A Review Study On Sustainable Solutions In Special Domains Using AI And ML

"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

Reference Based Study On Sustainable Solutions In Some Sectors Using AI And ML

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

Developing A Machine Learning Methodology That Is Both Reliable And Secure

To swiftly and effectively obtain robust features from a representation in the bag of words, we propose a semantically improved Marginalized Stacked Denoising autoencoder. There is a critical need for robust and selective statistical representations of text learning in this vast area of research. A unique representational learning technique for addressing this issue is proposed in this research. In order to develop our own method, Semantically-Enhanced Marginalized Denoising Auto-encoder (smSDA), we take the popular deep learning model stacked Denoising auto-encoder and apply a semantic modification to it. Several academic disciplines, including topic recognition and emotional analysis, are intertwined with the research on cyberbullying detection. They paved the way for the automated detection of cyberbullying. By mining the bullying dataset's inherent feature structure, our suggested method may locate a reliable and discriminative textual representation. Our proposed methodology is extensively tested using two publicly accessible cyberbullying corpora, with findings that demonstrate its superiority over existing basic text representation learning methods. The semantic extension also includes sparsity limitations and semantic dropout noise, both of which were produced with the use of domain knowledge and the word embedding technique. Extensive testing using real-world data sets has validated the efficacy of our suggested methodology

Reference Based Study On Soundtrack (Music) Sustainable Solutions Using AI Techniques

Artificial intelligence (AI) has been used in applications to alleviate certain problems throughout industry and academia. AI, like electricity or computers, is a general-purpose technology that has a multitude of applications. It has been used in fields of language translation, image recognition, credit scoring, e-commerce and other domains. Computer music is the application of computing technology in music composition, to help human composers create new music or to have computers independently create music, such as with algorithmic composition programs. It includes the theory and application of new and existing computer software technologies and basic aspects of music, such as sound synthesis, digital signal processing, sound design, sonic diffusion, acoustics, electrical engineering, and psychoacoustics. The field of computer music can trace its roots back to the origins of electronic music, and the first experiments and innovations with electronic instruments at the turn of the 20th century.  In this paper we will study how AI is applied for sustainable music solutions

Cloud Storage System Towards Dynamic Encrypted Cloud Data With Symmetric-Key Based Verification

Verifiable Searchable Symmetric Encryption, as an important cloud security technique, allows users to retrieve the encrypted data from the cloud through keywords and verify the validity of the returned results. Dynamic update for cloud data is one of the most common and fundamental requirements for data owners in such schemes. Attracted by these appealing features, both individuals and enterprises are motivated to contract out their data to the cloud, instead of purchasing software and hardware to manage the data themselves. So far, most of the works have been proposed under different threat models to achieve various search functions, such as single keyword search, similarity search, multi- keyword Boolean search, ranked search, multi-keyword ranked search, etc. Among them, multikeyword ranked search achieves more attention for its practical applicability. propose a secure and ranked multikeyword search protocol in a multi-owner cloud model over encrypted cloud data

Hybrid Cryptography For Data Safety In The Cloud

Users have access to high-speed networks and constant Internet connectivity, regardless of their physical location. Computing in the cloud is an approach that views the many resources available online as a single, cohesive whole. The term "cloud storage" refers to a certain kind of networked, online data storage paradigm in which information is kept in shared, remote pools of storage that are implemented using virtualization technology. Businesses and individuals that need their data hosted pay for or lease storage space from organizations that run massive data centers. Data centre operators virtualized resources per customer need and make them available to end users in the form of storage pools for archival or long-term data storage. It's possible that the resource is physically spread among many servers. The durability of data is a must for every storage medium. There have been numerous suggestions for data storage servers. Replicating a message so that each storage server has a copy of the message is one technique to make data more reliable. A distributed storage system is an ideal environment for a decentralized erasure code. Using AES and proxy re-encryption, we build a cloud storage system that can transmit data securely. At the first stage of this concept, the data will be encrypted using AES by the owner. The next step involves applying a dividing key inside the cloud, where the data has been broken down into smaller bits. Several data storage formats will be used. Data monitoring will be handled by a specialized data distributor. Whether the authorized user may recover the data in a reverse fashion from the cloud storage system

Filter-Based Product Search Engines With Dynamic Component Ranking

The use of faceted browsing is common on shopping and comparison websites. When dealing with problems of this kind, it is usual practise to apply a specified set of features in a certain order. This tactic suffers from two major flaws that undermine its effectiveness. First things first: before you do anything else, you need to make sure that you set aside a significant amount of time to compile an effective list. Second, if you have a certain number of aspects and all of the products that are relevant to your search are tagged with the same aspect, then that particular aspect is basically worthless. This article presents a method for doing online business that makes use of a dynamic facet ordering system. On the basis of measurements for specificity and dispersion of aspect value dispersion, the entirely automated system assigns ratings to the characteristics and facets that lead to a speedy drill-down for each and every prospective target product. In contrast to the methodologies that are currently in use, the framework takes into consideration the subtleties that are specific to e-commerce. These nuances include the need for several clicks, the grouping of facets according to the traits that they share, and the predominance of numerical facets. In a large-scale simulation and user survey, our approach performed much better than the baseline greedy strategy, the facet list prepared by domain experts, and the state-of-the-art entropy-based solution. These comparisons were made using the same data

The Arima Algorithm To Model And Predict Cyber Hacking Attacks

A security incident is known as a data breach when sensitive data is accessed without the consent of a website or an organization. This may happen for a number of reasons. The collection of confidential or personally identifiable information from an organization, whether on purpose or unintentionally, will be referred to as a breach of information. A violation may be defined as the unauthorized accessing of data by a third party; rules of this kind need to be accompanied by a safe and secure regulatory framework, but this is not the case in many businesses. So, the suggested model can be trained to adapt to new settings and anticipate future breaches by studying prior efforts. This may be done by assessing successful or unsuccessful attacks. In addition, the findings of this study have led to the development of a model that uses machine learning in order to protect a website from having its security compromised. The major objective of this body of research is to develop a machine-learning model that can monitor a website or system while simultaneously training from examples of state-of-the-art assaults. This model is intended to be trained in real time. The model under consideration has resulted in the production of a web application built with Django. This application gathers information from a variety of sources, including Amazon, Mesho and displays only the information that can be obtained in a secure manner from the website. The model is trained on a regular basis, and it derives its predictions from the many datasets that are currently accessible as well as from the most recent state-of-the-art assaults. This model will be trained using the previously collected datasets as well as the record of attacks and breaches that have occurred on our website

Lane Detection For Automatic Cars

The 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 Novel VLSI Design On CSKA Of Binary Tree Adder With Compaq Area And High Throughput

Addition is one of the most basic operations performed in all computing units, including microprocessors and digital signal processors. It is also a basic unit utilized in various complicated algorithms of multiplication and division. Efficient implementation of an adder circuit usually revolves around reducing the cost to propagate the carry between successive bit positions. Multi-operand adders are important arithmetic design blocks especially in the addition of partial products of hardware multipliers. The multi-operand adders (MOAs) are widely used in the modern low-power and high-speed portable very-large-scale integration systems for image and signal processing applications such as digital filters, transforms, convolution neural network architecture. Hence, a new high-speed and area efficient adder architecture is proposed using pre-compute bitwise addition followed by carry prefix computation logic to perform the three-operand binary addition that consumes substantially less area, low power and drastically reduces the adder delay. Further, this project is enhanced by using Modified carry bypass adder to further reduce more density and latency constraints. Modified carry skip adder introduces simple and low complex carry skip logic to reduce parameters constraints. In this proposal work, designed binary tree adder (BTA) is analyzed to find the possibilities for area minimization. Based on the analysis, critical path of carry is taken into the new logic implementation and the corresponding design of CSKP are proposed for the BTA with AOI, OAI