The production of minerals is very useful for human being and every living creature. Mineral are playing very vital role for living creature on the earth. Every mineral plays a different role with different combination as well as different type of productive nature. Minerals are deposited in different way on the earth’s i.e., igneous deposit, Sedimentary deposit and metamorphic deposit. Here we are talking Bauxite, Laterite and clay deposit and its “Systematic mining, Reserves estimation, grade and uses of Bauxite, Laterite and Clay minerals in mining industries’’ in Jabalpur and Katni district area. Jabalpur district Katni district area : It is the most developed and exploited bauxite area of M.P. Bauxites are associated with low level laterites and clay. The average thickness of bauxite in the leasehold of various area varies from 2 to 12m. Bauxite is derived from Vindhyan limestone and shale. Katni bauxite has good reserve of refractory grade as well metal grade.

The production of minerals is very useful for human being and every living creature. Minerals are the leading cause of extant phase of automation, & play a central role in the present phase of the national reduced & global progress of the nation. India has significant mineral resources. India procures many minerals (approximate 89 minerals) out of which 4 are fuel minerals, approximate 11 metallic and 52 nonmetallic and 2 minor minerals for example Bauxite, laterite, Copper, Lead & zinc, Gold, Iron ore, Chromite’s Manganese, Limestone, Dolomite, Diamond etc. Here we are talking Bauxite, Laterite and clay deposit and its “Systematic mining, Reserves estimation, grade and uses of Bauxite, Laterite and Clay minerals in mining industries’’ in Jabalpur and Katni district area. Jabalpur district Bauxite deposits are located near Katni, Bakeware, Salemabad and Dundi area. Most of these smaller deposits are leaseholds. Katni district area: It is the most developed and exploited bauxite area of M.P. Bauxites are associated with low level laterites and clay. The average thickness of bauxite in the leasehold of various area varies from 2 to 12m. Bauxite is derived from Vindhyan limestone and shale. Katni bauxite has good reserve of refractory grade as well metal grade. The important bauxite mining areas in Katni area are Tikuri, Tikariya, Bargawan and Padarwara, Kusmi, Baghai etc.

The early detection of breast cancer is critical, as therapeutic actions are likely to be successful in the early stage of the disease. The different best radiological techniques are currently available for early detection of breast cancer. However, some of the breast lesions are missed during screening by radiologist, as they need to interpret large number of screening programs. Medical image processing techniques have been developed to help radiologists to improve detection process. The need for the thesis is to exploit recent developments of image processing techniques to improve breast cancer detection and reduce diagnosis errors. To proposed a new algorithm using hybrid machine learning for Breast Cancer Detection.

As we know by day to day increase in fuel like gasoline, diesel, petrol with day by day increasing the demand of vehicles we have to choose an alternative option for fulfil the demand of people. From about 100 varieties of oil seeds only , 10-12 varieties have been tapped so far . the main non -edible oil is Karanja, neem, mahua, olive etc. when we use non edible oil like mahua, neem, jatropha, olive etc by mixing with diesel an experimental results will found. when we mixed jatropha, mahua, neem as 20% with 80% diesel and 40% and 60% diesel. An experimental results will found with help of RK software. The emission like Cox, Sox , NOx is also less in these type of biodiesel fuel . If such type of process will be scaled at commercial level then a suitable business will be formed and beneficial income for farmers.

Cloud computing has a lot of advantages for those who use it, but it also has a lot of drawbacks and inefficiencies, the most important of which is security. In order to take use of a remote cloudbased infrastructure, a corporation must essentially hand up sensitive and proprietary data and information. To limit access to such sensitive and confidential data, secret sharing mechanisms are employed. The number of participants in the reconstruction phase is critical for recovering the secret in threshold secret sharing schemes. In this research, we introduce outsourced computation Design and Implementation Cloud Computing Data Using Light Weight Hybrid Encryption Scheme.

With a continuous escalation in demand of power, the Indian Electrical system is in constant demand for long transmission lines to fulfill its requirement due to extremely distributed demand and generation location. Advanced HVDC system is one such possibility that finds its utility, especially during long-distance transmission. Such electrical transmission systems are prone to short circuit faults, which subsequently leads to a large current, which will eventually harm or damage the system’s equipment. Thus, the system requires a quick restoration in order to reestablish power transmission and assure system safety. Hence, the objective of this work is to develop a model, which can precisely assess the location of the fault. The work intends to cultivate a model, which will not only provide accurate results but is also collectively optimal. A Bi-polar transmission line 814 km long and operates at 700 kV, with the ability to transfer 1500 MW of power, developed on PSCAD/EMTDC software based on CIGRÉ benchmark guidelines. The designed model is further simulated for short circuit fault with fault ON resistance of 0.01 ? and fault OFF resistance of 1.0 x 10 ⁶ ? with varying fault location along transmission line at an interval of 1 km. The acquired data collected and processed for feature extraction. Data from both the ends of the transmission line is used for training and testing of deep neural network models. The evaluation of the proposed system has been done based on the mean squared error and accuracy of fault estimation. The error obtained during testing are in the range of 1-2 km, which is outperforms contemporary baseline approaches.

The malicious activity and policy violations on network of systems is continuously monitored by device or software application called IDS passively monitor the data and uncover any potentially disastrous connection. Technically IDS are aimed at serving three important security functions i.e., monitoring the data, unearthing any potentially harmful transactions and finally responding to unauthorized activity. With the gigantic structure of the Internet, its distributed nature and lack of central security mechanism, the prevention of attacks is not possible and therefore detection and recovery from attacks become indispensable. The IDS does exactly as the name suggests, it detects the possible intrusion. To study the impacts of application of the wavelets on the detection coverage of Recurrent Neural Networks classification model for network Intrusion Detection.

Cloud services for smart objects face challenges with latency and sporadic connectivity. Positioned between the cloud and smart devices, fog devices alleviate these issues. Their high-speed Internet connection to the cloud and proximity to users enable real-time applications, location-based services, and mobility support. Cisco has championed fog computing in domains like smart grids, connected vehicles, and wireless sensor networks. This survey explores extending fog computing to decentralized smart building control, recognizing cloudlets as part of this paradigm and linking it to SDN scenarios. Literature review findings indicate a limited number of articles on this topic. The study delves into demand response management in smart grids, cooperative data scheduling, and adaptive traffic light challenges in vehicular networks. However, security, privacy, trust issues, and network control regulations remain underexplored in fog computing.

Computer networks security plays an important role in modern computer systems. In order to enforce high protection levels against threats, a number of software tools are currently developed. Intrusion Detection Systems (IDS) aims to detect intruder or anomaly in the computer networks. Software model protects a computer networks from unauthorized users through detecting intruders in the network. In this we build a machine learning classifier and trained the model on the NSL-KDD dataset, after training the model are able to detect or classify the attacks in to category like normal or attack. Recently there is already work done by data mining techniques to accurately detect the malicious activities. So to further improve the accuracy of this intrusion detection system we proposed a deep learning machine.