2023 3rd International Conference on Smart Data Intelligence (ICSMDI)最新文献

{"title":"Design and Implementation of IoT based Dual Axis Solar Tracking System","authors":"V. S., Kathirvel C, D. P, Mohan Kumar R","doi":"10.1109/ICSMDI57622.2023.00102","DOIUrl":"https://doi.org/10.1109/ICSMDI57622.2023.00102","url":null,"abstract":"Web of Things (WoT) innovations, along economies of scale and advances in system, programming, and company improvements, have speeded up the blast of related items throughout the Internet. A related article may be managed on-line through an IoT level and may transfer, get, and procedure exclusive and modified parameters. This study has reported about the IoT improvements to advocate a primary and cost efficient IoT application to display and manipulate first rate single-pivot solar powered tracker to perform execution assessment. The proposed model includes pre-programmed alerts that can be sent to a remote customer via smartphone or e-mail. The proposed model is a low-cost and simple-to-use system and programming, as well as a web-based model. The design components of IoT include solar powered trackers. In addition, a version of IoT primarily based on fully solar powered trackers has been developed and tested. The results show how daylight-based tracker data can be sent successfully and correctly. This can be easily checked on the web, as well as how the solar powered trackers from the NodeMCU device. With the help of this proposed concept, the orientation of the solar power will be continuously monitored and tracked to improve the energy conversion efficiency.","PeriodicalId":373017,"journal":{"name":"2023 3rd International Conference on Smart Data Intelligence (ICSMDI)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116288799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Malware Classification using Deep Learning Methods","authors":"Sundharakumar K B, Bhalaji N, Prithvikiran","doi":"10.1109/ICSMDI57622.2023.00058","DOIUrl":"https://doi.org/10.1109/ICSMDI57622.2023.00058","url":null,"abstract":"With an increase in the fnumber of machines to the internet, the attack surface for cybercriminals has increased multifold, leading to increased risk and damage to the users. One such common attack is due to malicious software (malware) which compromises computers/smart devices, steals confidential information, penetrates networks, and cripples critical infrastructures, etc. The entire cost of malware attacks are projected to be the $3 trillion in 2015 and it is anticipated to rise above $6 trillion by the end of 2021. In order to address and confine the cyber attacks, several approaches such as Intrusion Detection Systems (IDSs) and Intrusion Protection Systems(IPSs), firewalls and antivirus software. These existing malware detection tools, which employ static and dynamic analysis of malware signatures and behaviour patterns, have shown to be inefficient at quickly discovering polymorphic security assaults that haven't been observed before. Also with the maching learning algorithms, feature engineering phase becomes a tedious process with various features present in these datasets. This study incorporates deep learning algorithms to avoid the feature engineering phase and hence, enhance the performance and accuracy of the malware classification.","PeriodicalId":373017,"journal":{"name":"2023 3rd International Conference on Smart Data Intelligence (ICSMDI)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126675943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Utilising Deep Learning as a Law Enforcement Ally","authors":"D. Ramani, M. Nirmala, Sourabh V.N, Shreyas Chaudhary, Deepesh Kumar","doi":"10.1109/ICSMDI57622.2023.00089","DOIUrl":"https://doi.org/10.1109/ICSMDI57622.2023.00089","url":null,"abstract":"Finding fugitive offenders after they have committed a crime or an illegal act takes time and effort. It is challenging for law enforcement authorities to complete this work on their own given the rising population density and the size of any nation's landmass. Thus, public participation becomes crucial, revolutionary, and beneficial. This cycle is both times and works seriously. In this paper, we tried to suggest a different framework for criminal Distinguishing & Recognition using Deep learning and Heroku Cloud, i.e. Cloud Computing, which, assuming it is used by our Crime Control Organizations, would help them catch criminals from CCTV images or images uploaded by the public if seen anywhere. This system is in place to assist in capturing criminals and anyone who can upload information indicating that they saw the relevant individual at a specific location and time. In India, where conditions are always changing due to things like light, weather, and specific directions, existing solutions use conventional face acknowledgement computations, which might be problematic because there is no open public contribution. Our research paper employs LBPH, Deep Learning, and Heroku Cloud technologies to construct the system.","PeriodicalId":373017,"journal":{"name":"2023 3rd International Conference on Smart Data Intelligence (ICSMDI)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126806712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Guide Towards Implementing the Effective Algorithm for Optimum Topology in Complex Terrains","authors":"Anshika Salaria, Amandeep Singh","doi":"10.1109/ICSMDI57622.2023.00011","DOIUrl":"https://doi.org/10.1109/ICSMDI57622.2023.00011","url":null,"abstract":"Wireless Sensor Networks (WSN) have recently been in high demand for various applications. Specifically, in disaster and management scenarios where the terrains are complex and not easily accessible, such networks prove to be the most successful. However, deploying a large number of nodes in such complex terrains at optimum positions is a tedious task. In fact, the network's overall performance is affected by the inefficient deployment of nodes. There are a number of criteria to be kept in mind while deciding the optimum positions of nodes. Therefore, a high demand arises for algorithms that would calculate the most efficient positions based on some parameters. This paper highlights the need for optimum topology network algorithms for wireless sensor networks, state-of-the-art topology optimization algorithms, evaluation criteria to be considered and the tools which can be opted for simulations. This paper would help the researchers to design an optimum topology algorithm to make an efficient wireless network system for complex terrains like forests, wildlife, landslides, etc.","PeriodicalId":373017,"journal":{"name":"2023 3rd International Conference on Smart Data Intelligence (ICSMDI)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127983897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Review: Early Detection, Segmentation and Classification Techniques for Melanoma and Skin Cancer in Images","authors":"Vankayalapati Radhika, B. S. Chandana","doi":"10.1109/ICSMDI57622.2023.00057","DOIUrl":"https://doi.org/10.1109/ICSMDI57622.2023.00057","url":null,"abstract":"The rate of skin cancer has been increasing rapidly all over the world, making it one of the most deadly cancer categories. If it is not detected in its early stages, it can spread and cause metastases which would result in significant fatality rates. Skin cancer is curable if it is detected early, As a result, an important goal of current research is to prompt and precise detection of such malignancies. In the computer-aided diagnosis of melanoma identification and malignant categorization, many technologies have been used. The effectiveness, difficulty, and dataset quality of different methods for the detection of skin cancer techniques are examined in this study. The effectiveness of skin cancer reorganization, segmentation, and categorization methods described in the literature within the last three years is investigated in this study (2020–2022). A com parative table of the works mentioned is also induded. However, skin cancer has gained recent attention as a practical and outstanding option among the suggested solutions.","PeriodicalId":373017,"journal":{"name":"2023 3rd International Conference on Smart Data Intelligence (ICSMDI)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116659796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Wireless Sensor Network Intrusion Detection Model for Real-time Hazardous Chemical Monitoring","authors":"Bing Xu, Y. Chen","doi":"10.1109/ICSMDI57622.2023.00041","DOIUrl":"https://doi.org/10.1109/ICSMDI57622.2023.00041","url":null,"abstract":"Intelligent video surveillance technology is composed of the extremely complex algorithms and technologies. Hence, this article proposes a wireless sensor network intrusion detection model to perform real-time hazardous chemical monitoring. The video surveillance is mainly obtained through the on-site investigation process. After a preliminary understanding of the on-site situation, the “centripetal method” or “centrifugal method” is used to visit the periphery, and finally the surveillance video will be detected. To improve the security, the network intrusion detection model is employed. The performance is validated by testing the model under different conditions.","PeriodicalId":373017,"journal":{"name":"2023 3rd International Conference on Smart Data Intelligence (ICSMDI)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122799335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ARTSY: Digital Assistance and Routing Detection Using ML","authors":"Jeevitha R, Selvan C, Nishitha T, V. P., Karthik Surya J","doi":"10.1109/ICSMDI57622.2023.00070","DOIUrl":"https://doi.org/10.1109/ICSMDI57622.2023.00070","url":null,"abstract":"The existing voice assistants, such as Apple Siri, Amazon Alexa, and Google Assistant, rely on complex Artificial Intelligence technology. People now connect with computers in different ways via virtual assistants, conversational interfaces, and chatbots. A personal virtual assistant may even perform certain basic duties like launching apps, reading out notifications and messages, taking personalized notes for you, etc. with just a voice command. Users can ask inquiries to them in the same way they would to a real person. This paper targets on describing the importance of digital assistants and how they can be further enhanced compared to the versions we have in the market today.","PeriodicalId":373017,"journal":{"name":"2023 3rd International Conference on Smart Data Intelligence (ICSMDI)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115800139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi Head Graph Attention for Drug Response Predicton","authors":"P. Selvi Rajendran, M. Sivannarayna","doi":"10.1109/ICSMDI57622.2023.00078","DOIUrl":"https://doi.org/10.1109/ICSMDI57622.2023.00078","url":null,"abstract":"Precision medicine is based on curing diseases based on a patient's genetic profile, lifestyle, and environmental factors. This method improves clinical trial success rates and speed up drug regulatory approval. Predicting tumour vulnerability to specific anti-cancer therapy is critical for the successful implementation of precision medicine. Drug combinations have been shown to be very effective in cancer treatment to lower the drug resistance and improve the therapeutic effectiveness. The experiments carried out in all these therapeutic combinations have become expensive and time-consuming as a result of the increasing number of anti-cancer drugs. Large-scale drug response testing on cancer cell lines might help to understand the way drugs react with cancer cells. This study proposes a multi head graph attention network to perform drug response prediction.","PeriodicalId":373017,"journal":{"name":"2023 3rd International Conference on Smart Data Intelligence (ICSMDI)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116383341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"File Encryption using Noise Images as Key","authors":"B. A, P. R, SESHADRI. K S, S. B","doi":"10.1109/ICSMDI57622.2023.00046","DOIUrl":"https://doi.org/10.1109/ICSMDI57622.2023.00046","url":null,"abstract":"Everything in today's highly connected digital world is increasingly dependent on instantaneous global data transfers. Internet efficiency facilitates our daily lives. Sharing information online presents significant security dangers and difficulties in the modern day. The use of cryptography is the means through which sensitive information can be protected from various threats. Improved cryptosystems technology is a no-brainer for securing communication networks. An improved cryptosystem is the focus of our research, and to that end we have presented a novel method of data encryption and decryption that guarantees increased efficiency over current best practises. This research work has proposed a noise image encryption and decryption scheme, wherein the noise signal is randomly selected to set the initial values for a chaotic system which also enhances the security of the system. for understanding the effectiveness of the proposed system. Experimental results confirm that the proposed chaos-based cryptosystem is efficient and suitable for information (image) transmission in a highly secured way. This study has developed a file encryption system, where encrypted file is saved to decrypt and download later. The main difference between the existing and proposed system is the implementation of file encryption system using noise images that store key values inside it.","PeriodicalId":373017,"journal":{"name":"2023 3rd International Conference on Smart Data Intelligence (ICSMDI)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114615503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sign Language Recognition using Python and OpenCV","authors":"K. S, Mowlieshwaran S, K. R., Kishore Ds, K. M","doi":"10.1109/ICSMDI57622.2023.00023","DOIUrl":"https://doi.org/10.1109/ICSMDI57622.2023.00023","url":null,"abstract":"Conversing with an individual along with listening to impairment is consistently a primary problem. Authorized foreign language has indelibly come to be the ultimate cure-all as well as is an extremely effective resource for people with listening and also pep talk impairment to communicate their emotions and points of view to the world. It generates a combination method between all of them as well as others that are softer and less sophisticated. Having said that, the creation of an authorized foreign language alone is actually inadequate. Certainly, there certainly are numerous strings attached to this benefit. The legal actions are frequently mixed up and also misunderstood by someone who has never heard of it or even recognizes it in a different language. Having said that, this interaction void, which has actually existed for many years, may currently be tightened along with the introduction of numerous methods to automate the discovery of authorized motions. Within this particular study, our team presented an Authorize Foreign Language acknowledgment utilizing United States Authorize Foreign Language. Within this particular analysis, the customer needs to have the capacity to squeeze pictures of the possession in motion utilizing an internet electronic camera, and the device will forecast as well as show the title of the recorded picture. To find the possession motion and collect the history to dark, our team employs the HSV color protocol. The pictures go through a collection of handling actions that include numerous personal computer sight methods, including the conversion to grayscale, dilation, and mask function. Additionally, the area of enthusiasm, which, in our instance, is actually the possession motion, is actually segmented. The functions drawn out are actually the binary pixels of the picture. This study utilizes Convolutional Neural Networks (CNN) to categorize the image. The proposed model has the capacity to acknowledge 10 United States Authorized Motion Alphabets along with higher precision. The proposed version has actually attained an exceptional precision of over 90%.","PeriodicalId":373017,"journal":{"name":"2023 3rd International Conference on Smart Data Intelligence (ICSMDI)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114913585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}