Authors - Aafiya Anjum Abdul Rafique, Martin H Mollay, Shailesh Gahane, Deepak S. Sharma, Pankajkumar Anawade Abstract - Blockchain technology is a complete makeover for digital identity management, which solves the major problems seen with the previous centralized systems, like inefficiency, lack of control in front of the users, and susceptibility towards data breaches. The paper draws the modes of changing the digital identification system using blockchain technology by providing insights into how it is decentralized, transparent, and safe. Blockchain could improve privacy and trust by advising people to take control of their data and reduce dependency on other parties. The literature analysis shows that blockchain technologies, particularly those based on cryptographic security, give solid answers to privacy problems, which allow users to share data but keep sensitive information safe. Analysis of scalability issues, limitations in storage, and massive computational overhead of consensus protocols like Proof-of-Work. It proposes alternative solutions such as proof-of-stake, sharding, and sidechains that may circumvent their weaknesses. Interoperability between different blockchain systems remains one of the most significant areas of development toward support at a broader scale of adoption. Despite the challenges above, blockchain has immense possibilities in many sectors, including government identity systems, healthcare, and finance, for safe and independent management of identity. It underlines the revolutionary importance of blockchain-based identification solutions within the digital economy. It calls for further research, pilot projects, and regulatory modifications to overcome these issues and realize the full potential of these solutions.
Authors - Sanila S, S Sathyalakshmi, D. Venkata Subrahmanyan Abstract - Electroencephalography (EEG) remains the leading technique for identifying and diagnosing epileptic seizures due to its effectiveness in monitoring brain activity. However, the nonstationary nature, large volume, and rapid accumulation of EEG data present significant challenges for traditional analysis methods. To address these issues, a transition from basic data mining techniques to advanced machine learning and deep learning approaches is essential. This study focuses on developing algorithms to enhance the accuracy of seizure predictions while minimizing the volume of EEG data processed. The proposed method involves dividing EEG signals into fixed-sized windows to reduce data complexity, followed by extracting key features such as the top_k amplitude values from each window. These extracted features, combined with statistical measures of the EEG data, are then used to train classification algorithms to determine whether a seizure is occurring or not. This approach aims to balance efficiency and predictive accuracy, addressing both the computational and diagnostic challenges associated with EEG analysis. The entire raw dataset is experimented with Deep Neural Network Algorithms like Bidirectional Long Short-term memory with additional functionalities like Attention Mechanism and Spatial Weight matrix addition. Finally, both 2 D CNN and BiLSTM are applied in parallel with the additional functionalities of FFT applied EEG signal. The results are promising and found that ANN predicted with an average accuracy of 98.5%, 2DCNN with 94.3% BiLSTM with 99.6% and bi model architecture of top_k 2D CNN with Bi LSTM on FFT applied EEG signal including attention mechanism and Spatial weight matrix predicts better than all previous models with 99.8 % accuracy. .
Authors - Dhairya Goel, Chakshu Gupta, Saarthak Bansal, Chaitali Bhowmik Abstract - Mushrooms are a type of fungi, which have unique traits and health advantages. They also helps to fight against the cancer cells. Our main initiative of this research is to classify the mushrooms into two categories one is poisonous and other is non-poisonous. Mushrooms are of of different categories some of them can be used for daily needs but some of them has toxic ingredients in them which is harmful for consumption. So classifying the mushrooms correctly becomes very important as if someone uses a toxic mushrooms it can lead to serious health effects. Classification algorithms helps to solve this issue. Here we are using various ml algorithms to classify the mushrooms some of them are random forest and Decision tree Algorithm. Our main goal is to categorize the mushrooms correctly. We have achieved the highest accuracy with random forest. It is able to differentiate the mushrooms most effieciently and effectively. These results shows that classification algorithms can prove to be very important in categorizing mushrooms.
Authors - Pradnya Apte, Dipti Durgesh Patil Abstract - Agriculture forms the backbone of many developing countries, including India. Accurate crop yield estimation can give societies a better handle on food security and resource management. Existing studies on crop yield and harvest prediction apply deep learning as well as machine learning models. Various crop parameters like soil type, climate, water content, and so on have been used to predict crop yield. More advanced techniques include the use of satellite imagery and optical and SAR data along with plant indices like NDVI. Machine Learning algorithms, including KNN, SVM and Random Forest regression have been widely used for yield estimation. Deep learning approaches work by extracting salient and relevant features from images or non-visual data to estimate crop production. Networks like 3D-CNNs, LSTMs and Auto encoders have achieved significant improvement in accuracy in estimating crop yields from satellite images. This paper aims to summarize the techniques and models being used for the purpose of yield estimation along with limitations, and possible areas of further study.
Authors - Kasif Qamar, Supriya Narad Abstract - Emotional intelligence in Artificial Intelligence is an important and exciting growing field with much potential for assisting human-computer interactions in different domains. Self and others’ emotional awareness are referred to as emotional intelligence and is steadily being deemed paramount to develop technologies in artificial intelligence that will in the end be effective when handling needs and human states. In recent researches, it has been observed that although in real way, AI cannot be so expected to feel like human beings simulation does exhibit mimic emotions that add to the enhancement of the user experience and acceptance more so in service-oriented applications Emotional Simulation of Artificial Intelligence and The awareness of the EI is really rising in various organizations in today’s workplaces especially with integration and Automation of work using artificial intelligence. Since the roles and responsibilities of human beings working in industries will change due to AI and automation, EI skills will be are important to be applied by the employee’s at all organizational levels. However, machines are good at things that involve rules of logic and therefore getting them to understand and among the limitations of URLs of expressing human feelings, answering to the feelings still remain a problem to be solved in AI. Hence, improving EI becomes something of enormous value to be competent in the new era of jobs.
Authors - Anup Vinod Pachghare, Smita Deshmukh, Satish Salunkhe Abstract - Human Resources Management plays a key role in the company’s growth by recruiting high-quality employees and evaluating their performance by using the Machine Learning (ML) technique. Despite these rigorous efforts some employees still resign before their contracts expire, which negatively impacts business. Existing methods have considered various factors influencing employee turnover across different employee groups. This paper proposes an Ensemble Learning approach which integrates AdaBoost, K-Nearest Neighbour (KNN), Random Forest (RF), stacking and Voting to enhance churn prediction accuracy. The ensemble learning mitigates the risk of overfitting by combining predictions from multiple models, making it less sensitive to irrelevant features. This approach efficiently captures diverse patterns in the employee churn data, achieving better accuracy. AdaBoost captures complex patterns, while KNN extracts valuable data from employee churn data. By stacking these methods, their combined strengths lead to enhanced accuracy in predicting churn data. Initially, the data collected from employee churn records and pre-processing phases handle unwanted noise and min-max normalization, which standardizes the feature vector to ensuring uniformity across the dataset. The proposed ensemble model obtained 91.06% accuracy, and 0.8853 of recall on the employee churn dataset compared with conventional techniques like Artificial Neural Networks (ANN).
Authors - Pranav Jawale, Srushti Bonde, Dhruv Gidwani, Omkar Aher, Bhavana Kanawade Abstract - The GPS-based toll tracking and speed monitoring system uses GPS technology to revolutionise highway toll collection and road safety. The system calculates payables based on distance travelled by the vehicles on highways and monitors vehicle speeds to ensure fair charges while discouraging speeding. The system delivers real-time notifications for toll deductions and penalties, supporting transparency and eliminating the need for traditional toll booths. Users can access a dashboard to track their journey, view toll routes, and review their vehicle and transaction history, offering a comprehensive and user-friendly experience.
Authors - Akshay Kumar, Sudhir Agarmore, Edidiong Akpabio, Kumar Gaurav, Nitesh Kumar, Aditya Mandal Abstract - Intrusion Detection Systems (IDS) has been seen to be an integral aspect of network security, where an extra layer of protection mechanisms may contribute to protection against different kinds of cyberattacks. With fast-evolving cyber threats, from simple malware to sophisticated zero-day attacks, continuous developments are required in IDS technologies. Traditional IDS models, such as signature-based detection, work better when applied to known threats but are pretty weak against emerging and unseen types of attacks. On the other hand, anomaly-based IDS models give pretty good results in finding unknown attacks, but most of them report a high false-positive rate. Finally, this work provides an overview of current state-of-the-art IDS methods and focuses on machine learning, deep learning, and hybrid models for intrusion detection. We further discuss the benefits and limitations of both the supervised and unsupervised learning algorithms and their applications in anomaly detection and pattern identification within network traffic. It investigates the applications of deep learning methodologies, such as CNNs and RNNs, within the IDS framework. These models give a high chance to work with complex data and detect various kinds of sophisticated attacks efficiently. Hybrid systems that combine traditional detection methods with machine learning demonstrate better accuracy and fewer false positives. Among the future directions of research that will be discussed are development challenges for the scalability of IDSs, real-time detection, and handling zero-day vulnerabilities for improving the efficiency of IDS in securing modern network infrastructures.
Authors - Koyana Jadhav, Aditya Shinkar, Mayank Sohani Abstract - In the light of dynamic pricing policy, flying is becoming too expensive, and it's really hard to book tickets at proper prices. In response to this, researchers began discussions on how machine learning models could be used to predict an approximate fare for a flight so that the passenger could purchase at the most ideal time to get low fares. These models consider travel dates, destination, airlines, stopovers, timing of booking, holidays, and demand. Techniques used are Decision Trees, Random Forest, Gradient Boosting, and ANNs. These have different strengths-some, such as ensemble methods, Random Forest and Gradient Boosting, with high robustness in terms of predictions because they average multiple decision paths; ANNs represent complex, non-linear relationships but at the cost of significant computation. Model performance was evaluated using Mean Absolute Error, Root Mean Square Error, and R-squared. This research informs passengers on price trends, and better booking decisions will be achieved. Real-time data integration and more advanced algorithms comprise future improvement prospects. The research work bridges the gap between revenue strategies employed by airlines and the need of the travellers to travel affordably, thereby optimizing passengers travel costs.
Authors - Ketan J P, Amulya A Shetty, Ashwini Bhat Abstract - In an era defined by using heightened virtual connectivity, the safety and privateness of voice communication are important. This research paper introduces an innovative system designed to establish secure voice communication between two systems. Including a combination of cryptographic algorithms, including RSA, 3DES (Triple Data Encryption Standard), modified RSA, and modified 3DES, the proposed solution gives confidentiality and integrity of voice data during transmission. The need for such a system is underscored by the growing demand for secure communication in sectors where sensitive information is exchanged, such as military and intelligence operations, healthcare, and business. This study explains a client-server architecture where the client system employs RSA and 3DES for encryption, while the server system utilizes corresponding decryption mechanisms. Socket programming serves as the connectivity bridge, with the server's IP address acting because the transmission key.
