Author - mgmu

Dr. Vilas Sapkal holds an M.Tech, Ph.D. in Chemical Engineering from IIT Bombay. He takes over the reins from Dr. Sudhir Gavhane who has served as the first Vice-Chancellor of MGM University and has ushered in 48 programs across the various constituent colleges. Dr.Sapkal has been teaching for about 32 years and has held leadership positions: Vice-Chancellor of Rashtrasant Tukdoji Maharaj Nagpur University (from 2010 to 2014 ) and earlier he has served as Vice-Chancellor of Kavikulguru Kalidas Sanskrit University, Ramtek, and Sant Gadge Baba Amravati University. In the field of research, Dr. Sapkal's contribution has been immense. He has worked on various research projects of UGC, AICTE, DST, MNES. Besides, he has also served as an expert on AICTE's National Recognition Board as well as for NAAC.

Dr. Sharvari Chandrashekhar Tamane, Professor and HoD, Information Technology, MGM University, Aurangabad has published a patent on “Deep Learning Based Intrusion Prediction Model“ on 29^th January 2021 in the Indian Patent Advanced Search System, Government of India, along with Ms. Manisha Bharati, AP, Department of Computer Engineering, Indira College of Engineering & Management, Pune.

1. Introduction:

Cloud computing sites are a major obligation for interviewees who are interested in assessing the vulnerability of their services and as a result have misused many resources. The growing number of attacks and vulnerability tactics requires preventive measures by program managers.

Predicting the user's intentions when using the app can improve the services provided to users by adjusting the app's resources to their needs. In this context, the need for a more efficient and faster security system is gaining importance. These measures are further compounded by the increase in data heterogeneity and the increasing severity of the attack. In addition, less response time from human resources and the greater amount of information and information generated, makes the decision-making process more difficult. In response, there is an increase in the use of Intrusion Detection Systems (IDS), as a means of identifying attack patterns, aggressive actions and unauthorized access to the environment.

1. Proposed Attack Detection Architecture

There are two main stages in the construction of the attack detection, as shown in Figure 1. The first phase is the “Model Builder” to perform (i) data collection, (ii) data classification, (iii) model training, and (iv) feature selection. In "Data Collector," it is necessary to collect malicious data and data from network attacks, such as in the IoT environment. The dataset distinguishes each type of attack and malicious data. Each category includes all data with the same category of attack and risk data the "Selection Feature" module, selects the most appropriate features for each type of attack category. This module is essential for achieving a heavy-duty acquisition program with high acquisition performance. The method of selecting an element based on the combination would be acceptable to select the appropriate element for each type of attack. After obtaining the most suitable feature sets, a trained model for each type of attack will be made with machine learning algorithms (ML) in the "Model Trainer" module. We have tried several ML algorithms, including naïve Bayes, neural input network (ANN), Convolution Neural Network (CNN) etc. As mentioned, filter-based selection methods are very similar to ML-based acquisition programs.

Figure 1. The block diagram of the proposed attack detection prediction architecture

3. Research Problem:

As the network user grows, network attacks become more and more common, more difficult to access, traditional network technology to meet network security requirements, while network access (IDS) is a form of surveillance technology, it has become an important topic in the field of network security. Network access detection is a problem of isolation in pattern recognition, including mainly modules such as feature selection and partition selection and optimization, etc. Network data is very complex with high-density features, and the feature set contains some of the more obsolete and useless features, this will increase the training time model and complications of the computer, and have negative effects on the acquisition of entry. Therefore, before modeling detected network penetration, it is common to make a feature selection algorithm to select feature subsets in the acquisition results, and reduce the size of the features, current methods based on sequential search algorithms, key component analysis, genetic algorithm, particle particles optimization algorithm and other methods for selecting features. However, no approach has so far provided a satisfactory approach to the solution to malicious internal attacks. Therefore, this study will investigate appropriate strategies to reduce and overcome the problem of internal attacks / malicious intruders.

Research Hypothesis: we thought that one of the best ways to detect threats and reduce false positives could be provided by CNN-based IDS models within Cloud Computing over conventional methods.

The question in the study's study was "does the Deep Learning system improve the performance of Cloud Computing security compared to IDS only?"

4. Objectives:

The purpose of this work is to create a distinction that can distinguish network flow as positive or negative. The problem is understood as a problem of classification and supervised learning using labels provided in the database that identifies network flow as positive or negative. Various methods of data classification will be explored for problem-solving as the binary separation problem distinguishes between each category of attacks assigned to the database.

5. Methodology:

We used new virtual reality data set CSE-CIC-IDS2017. The test is performed on Google Collaboratory under python 3 using TensorFlow and Graphics Processing Unit (GPU) and 25GB RAM and 200 GB with extended Cloud Space. Details of the IDS method used in the test are shown in Fig. 2. Specifically, the method consists of four phases: (1) Database Phase, (2) Pre-processing Phase, (3) Training Phase and (4) Testing Phase.

Figure 2. Flowchart of the IDS methodology.

We have used some python libraries, such as Scikit-learn, Camera, and Tensorflow, to support program implementation. Scikit-learn: is a support tool to use many algorithms for machine learning efficiently. It also provides the task of dividing data sets into multiple subsets, including splitting training and testing sets. We used this library to separate selected databases from training and testing data. In addition, we have used this library to experiment with tree-based algorithms and the Naïve Bayes. Keras: is a high-level neural network Python-API, and capable of operating on dependencies, including TensorFlow. Built with a focus on enabling rapid testing. Works over Tensorflow. It can also support multilayer view. Tensorflow: an even more sophisticated library of calculations using data flow graphs. It is possible to train and use wide neural networks effectively. Made with Google, and is an open-source software for high performance numeracy. It can strongly support machine learning and in-depth learning in many other scientific fields. Various activation tasks such as sigmoid, Tanh, and ReLU were used to determine which option was best for the proposed system.

6. Experimental Results
7. CSE-CIC-IDS2017 Dataset

This database contains data taken from Monday, July 3,2017, to Friday, July 7, 2017. The CICIDS2017 database is being revised by Sharafaldin et al., using the attack include BruteForce SSH, DoS, Heartbleed, Web Attack, infiltration, Botnet and DDoS, and Brute Force FTP. The CICFlowMeter tool is used to extract 80 network flow features in generated network traffic. The CICFlowMeter tool is used to extract 80 network flow features in generated network traffic. In addition, the CICIDS2017 database yields an ambiguous character of 25 users based on specific protocols such as FTP, HTTPS. Analyzing the typical features of each model is taken as shown in Fig. 3 below. Analyzing the training time for each model taken Above we can see that XGBoost takes less training time followed by ExtraTree and Decision Tree and Adaboost takes longer compared to training model.

Figure 3: Amount of Traffic Data Samples for Each Type in Datasets

Accuracy Analysis for each model and as shown in the graph Random Forest and ExtraTree are the most accurate models as they offer 98 Percent accuracy and Decision Tree provides 92 Percent accuracy, AdaBoost gives 80 Percent accuracy and XGBoost shows 62 percent accuracy.

The CSE-CIC2017 dataset used to train the model is extensive, including up-to-date network-level traffic attack scenarios, including imbalanced classes. The advantages of the ensemble methods used are resilient to outliers, feature scaling and missing values, particularly when highlighted. It can be seen from the experiment that the ensemble methods outperform conventional methods on this type of complex dataset, and Random Forest is the best accuracy classifier and feature selection for data set size reduction. Since training time for Extratrees is less and highest Accuracy we can conclude that Extratrees is the Fastest and accurate model.

1. Design of CNN Model

CNN is the most advanced learning algorithm used for image training. To upgrade the CNN-based intervention model, converting the CIC-2018 database into images is required. We convert each labelled data into 13x6 image size because each data contains 78 elements without the ‘Label’ feature. 'Label' is used for image classification. The CNN model has convolutional layers, max-pooling layers, and a fully connected layer. We can find the perfect CNN model by editing those layers and model parameters such as kernel size, character number, and number of school leavers. Figure 4 shows our CNN model for CIC-2018. Table 1. Shows CNN model and parameters. We apply three layers of convolutional and two layers of maxpooling after each convolutional layer. Although the max pooling layer is not mandatory for the CNN model, we use the layer because there is very little chance of losing important features in large compounds as the converted images contain only numerical data rather than hidden signatures. In addition, we use 'value' as a function to activate each decision base. To reduce overcrowding, dropping is applied after each step of the top joint. Finally, a fully bonded layer is still distributed after the final layer of bonding. For CSE-CIC-IDS2017 dataset We had used Monday as the training is set with other csv files as test setup, Here Monday’s data contains only Benign data and other days contain Benign and Attack data.

Figure 4. An Intrusion Detection Model based on a Convolutional Neural Network

Table 1. Training parameters CNN Model.

Table 2. Accuracies Achieved by different models on dataset

Figure 5. Testing Accuracy Graph

Results As per the Table. 2 Clearly Highlights Convolution Neural Network one of the Deep Learning Approach Outperformed the traditional Machine learning Approaches when applied on dataset CSE-CIC-IDS-2017.

Conclusion:

We have noticed that the model created using the Deep Neural Network uses AutoEncoder layers as hidden layers show better results compared to the results from the model created using a using machine learning techniques. So, our acquired discovery structure can detect known attacks and their variations, too, and the system is often extended to detect new types of future attacks. In addition, the system has acquired a lightweight environment and therefore the best accuracy of using the feature selection method. Hybrid separation can also be very helpful in achieving easy accuracy with quick detection. IPS and IDS meet important business needs in terms of security. it is the basis of technology that tracks, monitors traffic across the network, identifies suspected traffic congestion, blocks and takes necessary action by notifying the supervisor. If an organization wants to send information privately then it is best to use IPS /IDS. Looking at current scenario of Data increase we suggest Deep Learning Approach like Convolution Neural Network Classifier to Protect the Cloud Network from Intrusions.

Dr. Sharvari Chandrashekhar Tamane, Professor and HoD, Information Technology, MGM University, Aurangabad has published a patent on “Intelligent way to identify the cloud based attacks in web applications using encryption algorithm“ on 29^th January 2021 in the Indian Patent Advanced Search System, Government of India, along with Ms. Smita Chavan, AP, GCOE Aurangabad.

Nowadays, security measures, such as, authentication and confidentiality, in content-based publish / subscribe systems is a major challenge. It is very difficult for publishers and subscribers to authenticate messages between them. Many users upload their personal data on a cloud, which makes web application’s security a very important issue. One should always consider data or network security when thinking about real time projects. Publish-subscribe is a communication paradigm that supports dynamic, many-to-many communications in a distributed environment. Content-based pub-sub systems are often implemented on a peer-to-peer infrastructure. The main objective of the invention is to provide a simple encryption and decryption device.

This Patent will provide:

1. A new approach to confidentiality and authentication in a brokerless content-based publish / subscribe system.
2. The authentication of publisher and subscriber, as well as the confidentiality of the event, is guaranteed by adapting the encryption mechanisms. These are based on pairs of publish / subscribe systems.
3. Additionally, an algorithm for grouping subscribers based on their subscriptions preserves a weak concept of subscription confidentiality.

The invented technology with the underlying algorithm is a fast block cipher that can be implemented efficiently in hardware or software. The algorithm makes heavy use of data-dependent rotations.

This invention provides a secure method to initialize the cryptographic system to allow secure operations and protect against tampering with application software and programs. These are retrieved from an encrypted file in external memory and authenticated before being executed. It uses searchable encryption to allow efficient routing of encrypted events.

The global approach provides granular key management, and the cost of encryption, decryption, and routing is determined in the order of the signed attributes. In addition, evaluations show that security is cost effective when compared to the throughput of the proposed cryptographic primitives. The latency associated with publish/subscribe overlay creation and event transmission is also cost effective.

Overall system is useful in any organization as utility, for creation of an effective examination system to communicate confidential data securely over the cloud.

Eloquent and confident students of MGM University, College of Journalism and Mass Communication grabbed 4 Best Parliamentarian Awards out of 5 in 'YOUTH MOCK PARLIAMENT' organised by Yuvak Biradari and V.S. Page Parliamentary Training Institute, Maharashtra Assembly at Vidhan Bhawan, Mumbai.

Winners

1) Bhagwat Zumbad
2) Amol Doifode
3) Komal Pareek
4) Nishant Pawar

Yuvbhushan cash prize of Rs. 50,000 has been awarded to Ashay Yedge, student of B.A Int Journalism, First Year amongst 3000 participant students.

Retired High Court Chief Justice Dharmadhikari, Economist & Ex Rajyasabha Member Bhalachandra Munagekar, State Youth & Sports Minister Aditi Tatkre, Parliamentary Affairs Work Officer Nilesh Madane awarded the winners.

Technical Excellence Center inaugurated by Hon. Shri. Nitinji Gadkari on the occasion of 38th foundation day of Mahatma Gandhi Mission on 20th December 2020

MGM's Innovation, Incubation & Research Centre (IIRC), first of its kind in Maharashtra State. It is developed to inculcate knowledge and skills to meet the demands of Design & Manufacturing, E-vehicle, Automobile, Robotics and Automation industries. In pursuit of Excellence as a part of JNEC’s Industry-Academia Initiative, IIRC has been established by MGM University to promote research, technical skills which are significant for students’ development, and provide consultancy services to nearby industries & tool rooms. 

MGM University has come up with the unique state of art infrastructure to meet the demands of all types of Industries. IIRC is an outcome of visionary inspiration of MGM Management. It was developed in two stages: Phase-I, which includes Robotics, Mechatronics, Industrial Automation and Computer Integrated Manufacturing which was inaugurated by the auspicious hands of Padmashree Hon. Sharadchandraji Pawar on 20th December 2019, Phase-II comprises of Industry 4.0, Electric Vehicle, Advanced Driver Assistance System (ADAS), Unmanned Aerial Vehicle (DRONE), Additive manufacturing & Reverse Engineering inaugurated by Hon. Shri. Nitinji Gadkari on the occasion of 38th foundation day of  Mahatma Gandhi Mission on 20th December 2020. To be convergent with the technologies introduced in IIRC, a team (13 Nos.) of interdisciplinary faculty member’s undergone rigorous trainings in Rheine, Germany in June 2019. Electric Vehicle, ADAS, DRONE, have been introduced for the first time in India in association with MSC Hexagon (USA), SSIGMA (India) and CSTT (Germany). 

IIRC has been working in association with nearby MSME’s in development of various projects with involvement of university students like CNC Retrofitting, Mechatronics Manufacturing Factory Set-Up, Medical Emergency Drone, Other Manufacturing machines & working prototypes, Automation of Shearing machines, etc. To promote such activities on a larger scale IIRC seeks MSME incubation centre to incubate aspirant entrepreneurs from rural as well as urban background. IIRC comprises of industry sponsored labs such as Endress +Hauser Process Instrumentation and BMW.

As a social responsibility during COVID-19 pandemic situation, MGM University has contributed through the design & development of Face Shields, Swab Collection Tube Tray, C-Type Shield Structures for Medical Surgeries, Ventilator. In this regard, IIRC would like to mention here that more than 1.5 Lakhs of Face Shields been supplied in the state of Maharashtra, Karnataka, Rajasthan and Gujrat in association with our alumni entrepreneurs.

Currently we are working on Electric Vehicle area such as axial flux motor, Battery Management System (BMS), Lithium Battery pack manufacturing system, ICE to EV conversion kits and Design & development of drones for agricultural application, fire fighting Drones etc.  

Global warming is increasing due to Greenhouse Gases (GHG) emissions. GHGs include carbon dioxide (76%), methane and other gases which are the cause of climate change and are not good for an environment. If we want to control GHGs emission then the Government of India must decide on reducing carbon dioxide per unit of Gross Domestic Product (GDP) by 35% until the year 2030, compared to its level in the year 2015. At the same time all countries must take measures to curb GHG emissions, sooner the better; otherwise more burdens will be to do so in later years. Just to remind you that India is the world’s third largest GHGs emitter in line with China and USA.

As per the recent report published by Global Coal Plant Tracker, India has 221 Gigawatts (GW) of operating coal plants which is the world’s third largest fleet with 11% of global capacity. Another 36GW is being built and a further 58GW is at earlier stages of development. India being world’s second largest coal consumer after China and having moved ahead of the US in 2015 in terms of consumption, recent report by the Potsdam Institute for Climate Impact Research quotes that India released 3,571 M tonnes of carbon dioxide equivalent (MtCO2e) in the same year. For electricity generation, India largely depends on thermal power plants which emit GHGs, predominantly carbon dioxide and carbon monoxide, leaving heavy carbon footprints and climate change. If proper pollution control measures are not taken, coal based thermal power plants put health risks to humans and environment due to release of carbon dioxide, carbon monoxide and other hazardous gases in the air. As per a recent report published by the Lancet Planetary Health, one in every eight deaths in India is due to air pollution. Moreover, heavy industrialization and excessive use of fossil fuels and burning of biomass in rural areas are resulting in more of these gases being released into the atmosphere. At the same time it is to be noted that due to COVID-19 pandemic situation in the year 2020 and subsequent lockdown, carbon dioxide emissions have drastically reduced, but lockdowns must not be mistaken as the solution to this problem.

So after understanding the effects of carbon emissions, question arises how to curb these emissions primarily from thermal power plants and other heavy industries such as cement, steel and refineries? Well, banning deforestation, encouraging tree plantation, reducing burning of fossil fuels etc. will give positive result after some time in future. Hence for an immediate effect to reduce or eliminate this detrimental effect, carbon present in the earth’s atmosphere as carbon dioxide or carbon monoxide can be trapped or converted by carbon sequestration methods. Carbon sequestration or capture technology needs to be refined and implemented to control these emissions. In this method carbon dioxide present in the atmosphere or emitted from industries is separated and trapped or deposited in a reservoir deep in the earth. Alternatively carbon dioxide or methane can be converted to valuable products instead of releasing it into the atmosphere.

Carbon sequestration can be classified into two approaches, namely Natural which is more into using biological processes to improve management of forestation, wetland, soils quality through regenerative agriculture which is about capturing carbon from the air and releasing it into the soil as an organic carbon etc. However technological approach is more into technology enabled removal of carbon or carbon dioxide from the air, flue gases from Industries and exhaust gases from automobiles. Moreover natural carbon-removal processes can also be improved using technology approach to increase carbon sequester. In a natural approach continuous soil testing needs to be done to analyze nitrogen requirement of soil and subsequently be supplied in a controlled manner to convert carbon in soil to microbial organic matter. Also implementing regenerative agriculture, which is a method wherein farmers opt to not plow soils so that carbon is not released and sow seeds by drilling it into the soil. Also plants called cover crops can be grown to cover the soil after harvesting the main crop. Other ways include planting three or more crops in rotations over several seasons coupled with grazing by livestock.  Sometimes reduced use of fertilizer and pesticide is also considered as regenerative agriculture. All these measures taken collectively or independently improve carbon sequestration in the soil.

Talking about technological approach, it is necessary to point out that many available technologies to sequester or capture carbon dioxide are at demonstration level. In carbon sequestration or carbon capture, carbon dioxide present in the atmosphere or emitted from industries is chemically or physically removed. In chemical methods carbon dioxide is converted to valuable material instead of releasing it into the atmosphere whereas in physical method carbon dioxide is trapped or deposited in a reservoir, called geologic sequestration. Some companies are working on separating it from air and dumping it deep into Earth, some are working on thermal power generation process which will have zero discharge of carbon into the atmosphere e.g. process called ‘Allam Cycle’ converts gaseous fuels into thermal energy, but importantly captures carbon dioxide and water in the process. Another latest example is of BS-VI emission standard norms compliant vehicles, made mandatory by Government of India are installed on the exhaust of an automobile. It is basically a catalytic converter which captures carbon present in an exhaust gases and releases less polluted gases into the atmosphere. Other processes include enhanced oil recovery from oil fields where carbon dioxide is pumped in oil well from one end which pushes oil out from other end. Once oil recovered, these wells are then plugged trapping carbon dioxide deep underground in these wells. Carbon mineralization process captures carbon dioxide from the air and stores it in the form of permanent carbonate minerals, such as calcite or magnesite. Carbon dioxide can be used to obtain value added products ranging from carbonating beverages, plastics, concrete additives, supplying to plants in greenhouses, or converting it into methane or methanol to paints, adhesives, olefins, syngas and chemicals.

New technologies are being discovered and researchers are working tirelessly to provide better solutions. In order to prevent further damage to the environment, every country should reach negative carbon emissions i.e. removing more carbon from air than putting in to the atmosphere. Combination of measures can be taken to tackle this problem faster i.e. increase natural carbon-removal approaches and investing in technological approaches through research and development. As per United Nations Emissions Report published in the year 2017, world needs to sequester and store 8 Gigatonnes of carbon dioxide annually on an average by the year 2050. Not to forget that it is also a responsibility of the major economies of the world, or so called developed countries, to transfer the needed carbon sequestration technology and methods to developing and poor countries to help them cut emissions and adapt to carbon capture and sequestration. All efforts ultimately help in mitigation of climate change and chemical engineers have major role to play in saving mother earth!

Chemical Engineering department of MGM University is also committed to address this problem through research on carbon capture. I have, in collaboration with research group from the National Environmental Engineering and Research Institute (NEERI-CSIR) Nagpur, worked on chemical looping combustion technology to provide carbon sequestration solutions mainly for thermal power plant’s exhaust gases. Although we have obtained promising results but further investigations are to be done.

Dr. Sadanand Guhe

Head, Chemical Engineering Department

Nothing makes us prouder than being able to talk about the success of our alumni. We at JNEC are proud of you, Mr. Raj Gore.
Congratulations from all of us here, Chancellor MGM University Shri. A.N. Kadam Sir, VC, Dr. Sudhir Gawhane Sir, Trustee MGM, Pratap Borade Sir, Registrar, Dr. Ashish Gadekar Sir, Principal Jnec Dr. Harirang Shinde Sir, Vice-Principal Dr. Vijaya Musande ma'am, Alumni Engagement Head, Dr. Parminder Kaur, all faculty members and students of JNEC family.
#jnecalumni #jnecsuccessstories #MGMUniversity

An article by Dr. Parminder Kaur
Head, Training & Placements,
JNEC, MGM University, Aurangabad

Engineering is an extensive subject and choosing the right branch will be the first step towards your career. Identifying your interest areas and what gets you excited is important. Everyone has a natural inclination towards a specific field in engineering and selecting that one will play an important role in deciding the future of your choice. There are different engineering branches that you can consider graduating in like B. Tech in Computer Science and Engineering (CSE), Information Technology (IT), Mechanical, Chemical, Civil, Electronics & Telecommunication (E&TC) and Electrical engineering.  AISEET 2^nd rank in Maharashtra (top 5 best institutes in Maharashtra) and 5^th in India (top 5 best institutes), Jawaharlal Nehru Engineering College (JNEC), MGM University, Aurangabad, Maharashtra offers all these courses along withB. Arch. The college is a constituent part of MGM University from academic year 2020-21. The college is affiliated to Dr. BabasahebAmbedkar Technological University (Dr. BATU), Lonere which confers the degree of Bachelor of technology (B.Tech.), Master of Technology (M.Tech.) in various disciplines and Bachelor of Architecture (B. Arch.), Master of Architecture (M. Arch.) for 2021 passing out batch. While shortlisting different engineering streams, you should do an elaborate research into the future scope of studies, prospective career opportunities. More emphasis can be put on choosing core branches, if you want to work in government or public sector.

After shortlisting your engineering branch(es) of interest, put them in order of priority as now you have to make a choice for the best engineering college. You can do some research and can give marks on different aspects like faculty qualification, lab and classroom infrastructure, campus amenities, campus placements, library, hostel, computer infrastructure, internet facility, location of college, previous year results and how well alumni is doing. The priority should be as per your branch of interest and reputation of the college. One of the best engineering colleges, JNEC, MGM University can be your top choice as it has a smart campus, qualified and experienced senior faculty members, and well-established infrastructure. Accredited with NAAC ‘A’ grade, JNEC has earned the reputation of a smart learning institute asInnovation Incubation Research Center (IIRC) has been established as part of Industry-Academia Initiative. To promote research and technical skills which are significant for students’ development, for the first time in Maharashtra, JNEC has come up with the unique state of the art infrastructure which will meet the demands of all types of industries starting from design to manufacturing and rapid prototyping to Industry 4.0. JNEC is equipped with modern infrastructural facilities: an auditorium of seating capacity of 1000 students, two seminar halls of seating capacity of 200 students, central computing lab with 200 computer systems at one location and overall 1200 computer systems in college campus and two conference rooms. MGM University (MGMU), Aurangabad is a smart campus university in Marathwada region.

You should visit the college and talk with faculty members, present students of the college and also with alumni. JNEC, MGM University is the best institute for women as it is ranked at 1^st position in India Today Best -Engineering College ranking 2019 for engineering colleges with best male to female ratio.Over the last four decades, more than 15,000 engineers and architects have graduated from the institute. It is gaining fame for the generous investments done in research of diverse areas of science and technology.

Visit the websites of the colleges shortlisted by you to get all these details. Highest numbers of job offer are made to JNEC students during pool campus recruitment drives in Marathwada region. TCS has accredited JNEC as a center for TCS campus recruitment process in Marathwada. MGM University,the best university in Aurangabad is the nodal center for big brands like Samsung, HSBC, Bosch, Amazon, Adani and other major campus recruitment drives. It has been coordinating campus placement drives for many multinational companies in JNEC premises. JNEC independently organizes and hosts a unique annual TEDx conference that provides an opportunity for students to listen and network with eminent achievers in diverse fields such as literature, arts, technology, health, social services and bureaucrats. Throughout the year MGM University Campus celebrates vibrant activities through which students are exposedto multifaceted opportunities that carves out a brighter and flourished façade of them. In parallel they build a sensitive approach towards body, mind and spirit. Students also participate in Indian Student Parliament, an event where they listen to eminent leaders.

“Engineering is the art of directing the great sources of power in nature for the use and convenience of man “- Thomas Tredgold