International Journal of Innovative Technology and Research (IJITR)
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Verification via Shared Negative Password Encryption
Get PDFAs the Internet has grown, it has led to the creation of a huge number of online services. Most people use password authentication because it is cheap and easy to set up. As a result, academics and businesses are constantly showing a great deal of interest in password security. Cracking a password is one of the most common types of cyber attacks used in today's world. Passwords are becoming more complex. For instance, many users choose passwords based on the language they use most often and then repeat those passwords across many sites. The attacker uses a number of techniques to get the credentials needed to steal sensitive data. These techniques include guessing the password, shoulder surfing, and other tools that are designed to break passwords. It is recommended that we use passwords that are highly encrypted and hashed to get around this problem. Since the hash function is combined with the encryption process, it is very difficult to distinguish passwords from ENPs. The investigation and comparison of algorithms show that the ENP cloud is resistant to attacks using lookup tables and provides a higher level of protection for a password when it is subjected to dictionary attacks. In this case, the process of developing a secure password involves two steps: first, the password is hashed, and then it is encrypted
Improving Picture Captioning Using A Multi-Task Learning Method
Get PDFWe present MLAIC, a multi-task learning approach to image captioning, motivated by the idea that individuals are naturally gifted in more than one area. The three main parts of MLAIC are as follows: (1) an image classification model that learns to use a convolutional neural network (CNN) to encode images with a lot of category awareness; (2) an image syntax generation model that learns to use a long short-term memory (LSTM) decoder to encode images with better syntax awareness; and (3) an image captioning model that uses its CNN encoder for object classification and its LSTM decoder for syntax generation. The extra information on syntax and object classification is very useful for the picture captioning model. Our model outperforms other formidable rivals, according to experimental findings on the MS-COCO dataset
The E-Health Cloud Platform Now Supports A Keyword Search Related To Timer Use And Lab-Enabled Proxy Recoding
Get PDFThe delivery of healthcare may be vastly enhanced by the introduction of novel software, such as an electronic health record system. Users' fundamental concerns about the privacy and security of their personal information may be slowing the systems' widespread adoption. The searchable encryption (SE) method is a promising option for the electronic health record system due to its ability to provide strong security without sacrificing usability. Our research introduces a new cryptographic primitive, which we've termed "Re-dtPECK." It's a time-dependent SE approach that combines conjunctive keyword search with a designated tester and a proxy reencryption function that takes time into consideration. Patients may use this function to provide access to their data to carefully chosen researchers for a short period of time. Any allotted period for a delegatee to view and decode their delegator's encrypted papers may be extended if required. It's possible that the delegate's access and search capabilities will expire after a certain period of time has passed. It's also capable of conjunctive keyword searches and resisting assaults based on guessing. Only the authorized tester is allowed to look for the existence of certain keywords in the proposed method. We provide a system model and a security model for the proposed Re-dtPECK approach to prove that it is a safe and effective replacement for the existing standard. Simulations and comparisons with other methods show that it requires very little bandwidth and storage space for data
A Review study on Detection of Breast Cancer using Machine Learning Techniques
Get PDFBreast cancer (BC), which accounts for one in four cancer occurrences worldwide, is the most often diagnosed kind of cancer in women, according to the World Health Organization (WHO). Breast cancer is a malignancy that arises from the proliferation and uncontrolled growth of cells inside the breast tissue.Metastasis is a probable occurrence in which malignant cells possess the capability to extend beyond the boundaries of the breast tissue and infiltrate neighboring lymph nodes, later on disseminating to organs such as the liver, lungs, and bones. The exact cause of breast cancer remains uncertain, as it is a complex condition driven by a combination of genetic, environmental, and lifestyle factors that all lead to an increased susceptibility. According to the 2021 data provided by the World Health Organization (WHO), Breast Cancer (BC) has emerged as the most prevalent form of cancer globally. Projections indicate that BC may account for almost 30% of all cancer cases diagnosed in women[1]. The International Agency for Research on Cancer (IARC) reported an approximate figure of 2.3 million newly diagnosed cases of breast cancer in the year 2020. Breast cancer constitutes 14% of the total cancer cases observed among women in India. Among every 1000 women screened, in 8 disease-specific death is averted, but 11 still die from breast cancer[2]. This paper presents a review study based on literature, for doing research further
Literature Based Study On Sustainable Solutions Using AI And ML In Some Societal Applications
Get PDFSustainability is the long-term viability of a community, set of social institutions, or societal practice. In general, sustainability is understood as a form of intergenerational ethics in which the environmental and economic actions taken by present persons do not diminish the opportunities of future persons to enjoy similar levels of wealth, utility, or welfare. "Sustainability means meeting our own needs without compromising the ability of future generations to meet their own needs. In addition to natural resources, we also need social and economic resources. Sustainability is not just environmentalism. Embedded in most definitions of sustainability we also find concerns for social equity and economic development." In this paper we will see as applications of AI and ML , sustainable solutions in some special domain areas
Lane Detection For Automatic Cars
Get PDFThe first stage in developing an autonomous car is the lane detection system. To help us identify lanes, we've borrowed a pair of ready-made models. As a rule, these two models are very time-consuming and expensive to compute. To lessen the burden on the computer, we developed a technique called the "row anchor based" approach. The computational burden is reduced, and the no-visual-clue issue is addressed by using this technique. It is exceedingly challenging to identify lanes when we are unable to see them clearly, as occurs in inclement weather, when water is on the lanes, or when the lanes are not designated. No-visual-clue is the term for this kind of issue. ResNet-18, which is used for pretrained models, has been utilized. Because of this, velocity will rise. ResNet-34 is another option, but it is too resource-intensive for this particular project. Road detection from one image is used to locate the road in a picture so it can be used as a district in the automation of the driving system within the vehicles for moving the vehicle on the correct road given a picture captured from a camera attached to a vehicle moving on a road, which road may or may not be level, have clearly described edges, or have some previous acknowledged patterns thereon. Here, we apply techniques for vanishing point identification, Hough Transformation Space, area of interest detection, edge detection, and canny edge detection for road recognition to locate the road inside the picture acquired by the vehicle. To train our model to recognize the road in the fresh image processed by the car, we typically use hundreds of images of roads from different locations
A System For The Early Detection Of Plant Leaf Disease
Get PDFThe cultivation of crops is essential to the agricultural industry. Decreased growth rates are a direct result of food shortages, which are blamed on tainted crops. It is a major challenge in the agricultural industry to identify plant diseases. Incorrect identification causes substantial losses in both product assembly and market value. This study introduces a novel method for modeling the detection of plant diseases based on the categorization of leaf images using massive neural networks. The program's observation was made simpler and more accessible via the application of novel approaches and techniques. The proposed model has the versatility to distinguish diseased leaves from healthy ones and can recognize thirteen distinct types of plant illnesses. From what we can tell, this method of illness diagnosis was conceived for the first time. Agricultural consultants collected images to use as project documentation, and they took every other essential step to implement this disease identification model. Python and PyCham are our tools of choice for the deep CNN process
Effective Cloud-Based Strategies For Managing Online Reputations
Get PDFLeasing computing resources are now feasible thanks to the Infrastructure as a Service (IaaS) concept made available by cloud computing. In spite of the fact that leased computing resources provide a more financially advantageous answer to the requirements of virtual networks, customers are reluctant to make use of them due to low levels of trust in these resources. Multi-tenancy is a method for reducing operating expenses by allocating a single set of computer resources to serve the needs of several users simultaneously. The fact that computer resources and communication methods are being shared gives rise to concerns over the security and integrity of the data. Since the users are anonymous, it may be difficult for a person to decide who among their neighbours can be trusted. This may make it difficult for an individual to choose a place to live. It is very necessary to have faith in the capacity of the cloud provider (CP) to match customers with dependable co-tenants. Yet, it is in the CP's best interest to make the most of the usage of the resources. So, it enables the maximum possible degree of co-tenancy, which is unaffected by the actions of the user. We provide a powerful reputation management system that pays CPs for discriminating between genuine and malicious users. This prevents resource sharing across CPs in a federated cloud environment, which is one of the goals of our system. Through a combination of theoretical and empirical research, we demonstrate that the proposed method for managing reputations is effective and legitimate
Internet User Advice Based On Collaborative Screening And Voting
Get PDFThe option to vote on different social issues is a feature that has just recently been added to several social media sites. In the context of this question, there are fresh challenges and opportunities for counsel. In this study, we create a suite of recommender systems (RS) to mine users' social networks and group memberships in order to deliver social voting suggestions. We do this by using matrix factorization (MF) and nearest-neighbor (NN). We show that including information about social networks and group membership significantly improves the accuracy of popularity-based vote suggestions, with the former dominating the latter in NN-based methods. This was demonstrated by using data from social votes cast in the real world in experimental settings. In addition, we find that social and group information is valuable to light users to a greater degree than it is to heavy users. Experimentally, we observed that simple meta-path-based NN models performed better than computationally complicated MF models when it came to proposing hot votes. On the other hand, MF models performed better when it came to mining users' interests for cold votes. In addition, we recommend a hybrid RS, which is a combination of several distinct research strategies, in order to get the greatest possible amount of top-k hits
Literature Based Study On Healthcare Sustainable Solutions Using AI And Machine Learning Techniques
Get PDFAn article by Jiang, et al. (2017) demonstrated that there are several types of AI techniques that have been used for a variety of different diseases, such as support vector machines, neural networks, and decision trees. Each of these techniques is described as having a "training goal" so "classifications agree with the outcomes as much as possible…". To demonstrate some specifics for disease diagnosis/classification there are two different techniques used in the classification of these diseases including using "Artificial Neural Networks (ANN) and Bayesian Networks (BN)". It was found that ANN was better and could more accurately classify diabetes and CVD. Through the use of Medical Learning Classifiers (MLC's), Artificial Intelligence has been able to substantially aid doctors in patient diagnosis through the manipulation of mass Electronic Health Records (EHR's). Medical conditions have grown more complex, and with a vast history of electronic medical records building, the likelihood of case duplication is high. Although someone today with a rare illness is less likely to be the only person to have had any given disease, the inability to access cases from similarly symptomatic origins is a major roadblock for physicians. The implementation of AI to not only help find similar cases and treatments, such as through early predictors of Alzheimer’s disease and dementias, but also factor in chief symptoms and help the physicians ask the most appropriate questions helps the patient receive the most accurate diagnosis and treatment possible. This paper presents a literature based study on sustainabile solutions in Healthcare using AI and ML