.2019.2943018.
[5] K.Kiruba, D. Shiloah Elizabeth, C Sunil Retmin Raj, “Deep Learning for Human Action Recognition – Survey,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.323-328, 2018.
[6] B. Prasad, U.K. Devi , “Shape And Texture Based Scene Classification,” International Journal of Computer Sciences and Engineering, Vol.2, Issue.5, pp.79-87, 2014.
[7] Alejandro López-Cifuentes , Marcos Escudero-Viñolo, JesúsBescós, Álvaro García-Martín “Semantic-aware scene recognition” 0031-3203/© 2020 Elsevier.
[8] ” Songhao Zhu *, Yuncai Liu”Automatic scene detection for advanced story retrieval”,Institute of Image Process and Pattern Recognition, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China.
[9] Ramisa, F. Yan, F. Moreno-Noguer and K. Mikolajczyk, "BreakingNews: Article Annotation by Image and Text Processing," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 5, pp. 1072-1085, 2018. doi: 10.1109/TPAMI.2017.2721945
[10] C. P. Chaudhari and S. Devane, "Capturing Semantic Knowledge In Object Localization In Captioning Images," 2021 International Conference on Communication information and Computing Technology (ICCICT), pp.1-4, 2021. doi: 10.1109/ICCICT50803.2021.9510175.
[11] Stanislav Protasov, Adil Mehmood Khan, Konstantin Sozykin & Muhammad AhmadSignal,”Using deep features for video scene detection and annotation”, Image and Video Processing, Vol.12, pp.991–999, 2018.
[12] Y. Choi, S. Kim and J. Lee, "Recurrent Neural Network for Storytelling," 2016 Joint 8th International Conference on Soft Computing and Intelligent Systems (SCIS) and 17th International Symposium on Advanced Intelligent Systems (ISIS), pp. 841-845, 2016. doi: 10.1109/SCIS-ISIS.2016.0182.
[13] P. Haritha, S. Vimala and S. Malathi, "A Systematic Literature Review on Story-Telling for Kids using Image Captioning - Deep Learning," 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA), pp.1588-1593, 2022. doi: 10.1109/ICECA49313.2020.9297457.
[14] H. Zeng, X. Song, G. Chen and S. Jiang, "Learning Scene Attribute for Scene Recognition," in IEEE Transactions on Multimedia, Vol.22, no.6, pp.1519-1530, 2020. doi: 10.1109/TMM.2019.2944241.
[15] H. Seong, J. Hyun and E. Kim, "FOSNet: An End-to-End Trainable Deep Neural Network for Scene Recognition," in IEEE Access, Vol.8, pp.82066-82077, 2020. doi: 10.1109/ACCESS.2020.2989863.
[16] S. Wang, S. Yao, K. Niu, C. Dong, C. Qin and H. Zhuang, "Intelligent Scene Recognition Based on Deep Learning," in IEEE Access, Vol.9, pp.24984-24993, 2021. doi: 10.1109/ACCESS.2021.3057075.
[17] J. Guo, X. Nie and Y. Yin, "Mutual Complementarity: Multi-Modal Enhancement Semantic Learning for Micro-Video Scene Recognition," in IEEE Access, Vol.8, pp.29518-29524, 2020. doi: 10.1109/ACCESS.2020.2973240.
[18] S. Raghunandan, P. Shivakumara, S. Roy, G. H. Kumar, U. Pal and T. Lu, "Multi-Script-Oriented Text Detection and Recognition in Video/Scene/Born Digital Images," in IEEE Transactions on Circuits and Systems for Video Technology, Vol.29, no.4, pp.1145-1162, 2019. doi: 10.1109/TCSVT.2018.2817642.
[19] Chen Wanga,b,? , Guohua Penga , Bernard De Baets “Deep feature fusion through adaptive discriminative metric learning for scene recognition” 1566-2535/© 2020 Elsevier
[20] Lin Xiea,1 , Feifei Leea,1,? , Li Liub , Koji Kotani c , QiuChend, “Scene recognition: A comprehensive survey” , Elsevier Ltd. 0031-3203, 2020.
[21] H. Seong, J. Hyun and E. Kim, "FOSNet: An End-to-End Trainable Deep Neural Network for SceneRecognition," in IEEE Access, Vol.8, pp.82066-82077, 2020. doi: 10.1109/ACCESS.2020.2989863.
[22] A. Jalal, A. Ahmed, A. A. Rafique and K. Kim, "Scene Semantic Recognition Based on Modified Fuzzy C-Mean and Maximum Entropy Using Object-to-Object Relations," in IEEE Access, Vol.9, pp.27758-27772, 2021. doi: 10.1109/ACCESS.2021.3058986.
[23] G. Chen, X. Song, H. Zeng and S. Jiang, "Scene Recognition With Prototype-Agnostic Scene Layout," in IEEE Transactions on Image Processing, Vol.29, pp.5877-5888, 2020. doi: 10.1109/TIP.2020.2986599.
[24] Z. Xiong, Y. Yuan and Q. Wang, "RGB-D Scene Recognition via Spatial-Related Multi-Modal Feature Learning," in IEEE Access, Vol.7, pp.106739-106747, 2019. doi: 10.1109/ACCESS.2019.2932080.
[25] S. Wang, S. Yao, K. Niu, C. Dong, C. Qin and H. Zhuang, "Intelligent Scene Recognition Based on Deep Learning," in IEEE Access, Vol.9, pp.24984-24993, 2021. doi: 10.1109/ACCESS.2021.30570

Darapu Uma, M.Kamala Kumari, "A Comprehensive Survey and Comparison on Story Construction Techniques Using Deep Learning for Scene Recognition," International Journal of Computer Sciences and Engineering, Vol.10, Issue.12, pp.14-22, 2022.

Credit card companies must have the ability to identify fraudulent credit card transactions in order to stop customers from being charged for goods they did not purchase. These problems may be resolved with data science, and when combined with machine learning, it is extremely important. This study seeks to show how machine learning may be used to model a data set using credit card fraud detection. The Credit Card Fraud Detection Problem includes modelling prior credit card transactions using data from those that turned out to be fraudulent. Then, this model is used to analyse a new transaction to determine whether or not it is fraudulent. The objective is to detect 100% of the fraudulent transactions while minimising erroneous fraud categories. Due to the E-Commerce sector`s recent explosive expansion, fraudulent credit card transactions have cost incredibly significant sums of money. An effective method to stop these fraudulent transactions is to use a strong model based on cutting-edge machine learning algorithms that can handle massive volumes of data while still producing precise findings. In this study, the effectiveness of decision trees, random forests, and linear regression for identifying credit card fraud is compared.

Outliers, Decision Tree, Confusion Matrix, Isolation Forest, Logistic Regression, Naive Bayes Classifier, Credit Card Fraud

[1]J. O. Awoyemi, A. O. Adetunmbi, and S. A. Oluwadare, “Credit card fraud detection using machine learning techniques: a comparative analysis,” in Proceedings of the 2017 International Conference on Computing Networking and Informatics (ICCNI), IEEE, Lagos, Nigeria, pp.1–9, 2017.
[2]A. Dal Pozzolo, G. Boracchi, O. Caelen, C. Alippi, and G. Bontempi, “Credit card fraud detection: realistic modeling and a novel learning strategy,” IEEE transactions on neural networks and learning systems, Vol.29, no.8, pp.3784–3797, 2017.
[3]S. Xuan, G. Liu, Z. Li, L. Zheng, S. Wang, and C. Jiang, “Random forest for credit card fraud detection,” in Proceedings of the 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC), IEEE, Zhuhai, China, pp.1–6, 2018.
[4]J. Jurgovsky, M. Granitzer, K. Ziegler et al., “Sequence classification for credit-card fraud detection,” Expert Systems with Applications, Vol.100, pp.234–245, 2018.
[5]D. Varmedja, M. Karanovic, S. Sladojevic, M. Arsenovic, and A. Anderla, “Credit card fraud detection-machine learning methods,” in Proceeding of the 2019 18th International Symposium INFOTEH-JAHORINA (INFOTEH), IEEE, East Sarajevo, Bosnia and Herzegovina, March, pp.1–5, 2019,
[6]F. Carcillo, Y.-A. Le Borgne, O. Caelen, Y. Kessaci, F. Oblé, and G. Bontempi, “Combining unsupervised and supervised learning in credit card fraud detection,” Information Sciences, Vol.557, pp.317–331, 2021.
[7]K. Randhawa, C. K. Loo, M. Seera, C. P. Lim, and A. K. Nandi, “Credit card fraud detection using AdaBoost and majority voting,” IEEE access, Vol.6, 2018.
[8]A. G. C. de Sá, A. C. M. Pereira, and G. L. Pappa, “A customized classification algorithm for credit card fraud detection,” Engineering Applications of Artificial Intelligence, Vol.72, pp.21–29, 2018.
[9]R. Sailusha, V. Gnaneswar, R. Ramesh, and G. R. Rao, “Credit card fraud detection using machine learning,” in Proceeding of the 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS), IEEE, Madurai, India, pp.1264–1270, 2020.
[10]S. Bagga, A. Goyal, N. Gupta, and A. Goyal, “Credit card fraud detection using pipeling and ensemble learning,” Procedia Computer Science, Vol.173, pp.104–112, 2020.
[11]Wen-Fang Yu,Na Wang “Research on Credit Card Fraud Detection Model Based on Distance Sum” JCAI `09: Proceedings of the 2009 International Joint Conference on Artificial Intelligence April 2009.
[12] Survey Paper on Credit Card Fraud Detection by Suman , Research Scholar, GJUS&T Hisar HCE, Sonepat published by International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Vol.3 Issue.3, 2014.

Sayee Chauhan, "Detecting Fraudulent Transactions with the Ensemble Learning," International Journal of Computer Sciences and Engineering, Vol.10, Issue.12, pp.23-27, 2022.

Efficient spatial database management system is now considered to be a primary component of any GIS architecture or Spatial Data Infrastructure (SDI) which acts as a central repository that disseminates data across web and desktop clients. In such scenarios, a common requirement for production databases is the ability to track history and authenticity of the data generated. Using the database and the trigger system as regular practice, it’s possible to add history tracking, however, there exists a challenge of using desktop GIS tools for editing and validation of the normalized data residing in spatial databases, intacting data integrity with privileged and authenticated users. Considering the facts and importance of auditing, jurisdiction based user access management and limitation of editing spatial data in a web browser, the present study is an attempt to develop an Open Source solution based on authorization and authentication against LDAP, QGIS, PostgreSQL/PostGIS, and Multicorn FDW software.

LDAP, Web- Editing, QGIS, Open Source

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Santosh Gaikwad, Maulik Bhagat, Arjan Odedra, Rahul Kanani, Aditya Saraswat, "Database-driven Spatial Data Auditing using QGIS against LDAP Authentication and Authorization," International Journal of Computer Sciences and Engineering, Vol.10, Issue.12, pp.28-33, 2022.

Major security issues in distributed operating systems are categorized into information leakage via hacking, server redundancy, vulnerability and risk assessment inefficiency. This study presents a systematic survey of security and reliability issues in a distributed operating system. A distributed operating system is a unique system program which adopts numerous central processors for real-time execution of allocated tasks. A distributed operating system connects many computer systems using a single communication channel. The frequent application of distributed operating systems to data sharing and management among cooperate entities has necessitated the need for distributed resources and computing strategies. The security and reliability features of a distributed operating system are critical and highly indispensable. Data security models for reliability of a distributed operating system were analysed, compared and discussed in the study. The essence of the discussed models was to enhance the performance of distributed operating systems. The study looked at models such as data-at-rest security model, access control model, steganography model, body area model, data classification model and cryptography model. The models were adequately compared and analysed especially in terms of performance mode, pros and cons. The study could be beneficial to developers of distributed operating systems and researchers with keen interest in the study area.

Data Models, Distributed Operating System, Reliability, Security

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Attah Stella, Taylor O.E., "Distributed Operating System Security and Reliability," International Journal of Computer Sciences and Engineering, Vol.10, Issue.12, pp.34-40, 2022.

— This study introduces a unique method for strengthening healthcare data security by combining homomorphic encryption (HE) with elliptic curve cryptography (ECC) in a way that is both robust and effective. Patients` right to privacy and data security must be protected as the cloud computing industry moves towards a model based on data-driven insights. For safe data processing and analytics, we provide a hybrid system that takes advantage of ECC`s efficiency in key exchange while also including HE`s privacy-preserving characteristics. We illustrate the efficacy of our suggested strategy through a thorough comparative analysis, actual implementation, and case study in a Cloud computing setting. The hybrid architecture prevents sensitive information from falling into the wrong hands and facilitates secure and efficient data flow among legitimate parties. Our results highlight the promise of this robust union to revolutionize cloud securityand execution time,encryption,and decryption time compared to other crypto algorithms, opening doors to betterand more efficient programs for future endeavors.

Homomorphic encryption, Elliptical curve cryptography, Hybrid Framework, Cloud Computing, Comparative Analysis.

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Madhira Srinivas, Porika Sammulal, "Enhacing Cloud Security: Combining Homomorphic and Elliptic Curve Cryptography for Resilient Fusion," International Journal of Computer Sciences and Engineering, Vol.10, Issue.12, pp.41-46, 2022.

The advent of technology has shifted the representation of information from text to images. However, the image capturing process introduces noise, resulting in distortion and the generation of potentially misleading information. To address this challenge, it is essential to integrate noise handling mechanisms into existing image processing methods. Among these mechanisms, filtering stands out as a crucial strategy for mitigating noise effects. This research delves into the analysis of various noise handling mechanisms in the current context, aiming to identify optimized strategies for enhancing parameters in future implementations.

Image capturing, Noise handling mechanism, Filtering, Parameter enhancement

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Vikas Mongia, "Advanced Noise Mitigation Strategies in Image Processing: A Comprehensive Analysis and Optimization Study," International Journal of Computer Sciences and Engineering, Vol.10, Issue.12, pp.47-50, 2022.